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Helsinki-NLP/opus-mt-yo-fr | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"seq2seq",
"yo",
"fr",
"transformers",
"translation",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 38 | transformers | ---
tags:
- translation
---
### opus-mt-yo-fr
* source languages: yo
* target languages: fr
* OPUS readme: [yo-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yo-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/yo-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.yo.fr | 24.1 | 0.408 |
|
Helsinki-NLP/opus-mt-yo-sv | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"seq2seq",
"yo",
"sv",
"transformers",
"translation",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 41 | transformers | ---
tags:
- translation
---
### opus-mt-yo-sv
* source languages: yo
* target languages: sv
* OPUS readme: [yo-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yo-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/yo-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.yo.sv | 25.2 | 0.434 |
|
Helsinki-NLP/opus-mt-zai-es | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zai",
"es",
"transformers",
"translation",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 38 | transformers | ---
tags:
- translation
---
### opus-mt-zai-es
* source languages: zai
* target languages: es
* OPUS readme: [zai-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zai-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zai-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zai-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zai-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.zai.es | 20.8 | 0.372 |
|
Helsinki-NLP/opus-mt-zh-bg | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"bg",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 33 | transformers | ---
language:
- zh
- bg
tags:
- translation
license: apache-2.0
---
### zho-bul
* source group: Chinese
* target group: Bulgarian
* OPUS readme: [zho-bul](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-bul/README.md)
* model: transformer
* source language(s): cmn cmn_Hans cmn_Hant zho zho_Hans zho_Hant
* target language(s): bul
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.zip)
* test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.test.txt)
* test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.cmn_Hani.bul | 29.6 | 0.497 |
| Tatoeba-test.zho.bul | 29.6 | 0.497 |
### System Info:
- hf_name: zho-bul
- source_languages: zho
- target_languages: bul
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-bul/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'bg']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'bul', 'bul_Latn'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.test.txt
- src_alpha3: zho
- tgt_alpha3: bul
- short_pair: zh-bg
- chrF2_score: 0.49700000000000005
- bleu: 29.6
- brevity_penalty: 0.883
- ref_len: 3113.0
- src_name: Chinese
- tgt_name: Bulgarian
- train_date: 2020-07-03
- src_alpha2: zh
- tgt_alpha2: bg
- prefer_old: False
- long_pair: zho-bul
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-de | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"de",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 808 | transformers | ---
language:
- zh
- de
tags:
- translation
license: apache-2.0
---
### zho-deu
* source group: Chinese
* target group: German
* OPUS readme: [zho-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-deu/README.md)
* model: transformer-align
* source language(s): cmn cmn_Bopo cmn_Hang cmn_Hani cmn_Hira cmn_Kana cmn_Latn lzh_Hani wuu_Hani yue_Hani
* target language(s): deu
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-deu/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-deu/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-deu/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.deu | 32.1 | 0.522 |
### System Info:
- hf_name: zho-deu
- source_languages: zho
- target_languages: deu
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-deu/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'de']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'deu'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-deu/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-deu/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: deu
- short_pair: zh-de
- chrF2_score: 0.522
- bleu: 32.1
- brevity_penalty: 0.9540000000000001
- ref_len: 19102.0
- src_name: Chinese
- tgt_name: German
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: de
- prefer_old: False
- long_pair: zho-deu
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-en | 2021-02-26T18:53:22.000Z | [
"pytorch",
"rust",
"marian",
"seq2seq",
"zh",
"en",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"rust_model.ot",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 7,775 | transformers | ---
language:
- zh
- en
tags:
- translation
license: apache-2.0
---
### zho-eng
* source group: Chinese
* target group: English
* OPUS readme: [zho-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md)
* model: transformer
* source language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip)
* test set translations: [opus-2020-07-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt)
* test set scores: [opus-2020-07-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.eng | 36.1 | 0.548 |
### System Info:
- hf_name: zho-eng
- source_languages: zho
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'en']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'eng'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt
- src_alpha3: zho
- tgt_alpha3: eng
- short_pair: zh-en
- chrF2_score: 0.5479999999999999
- bleu: 36.1
- brevity_penalty: 0.948
- ref_len: 82826.0
- src_name: Chinese
- tgt_name: English
- train_date: 2020-07-17
- src_alpha2: zh
- tgt_alpha2: en
- prefer_old: False
- long_pair: zho-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-fi | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 669 | transformers | ---
language:
- zh
- fi
tags:
- translation
license: apache-2.0
---
### zho-fin
* source group: Chinese
* target group: Finnish
* OPUS readme: [zho-fin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-fin/README.md)
* model: transformer-align
* source language(s): cmn_Bopo cmn_Hani cmn_Latn nan_Hani yue yue_Hani
* target language(s): fin
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.fin | 35.1 | 0.579 |
### System Info:
- hf_name: zho-fin
- source_languages: zho
- target_languages: fin
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-fin/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'fi']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'fin'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: fin
- short_pair: zh-fi
- chrF2_score: 0.579
- bleu: 35.1
- brevity_penalty: 0.935
- ref_len: 1847.0
- src_name: Chinese
- tgt_name: Finnish
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: fi
- prefer_old: False
- long_pair: zho-fin
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-he | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"he",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 28 | transformers | ---
language:
- zh
- he
tags:
- translation
license: apache-2.0
---
### zho-heb
* source group: Chinese
* target group: Hebrew
* OPUS readme: [zho-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-heb/README.md)
* model: transformer-align
* source language(s): cmn cmn_Bopo cmn_Hang cmn_Hani cmn_Hira cmn_Kana cmn_Latn cmn_Yiii lzh lzh_Bopo lzh_Hang lzh_Hani lzh_Hira lzh_Kana lzh_Yiii
* target language(s): heb
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-heb/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-heb/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-heb/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.heb | 28.5 | 0.469 |
### System Info:
- hf_name: zho-heb
- source_languages: zho
- target_languages: heb
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-heb/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'he']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'heb'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-heb/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-heb/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: heb
- short_pair: zh-he
- chrF2_score: 0.469
- bleu: 28.5
- brevity_penalty: 0.986
- ref_len: 3654.0
- src_name: Chinese
- tgt_name: Hebrew
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: he
- prefer_old: False
- long_pair: zho-heb
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-it | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"it",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 50 | transformers | ---
language:
- zh
- it
tags:
- translation
license: apache-2.0
---
### zho-ita
* source group: Chinese
* target group: Italian
* OPUS readme: [zho-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-ita/README.md)
* model: transformer-align
* source language(s): cmn cmn_Bopo cmn_Hang cmn_Hani cmn_Hira cmn_Kana cmn_Latn lzh lzh_Hang lzh_Hani lzh_Hira lzh_Yiii wuu_Bopo wuu_Hani wuu_Latn yue_Hani
* target language(s): ita
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ita/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ita/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ita/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.ita | 27.9 | 0.508 |
### System Info:
- hf_name: zho-ita
- source_languages: zho
- target_languages: ita
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-ita/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'it']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'ita'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ita/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ita/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: ita
- short_pair: zh-it
- chrF2_score: 0.508
- bleu: 27.9
- brevity_penalty: 0.935
- ref_len: 19684.0
- src_name: Chinese
- tgt_name: Italian
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: it
- prefer_old: False
- long_pair: zho-ita
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-ms | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"ms",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 34 | transformers | ---
language:
- zh
- ms
tags:
- translation
license: apache-2.0
---
### zho-msa
* source group: Chinese
* target group: Malay (macrolanguage)
* OPUS readme: [zho-msa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-msa/README.md)
* model: transformer-align
* source language(s): cmn_Bopo cmn_Hani cmn_Latn hak_Hani yue_Bopo yue_Hani
* target language(s): ind zsm_Latn
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-msa/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-msa/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-msa/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.msa | 13.9 | 0.390 |
### System Info:
- hf_name: zho-msa
- source_languages: zho
- target_languages: msa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-msa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'ms']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'zsm_Latn', 'ind', 'max_Latn', 'zlm_Latn', 'min'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-msa/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-msa/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: msa
- short_pair: zh-ms
- chrF2_score: 0.39
- bleu: 13.9
- brevity_penalty: 0.9229999999999999
- ref_len: 2762.0
- src_name: Chinese
- tgt_name: Malay (macrolanguage)
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: ms
- prefer_old: False
- long_pair: zho-msa
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-nl | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"nl",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 23 | transformers | ---
language:
- zh
- nl
tags:
- translation
license: apache-2.0
---
### zho-nld
* source group: Chinese
* target group: Dutch
* OPUS readme: [zho-nld](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-nld/README.md)
* model: transformer-align
* source language(s): cmn cmn_Bopo cmn_Hani cmn_Hira cmn_Kana cmn_Latn
* target language(s): nld
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.nld | 31.5 | 0.525 |
### System Info:
- hf_name: zho-nld
- source_languages: zho
- target_languages: nld
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-nld/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'nl']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'nld'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-nld/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: nld
- short_pair: zh-nl
- chrF2_score: 0.525
- bleu: 31.5
- brevity_penalty: 0.9309999999999999
- ref_len: 13575.0
- src_name: Chinese
- tgt_name: Dutch
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: nl
- prefer_old: False
- long_pair: zho-nld
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-sv | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 60 | transformers | ---
language:
- zh
- sv
tags:
- translation
license: apache-2.0
---
### zho-swe
* source group: Chinese
* target group: Swedish
* OPUS readme: [zho-swe](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-swe/README.md)
* model: transformer-align
* source language(s): cmn cmn_Bopo cmn_Hani cmn_Latn
* target language(s): swe
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.swe | 46.1 | 0.621 |
### System Info:
- hf_name: zho-swe
- source_languages: zho
- target_languages: swe
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-swe/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'sv']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'swe'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-swe/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: swe
- short_pair: zh-sv
- chrF2_score: 0.621
- bleu: 46.1
- brevity_penalty: 0.956
- ref_len: 6223.0
- src_name: Chinese
- tgt_name: Swedish
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: sv
- prefer_old: False
- long_pair: zho-swe
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-uk | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"uk",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 26 | transformers | ---
language:
- zh
- uk
tags:
- translation
license: apache-2.0
---
### zho-ukr
* source group: Chinese
* target group: Ukrainian
* OPUS readme: [zho-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-ukr/README.md)
* model: transformer-align
* source language(s): cmn cmn_Bopo cmn_Hang cmn_Hani cmn_Kana cmn_Latn cmn_Yiii yue_Bopo yue_Hang yue_Hani yue_Hira yue_Kana
* target language(s): ukr
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ukr/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ukr/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ukr/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.ukr | 10.4 | 0.259 |
### System Info:
- hf_name: zho-ukr
- source_languages: zho
- target_languages: ukr
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-ukr/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'uk']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'ukr'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ukr/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-ukr/opus-2020-06-16.test.txt
- src_alpha3: zho
- tgt_alpha3: ukr
- short_pair: zh-uk
- chrF2_score: 0.259
- bleu: 10.4
- brevity_penalty: 0.9059999999999999
- ref_len: 9193.0
- src_name: Chinese
- tgt_name: Ukrainian
- train_date: 2020-06-16
- src_alpha2: zh
- tgt_alpha2: uk
- prefer_old: False
- long_pair: zho-ukr
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-vi | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zh",
"vi",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 58 | transformers | ---
language:
- zh
- vi
tags:
- translation
license: apache-2.0
---
### zho-vie
* source group: Chinese
* target group: Vietnamese
* OPUS readme: [zho-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-vie/README.md)
* model: transformer-align
* source language(s): cmn_Hani cmn_Latn
* target language(s): vie
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-vie/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-vie/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-vie/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.vie | 20.0 | 0.385 |
### System Info:
- hf_name: zho-vie
- source_languages: zho
- target_languages: vie
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-vie/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'vi']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'vie', 'vie_Hani'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-vie/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-vie/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: vie
- short_pair: zh-vi
- chrF2_score: 0.385
- bleu: 20.0
- brevity_penalty: 0.917
- ref_len: 4667.0
- src_name: Chinese
- tgt_name: Vietnamese
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: vi
- prefer_old: False
- long_pair: zho-vie
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zle-en | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"be",
"ru",
"uk",
"zle",
"en",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 22 | transformers | ---
language:
- be
- ru
- uk
- zle
- en
tags:
- translation
license: apache-2.0
---
### zle-eng
* source group: East Slavic languages
* target group: English
* OPUS readme: [zle-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-eng/README.md)
* model: transformer
* source language(s): bel bel_Latn orv_Cyrl rue rus ukr
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newstest2012-ruseng.rus.eng | 31.1 | 0.579 |
| newstest2013-ruseng.rus.eng | 24.9 | 0.522 |
| newstest2014-ruen-ruseng.rus.eng | 27.9 | 0.563 |
| newstest2015-enru-ruseng.rus.eng | 26.8 | 0.541 |
| newstest2016-enru-ruseng.rus.eng | 25.8 | 0.535 |
| newstest2017-enru-ruseng.rus.eng | 29.1 | 0.561 |
| newstest2018-enru-ruseng.rus.eng | 25.4 | 0.537 |
| newstest2019-ruen-ruseng.rus.eng | 26.8 | 0.545 |
| Tatoeba-test.bel-eng.bel.eng | 38.3 | 0.569 |
| Tatoeba-test.multi.eng | 50.1 | 0.656 |
| Tatoeba-test.orv-eng.orv.eng | 6.9 | 0.217 |
| Tatoeba-test.rue-eng.rue.eng | 15.4 | 0.345 |
| Tatoeba-test.rus-eng.rus.eng | 52.5 | 0.674 |
| Tatoeba-test.ukr-eng.ukr.eng | 52.1 | 0.673 |
### System Info:
- hf_name: zle-eng
- source_languages: zle
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['be', 'ru', 'uk', 'zle', 'en']
- src_constituents: {'bel', 'orv_Cyrl', 'bel_Latn', 'rus', 'ukr', 'rue'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.test.txt
- src_alpha3: zle
- tgt_alpha3: eng
- short_pair: zle-en
- chrF2_score: 0.6559999999999999
- bleu: 50.1
- brevity_penalty: 0.97
- ref_len: 69599.0
- src_name: East Slavic languages
- tgt_name: English
- train_date: 2020-08-01
- src_alpha2: zle
- tgt_alpha2: en
- prefer_old: False
- long_pair: zle-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zle-zle | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"be",
"ru",
"uk",
"zle",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 33 | transformers | ---
language:
- be
- ru
- uk
- zle
tags:
- translation
license: apache-2.0
---
### zle-zle
* source group: East Slavic languages
* target group: East Slavic languages
* OPUS readme: [zle-zle](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zle/README.md)
* model: transformer
* source language(s): bel bel_Latn orv_Cyrl rus ukr
* target language(s): bel bel_Latn orv_Cyrl rus ukr
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opus-2020-07-27.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bel-rus.bel.rus | 57.1 | 0.758 |
| Tatoeba-test.bel-ukr.bel.ukr | 55.5 | 0.751 |
| Tatoeba-test.multi.multi | 58.0 | 0.742 |
| Tatoeba-test.orv-rus.orv.rus | 5.8 | 0.226 |
| Tatoeba-test.orv-ukr.orv.ukr | 2.5 | 0.161 |
| Tatoeba-test.rus-bel.rus.bel | 50.5 | 0.714 |
| Tatoeba-test.rus-orv.rus.orv | 0.3 | 0.129 |
| Tatoeba-test.rus-ukr.rus.ukr | 63.9 | 0.794 |
| Tatoeba-test.ukr-bel.ukr.bel | 51.3 | 0.719 |
| Tatoeba-test.ukr-orv.ukr.orv | 0.3 | 0.106 |
| Tatoeba-test.ukr-rus.ukr.rus | 68.7 | 0.825 |
### System Info:
- hf_name: zle-zle
- source_languages: zle
- target_languages: zle
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zle/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['be', 'ru', 'uk', 'zle']
- src_constituents: {'bel', 'orv_Cyrl', 'bel_Latn', 'rus', 'ukr', 'rue'}
- tgt_constituents: {'bel', 'orv_Cyrl', 'bel_Latn', 'rus', 'ukr', 'rue'}
- src_multilingual: True
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opus-2020-07-27.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opus-2020-07-27.test.txt
- src_alpha3: zle
- tgt_alpha3: zle
- short_pair: zle-zle
- chrF2_score: 0.742
- bleu: 58.0
- brevity_penalty: 1.0
- ref_len: 62731.0
- src_name: East Slavic languages
- tgt_name: East Slavic languages
- train_date: 2020-07-27
- src_alpha2: zle
- tgt_alpha2: zle
- prefer_old: False
- long_pair: zle-zle
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zls-en | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"hr",
"mk",
"bg",
"sl",
"zls",
"en",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 25 | transformers | ---
language:
- hr
- mk
- bg
- sl
- zls
- en
tags:
- translation
license: apache-2.0
---
### zls-eng
* source group: South Slavic languages
* target group: English
* OPUS readme: [zls-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-eng/README.md)
* model: transformer
* source language(s): bos_Latn bul bul_Latn hrv mkd slv srp_Cyrl srp_Latn
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul-eng.bul.eng | 54.9 | 0.693 |
| Tatoeba-test.hbs-eng.hbs.eng | 55.7 | 0.700 |
| Tatoeba-test.mkd-eng.mkd.eng | 54.6 | 0.681 |
| Tatoeba-test.multi.eng | 53.6 | 0.676 |
| Tatoeba-test.slv-eng.slv.eng | 25.6 | 0.407 |
### System Info:
- hf_name: zls-eng
- source_languages: zls
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['hr', 'mk', 'bg', 'sl', 'zls', 'en']
- src_constituents: {'hrv', 'mkd', 'srp_Latn', 'srp_Cyrl', 'bul_Latn', 'bul', 'bos_Latn', 'slv'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opus2m-2020-08-01.test.txt
- src_alpha3: zls
- tgt_alpha3: eng
- short_pair: zls-en
- chrF2_score: 0.6759999999999999
- bleu: 53.6
- brevity_penalty: 0.98
- ref_len: 68623.0
- src_name: South Slavic languages
- tgt_name: English
- train_date: 2020-08-01
- src_alpha2: zls
- tgt_alpha2: en
- prefer_old: False
- long_pair: zls-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zls-zls | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"hr",
"mk",
"bg",
"sl",
"zls",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 46 | transformers | ---
language:
- hr
- mk
- bg
- sl
- zls
tags:
- translation
license: apache-2.0
---
### zls-zls
* source group: South Slavic languages
* target group: South Slavic languages
* OPUS readme: [zls-zls](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-zls/README.md)
* model: transformer
* source language(s): bul mkd srp_Cyrl
* target language(s): bul mkd srp_Cyrl
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zls/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zls/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zls/opus-2020-07-27.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul-hbs.bul.hbs | 19.3 | 0.514 |
| Tatoeba-test.bul-mkd.bul.mkd | 31.9 | 0.669 |
| Tatoeba-test.hbs-bul.hbs.bul | 18.0 | 0.636 |
| Tatoeba-test.hbs-mkd.hbs.mkd | 19.4 | 0.322 |
| Tatoeba-test.mkd-bul.mkd.bul | 44.6 | 0.679 |
| Tatoeba-test.mkd-hbs.mkd.hbs | 5.5 | 0.152 |
| Tatoeba-test.multi.multi | 26.5 | 0.563 |
### System Info:
- hf_name: zls-zls
- source_languages: zls
- target_languages: zls
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-zls/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['hr', 'mk', 'bg', 'sl', 'zls']
- src_constituents: {'hrv', 'mkd', 'srp_Latn', 'srp_Cyrl', 'bul_Latn', 'bul', 'bos_Latn', 'slv'}
- tgt_constituents: {'hrv', 'mkd', 'srp_Latn', 'srp_Cyrl', 'bul_Latn', 'bul', 'bos_Latn', 'slv'}
- src_multilingual: True
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zls/opus-2020-07-27.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zls/opus-2020-07-27.test.txt
- src_alpha3: zls
- tgt_alpha3: zls
- short_pair: zls-zls
- chrF2_score: 0.563
- bleu: 26.5
- brevity_penalty: 1.0
- ref_len: 58.0
- src_name: South Slavic languages
- tgt_name: South Slavic languages
- train_date: 2020-07-27
- src_alpha2: zls
- tgt_alpha2: zls
- prefer_old: False
- long_pair: zls-zls
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zlw-en | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"pl",
"cs",
"zlw",
"en",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 20 | transformers | ---
language:
- pl
- cs
- zlw
- en
tags:
- translation
license: apache-2.0
---
### zlw-eng
* source group: West Slavic languages
* target group: English
* OPUS readme: [zlw-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zlw-eng/README.md)
* model: transformer
* source language(s): ces csb_Latn dsb hsb pol
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-eng/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-eng/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-eng/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newssyscomb2009-ceseng.ces.eng | 25.7 | 0.536 |
| newstest2009-ceseng.ces.eng | 24.6 | 0.530 |
| newstest2010-ceseng.ces.eng | 25.0 | 0.540 |
| newstest2011-ceseng.ces.eng | 25.9 | 0.539 |
| newstest2012-ceseng.ces.eng | 24.8 | 0.533 |
| newstest2013-ceseng.ces.eng | 27.8 | 0.551 |
| newstest2014-csen-ceseng.ces.eng | 30.3 | 0.585 |
| newstest2015-encs-ceseng.ces.eng | 27.5 | 0.542 |
| newstest2016-encs-ceseng.ces.eng | 29.1 | 0.564 |
| newstest2017-encs-ceseng.ces.eng | 26.0 | 0.537 |
| newstest2018-encs-ceseng.ces.eng | 27.3 | 0.544 |
| Tatoeba-test.ces-eng.ces.eng | 53.3 | 0.691 |
| Tatoeba-test.csb-eng.csb.eng | 10.2 | 0.313 |
| Tatoeba-test.dsb-eng.dsb.eng | 11.7 | 0.296 |
| Tatoeba-test.hsb-eng.hsb.eng | 24.6 | 0.426 |
| Tatoeba-test.multi.eng | 51.8 | 0.680 |
| Tatoeba-test.pol-eng.pol.eng | 50.4 | 0.667 |
### System Info:
- hf_name: zlw-eng
- source_languages: zlw
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zlw-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['pl', 'cs', 'zlw', 'en']
- src_constituents: {'csb_Latn', 'dsb', 'hsb', 'pol', 'ces'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-eng/opus2m-2020-08-01.test.txt
- src_alpha3: zlw
- tgt_alpha3: eng
- short_pair: zlw-en
- chrF2_score: 0.68
- bleu: 51.8
- brevity_penalty: 0.9620000000000001
- ref_len: 75742.0
- src_name: West Slavic languages
- tgt_name: English
- train_date: 2020-08-01
- src_alpha2: zlw
- tgt_alpha2: en
- prefer_old: False
- long_pair: zlw-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zlw-zlw | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"pl",
"cs",
"zlw",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 45 | transformers | ---
language:
- pl
- cs
- zlw
tags:
- translation
license: apache-2.0
---
### zlw-zlw
* source group: West Slavic languages
* target group: West Slavic languages
* OPUS readme: [zlw-zlw](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zlw-zlw/README.md)
* model: transformer
* source language(s): ces dsb hsb pol
* target language(s): ces dsb hsb pol
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zlw/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zlw/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zlw/opus-2020-07-27.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ces-hsb.ces.hsb | 2.6 | 0.167 |
| Tatoeba-test.ces-pol.ces.pol | 44.0 | 0.649 |
| Tatoeba-test.dsb-pol.dsb.pol | 8.5 | 0.250 |
| Tatoeba-test.hsb-ces.hsb.ces | 9.6 | 0.276 |
| Tatoeba-test.multi.multi | 38.8 | 0.580 |
| Tatoeba-test.pol-ces.pol.ces | 43.4 | 0.620 |
| Tatoeba-test.pol-dsb.pol.dsb | 2.1 | 0.159 |
### System Info:
- hf_name: zlw-zlw
- source_languages: zlw
- target_languages: zlw
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zlw-zlw/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['pl', 'cs', 'zlw']
- src_constituents: {'csb_Latn', 'dsb', 'hsb', 'pol', 'ces'}
- tgt_constituents: {'csb_Latn', 'dsb', 'hsb', 'pol', 'ces'}
- src_multilingual: True
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zlw/opus-2020-07-27.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zlw/opus-2020-07-27.test.txt
- src_alpha3: zlw
- tgt_alpha3: zlw
- short_pair: zlw-zlw
- chrF2_score: 0.58
- bleu: 38.8
- brevity_penalty: 0.99
- ref_len: 7792.0
- src_name: West Slavic languages
- tgt_name: West Slavic languages
- train_date: 2020-07-27
- src_alpha2: zlw
- tgt_alpha2: zlw
- prefer_old: False
- long_pair: zlw-zlw
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zne-es | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zne",
"es",
"transformers",
"translation",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 39 | transformers | ---
tags:
- translation
---
### opus-mt-zne-es
* source languages: zne
* target languages: es
* OPUS readme: [zne-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.zne.es | 21.1 | 0.382 |
|
Helsinki-NLP/opus-mt-zne-fi | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zne",
"fi",
"transformers",
"translation",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 35 | transformers | ---
tags:
- translation
---
### opus-mt-zne-fi
* source languages: zne
* target languages: fi
* OPUS readme: [zne-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zne-fi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-fi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-fi/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.zne.fi | 22.8 | 0.432 |
|
Helsinki-NLP/opus-mt-zne-fr | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zne",
"fr",
"transformers",
"translation",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 38 | transformers | ---
tags:
- translation
---
### opus-mt-zne-fr
* source languages: zne
* target languages: fr
* OPUS readme: [zne-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zne-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.zne.fr | 25.3 | 0.416 |
|
Helsinki-NLP/opus-mt-zne-sv | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"seq2seq",
"zne",
"sv",
"transformers",
"translation",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"source.spm",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 45 | transformers | ---
tags:
- translation
---
### opus-mt-zne-sv
* source languages: zne
* target languages: sv
* OPUS readme: [zne-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/zne-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.zne.sv | 25.2 | 0.425 |
|
Helsinki-NLP/opus-tatoeba-af-ru | 2021-02-12T13:01:01.000Z | [
"pytorch",
"marian",
"seq2seq",
"af",
"ru",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"special_tokens_map.json",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 9 | transformers | ---
language:
- af
- ru
tags:
- translation
license: apache-2.0
---
### af-ru
* source group: Afrikaans
* target group: Russian
* OPUS readme: [afr-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-rus/README.md)
* model: transformer-align
* source language(s): afr
* target language(s): rus
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-09-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.zip)
* test set translations: [opus-2020-09-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.test.txt)
* test set scores: [opus-2020-09-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.afr.rus | 38.2 | 0.580 |
### System Info:
- hf_name: af-ru
- source_languages: afr
- target_languages: rus
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-rus/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['af', 'ru']
- src_constituents: ('Afrikaans', {'afr'})
- tgt_constituents: ('Russian', {'rus'})
- src_multilingual: False
- tgt_multilingual: False
- long_pair: afr-rus
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.test.txt
- src_alpha3: afr
- tgt_alpha3: rus
- chrF2_score: 0.58
- bleu: 38.2
- brevity_penalty: 0.992
- ref_len: 1213
- src_name: Afrikaans
- tgt_name: Russian
- train_date: 2020-01-01 00:00:00
- src_alpha2: af
- tgt_alpha2: ru
- prefer_old: False
- short_pair: af-ru
- helsinki_git_sha: e8c308a96c1bd0b4ca6a8ce174783f93c3e30f25
- transformers_git_sha: 31245775e5772fbded1ac07ed89fbba3b5af0cb9
- port_machine: LM0-400-22516.local
- port_time: 2021-02-12-14:52 |
Helsinki-NLP/opus-tatoeba-es-zh | 2021-01-04T16:53:57.000Z | [
"pytorch",
"marian",
"seq2seq",
"es",
"zh",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"special_tokens_map.json",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 68 | transformers | ---
language:
- es
- zh
tags:
- translation
license: apache-2.0
---
### es-zh
* source group: Spanish
* target group: Chinese
* OPUS readme: [spa-zho](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-zho/README.md)
* model: transformer
* source language(s): spa
* target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant hsn hsn_Hani lzh nan wuu yue_Hans yue_Hant
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2021-01-04.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zho/opus-2021-01-04.zip)
* test set translations: [opus-2021-01-04.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zho/opus-2021-01-04.test.txt)
* test set scores: [opus-2021-01-04.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zho/opus-2021-01-04.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.spa.zho | 38.8 | 0.324 |
### System Info:
- hf_name: es-zh
- source_languages: spa
- target_languages: zho
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-zho/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['es', 'zh']
- src_constituents: ('Spanish', {'spa'})
- tgt_constituents: ('Chinese', {'wuu_Bopo', 'wuu', 'cmn_Hang', 'lzh_Kana', 'lzh', 'wuu_Hani', 'lzh_Yiii', 'yue_Hans', 'cmn_Hani', 'cjy_Hans', 'cmn_Hans', 'cmn_Kana', 'zho_Hans', 'zho_Hant', 'yue', 'cmn_Bopo', 'yue_Hang', 'lzh_Hans', 'wuu_Latn', 'yue_Hant', 'hak_Hani', 'lzh_Bopo', 'cmn_Hant', 'lzh_Hani', 'lzh_Hang', 'cmn', 'lzh_Hira', 'yue_Bopo', 'yue_Hani', 'gan', 'zho', 'cmn_Yiii', 'yue_Hira', 'cmn_Latn', 'yue_Kana', 'cjy_Hant', 'cmn_Hira', 'nan_Hani', 'nan'})
- src_multilingual: False
- tgt_multilingual: False
- long_pair: spa-zho
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zho/opus-2021-01-04.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zho/opus-2021-01-04.test.txt
- src_alpha3: spa
- tgt_alpha3: zho
- chrF2_score: 0.324
- bleu: 38.8
- brevity_penalty: 0.878
- ref_len: 22762.0
- src_name: Spanish
- tgt_name: Chinese
- train_date: 2021-01-04 00:00:00
- src_alpha2: es
- tgt_alpha2: zh
- prefer_old: False
- short_pair: es-zh
- helsinki_git_sha: dfdcef114ffb8a8dbb7a3fcf84bde5af50309500
- transformers_git_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de
- port_machine: LM0-400-22516.local
- port_time: 2021-01-04-18:53 |
Helsinki-NLP/opus-tatoeba-he-fr | 2020-12-11T14:18:12.000Z | [
"pytorch",
"marian",
"seq2seq",
"he",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"special_tokens_map.json",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 15 | transformers | ---
language:
- he
- fr
tags:
- translation
license: apache-2.0
---
### he-fr
* source group: Hebrew
* target group: French
* OPUS readme: [heb-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-fra/README.md)
* model: transformer
* source language(s): heb
* target language(s): fra
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-12-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-fra/opus-2020-12-10.zip)
* test set translations: [opus-2020-12-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-fra/opus-2020-12-10.test.txt)
* test set scores: [opus-2020-12-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-fra/opus-2020-12-10.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.heb.fra | 47.3 | 0.644 |
### System Info:
- hf_name: he-fr
- source_languages: heb
- target_languages: fra
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-fra/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['he', 'fr']
- src_constituents: ('Hebrew', {'heb'})
- tgt_constituents: ('French', {'fra'})
- src_multilingual: False
- tgt_multilingual: False
- long_pair: heb-fra
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/heb-fra/opus-2020-12-10.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/heb-fra/opus-2020-12-10.test.txt
- src_alpha3: heb
- tgt_alpha3: fra
- chrF2_score: 0.644
- bleu: 47.3
- brevity_penalty: 0.9740000000000001
- ref_len: 26123.0
- src_name: Hebrew
- tgt_name: French
- train_date: 2020-12-10 00:00:00
- src_alpha2: he
- tgt_alpha2: fr
- prefer_old: False
- short_pair: he-fr
- helsinki_git_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96
- transformers_git_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de
- port_machine: LM0-400-22516.local
- port_time: 2020-12-11-16:03 |
Helsinki-NLP/opus-tatoeba-he-it | 2020-12-11T14:20:59.000Z | [
"pytorch",
"marian",
"seq2seq",
"he",
"it",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"special_tokens_map.json",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 13 | transformers | ---
language:
- he
- it
tags:
- translation
license: apache-2.0
---
### he-it
* source group: Hebrew
* target group: Italian
* OPUS readme: [heb-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-ita/README.md)
* model: transformer
* source language(s): heb
* target language(s): ita
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-12-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.zip)
* test set translations: [opus-2020-12-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.test.txt)
* test set scores: [opus-2020-12-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.heb.ita | 41.1 | 0.643 |
### System Info:
- hf_name: he-it
- source_languages: heb
- target_languages: ita
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-ita/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['he', 'it']
- src_constituents: ('Hebrew', {'heb'})
- tgt_constituents: ('Italian', {'ita'})
- src_multilingual: False
- tgt_multilingual: False
- long_pair: heb-ita
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/heb-ita/opus-2020-12-10.test.txt
- src_alpha3: heb
- tgt_alpha3: ita
- chrF2_score: 0.643
- bleu: 41.1
- brevity_penalty: 0.997
- ref_len: 11464.0
- src_name: Hebrew
- tgt_name: Italian
- train_date: 2020-12-10 00:00:00
- src_alpha2: he
- tgt_alpha2: it
- prefer_old: False
- short_pair: he-it
- helsinki_git_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96
- transformers_git_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de
- port_machine: LM0-400-22516.local
- port_time: 2020-12-11-16:01 |
Helsinki-NLP/opus-tatoeba-it-he | 2020-12-11T14:25:24.000Z | [
"pytorch",
"marian",
"seq2seq",
"it",
"he",
"transformers",
"translation",
"license:apache-2.0",
"text2text-generation"
]
| translation | [
".gitattributes",
"README.md",
"config.json",
"metadata.json",
"pytorch_model.bin",
"source.spm",
"special_tokens_map.json",
"target.spm",
"tokenizer_config.json",
"vocab.json"
]
| Helsinki-NLP | 12 | transformers | ---
language:
- it
- he
tags:
- translation
license: apache-2.0
---
### it-he
* source group: Italian
* target group: Hebrew
* OPUS readme: [ita-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-heb/README.md)
* model: transformer
* source language(s): ita
* target language(s): heb
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-12-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.zip)
* test set translations: [opus-2020-12-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.test.txt)
* test set scores: [opus-2020-12-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ita.heb | 38.5 | 0.593 |
### System Info:
- hf_name: it-he
- source_languages: ita
- target_languages: heb
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-heb/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['it', 'he']
- src_constituents: ('Italian', {'ita'})
- tgt_constituents: ('Hebrew', {'heb'})
- src_multilingual: False
- tgt_multilingual: False
- long_pair: ita-heb
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.test.txt
- src_alpha3: ita
- tgt_alpha3: heb
- chrF2_score: 0.593
- bleu: 38.5
- brevity_penalty: 0.985
- ref_len: 9796.0
- src_name: Italian
- tgt_name: Hebrew
- train_date: 2020-12-10 00:00:00
- src_alpha2: it
- tgt_alpha2: he
- prefer_old: False
- short_pair: it-he
- helsinki_git_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96
- transformers_git_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de
- port_machine: LM0-400-22516.local
- port_time: 2020-12-11-16:02 |
Hidde/iFlow | 2021-01-27T11:01:58.000Z | []
| [
".gitattributes"
]
| Hidde | 0 | |||
Hokuto/testrinna | 2021-04-12T05:20:16.000Z | []
| [
".gitattributes"
]
| Hokuto | 0 | |||
Homerzz/test | 2021-04-30T14:57:11.000Z | []
| [
".gitattributes"
]
| Homerzz | 0 | |||
HooshvareLab/albert-fa-zwnj-base-v2-ner | 2021-03-21T14:25:09.000Z | [
"pytorch",
"tf",
"albert",
"token-classification",
"fa",
"transformers"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
]
| HooshvareLab | 21 | transformers | ---
language: fa
---
# AlbertNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten types of entities:
- Date (DAT)
- Event (EVE)
- Facility (FAC)
- Location (LOC)
- Money (MON)
- Organization (ORG)
- Percent (PCT)
- Person (PER)
- Product (PRO)
- Time (TIM)
## Dataset Information
| | Records | B-DAT | B-EVE | B-FAC | B-LOC | B-MON | B-ORG | B-PCT | B-PER | B-PRO | B-TIM | I-DAT | I-EVE | I-FAC | I-LOC | I-MON | I-ORG | I-PCT | I-PER | I-PRO | I-TIM |
|:------|----------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Train | 29133 | 1423 | 1487 | 1400 | 13919 | 417 | 15926 | 355 | 12347 | 1855 | 150 | 1947 | 5018 | 2421 | 4118 | 1059 | 19579 | 573 | 7699 | 1914 | 332 |
| Valid | 5142 | 267 | 253 | 250 | 2362 | 100 | 2651 | 64 | 2173 | 317 | 19 | 373 | 799 | 387 | 717 | 270 | 3260 | 101 | 1382 | 303 | 35 |
| Test | 6049 | 407 | 256 | 248 | 2886 | 98 | 3216 | 94 | 2646 | 318 | 43 | 568 | 888 | 408 | 858 | 263 | 3967 | 141 | 1707 | 296 | 78 |
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
**Overall**
| Model | accuracy | precision | recall | f1 |
|:----------:|:--------:|:---------:|:--------:|:--------:|
| Albert | 0.993405 | 0.938907 | 0.943966 | 0.941429 |
**Per entities**
| | number | precision | recall | f1 |
|:---: |:------: |:---------: |:--------: |:--------: |
| DAT | 407 | 0.820639 | 0.820639 | 0.820639 |
| EVE | 256 | 0.936803 | 0.984375 | 0.960000 |
| FAC | 248 | 0.925373 | 1.000000 | 0.961240 |
| LOC | 2884 | 0.960818 | 0.960818 | 0.960818 |
| MON | 98 | 0.913978 | 0.867347 | 0.890052 |
| ORG | 3216 | 0.920892 | 0.937500 | 0.929122 |
| PCT | 94 | 0.946809 | 0.946809 | 0.946809 |
| PER | 2644 | 0.960000 | 0.944024 | 0.951945 |
| PRO | 318 | 0.942943 | 0.987421 | 0.964670 |
| TIM | 43 | 0.780488 | 0.744186 | 0.761905 |
## How To Use
You use this model with Transformers pipeline for NER.
### Installing requirements
```bash
pip install sentencepiece
pip install transformers
```
### How to predict using pipeline
```python
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification # for pytorch
from transformers import TFAutoModelForTokenClassification # for tensorflow
from transformers import pipeline
model_name_or_path = "HooshvareLab/albert-fa-zwnj-base-v2-ner" # Albert
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch
# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند."
ner_results = nlp(example)
print(ner_results)
```
## Questions?
Post a Github issue on the [ParsNER Issues](https://github.com/hooshvare/parsner/issues) repo. |
HooshvareLab/albert-fa-zwnj-base-v2 | 2021-03-16T16:36:38.000Z | [
"pytorch",
"tf",
"albert",
"masked-lm",
"fa",
"transformers",
"license:apache-2.0",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"spiece.vocab",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"unigram.json"
]
| HooshvareLab | 206 | transformers | ---
language: fa
license: apache-2.0
---
# ALBERT-Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
> Call it little_berty
### BibTeX entry and citation info
Please cite in your publication as the following:
```bibtex
@misc{ALBERTPersian,
author = {Hooshvare Team},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo. |
HooshvareLab/bert-base-parsbert-armanner-uncased | 2021-05-18T20:42:28.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"arxiv:2005.12515",
"transformers",
"license:apache-2.0"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"eval_results.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 4,262 | transformers | ---
language: fa
license: apache-2.0
---
## ParsBERT: Transformer-based Model for Persian Language Understanding
ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]
This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.
### ARMAN
ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.
1. Organization
2. Location
3. Facility
4. Event
5. Product
6. Person
| Label | # |
|:------------:|:-----:|
| Organization | 30108 |
| Location | 12924 |
| Facility | 4458 |
| Event | 7557 |
| Product | 4389 |
| Person | 15645 |
**Download**
You can download the dataset from [here](https://github.com/HaniehP/PersianNER)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|---------|----------|------------|--------------|----------|----------------|------------|
| ARMAN | 93.10* | 89.9 | 84.03 | 86.55 | - | 77.45 |
## How to use :hugs:
| Notebook | Description | |
|:----------|:-------------|------:|
| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
## Cite
Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
```markdown
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Acknowledgments
We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
## Contributors
- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
- Mohammad Gharachorloo: [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
- Marzieh Farahani: [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
- Mohammad Manthouri: [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
- Hooshvare Team: [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
+ And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: [Linkedin](https://www.linkedin.com/in/sara-tabrizi-64548b79/), [Behance](https://www.behance.net/saratabrizi), [Instagram](https://www.instagram.com/sara_b_tabrizi/)
## Releases
### Release v0.1 (May 29, 2019)
This is the first version of our ParsBERT NER!
|
HooshvareLab/bert-base-parsbert-ner-uncased | 2021-05-18T20:43:54.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"arxiv:2005.12515",
"transformers",
"license:apache-2.0"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"eval_results.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 62,805 | transformers | ---
language: fa
license: apache-2.0
---
## ParsBERT: Transformer-based Model for Persian Language Understanding
ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]
This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.
### PEYMA
PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent
| Label | # |
|:------------:|:-----:|
| Organization | 16964 |
| Money | 2037 |
| Location | 8782 |
| Date | 4259 |
| Time | 732 |
| Person | 7675 |
| Percent | 699 |
**Download**
You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
---
### ARMAN
ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.
1. Organization
2. Location
3. Facility
4. Event
5. Product
6. Person
| Label | # |
|:------------:|:-----:|
| Organization | 30108 |
| Location | 12924 |
| Facility | 4458 |
| Event | 7557 |
| Product | 4389 |
| Person | 15645 |
**Download**
You can download the dataset from [here](https://github.com/HaniehP/PersianNER)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|:---------------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
| ARMAN + PEYMA | 95.13* | - | - | - | - | - |
| PEYMA | 98.79* | - | 90.59 | - | 84.00 | - |
| ARMAN | 93.10* | 89.9 | 84.03 | 86.55 | - | 77.45 |
## How to use :hugs:
| Notebook | Description | |
|:----------|:-------------|------:|
| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
## Cite
Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
```markdown
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Acknowledgments
We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
## Contributors
- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
- Mohammad Gharachorloo: [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
- Marzieh Farahani: [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
- Mohammad Manthouri: [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
- Hooshvare Team: [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
+ And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: [Linkedin](https://www.linkedin.com/in/sara-tabrizi-64548b79/), [Behance](https://www.behance.net/saratabrizi), [Instagram](https://www.instagram.com/sara_b_tabrizi/)
## Releases
### Release v0.1 (May 29, 2019)
This is the first version of our ParsBERT NER!
|
HooshvareLab/bert-base-parsbert-peymaner-uncased | 2021-05-18T20:45:45.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"arxiv:2005.12515",
"transformers",
"license:apache-2.0"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"eval_results.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 6,576 | transformers | ---
language: fa
license: apache-2.0
---
## ParsBERT: Transformer-based Model for Persian Language Understanding
ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]
This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.
### PEYMA
PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent
| Label | # |
|:------------:|:-----:|
| Organization | 16964 |
| Money | 2037 |
| Location | 8782 |
| Date | 4259 |
| Time | 732 |
| Person | 7675 |
| Percent | 699 |
**Download**
You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|---------|----------|------------|--------------|----------|----------------|------------|
| PEYMA | 98.79* | - | 90.59 | - | 84.00 | - |
## How to use :hugs:
| Notebook | Description | |
|:----------|:-------------|------:|
| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
## Cite
Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
```markdown
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Acknowledgments
We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
## Contributors
- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
- Mohammad Gharachorloo: [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
- Marzieh Farahani: [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
- Mohammad Manthouri: [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
- Hooshvare Team: [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
+ And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: [Linkedin](https://www.linkedin.com/in/sara-tabrizi-64548b79/), [Behance](https://www.behance.net/saratabrizi), [Instagram](https://www.instagram.com/sara_b_tabrizi/)
## Releases
### Release v0.1 (May 29, 2019)
This is the first version of our ParsBERT NER!
|
HooshvareLab/bert-base-parsbert-uncased | 2021-05-18T20:47:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"masked-lm",
"arxiv:2005.12515",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"tf_model.h5",
"vocab.txt"
]
| HooshvareLab | 2,060 | transformers | ## ParsBERT: Transformer-based Model for Persian Language Understanding
ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
---
## Introduction
This model is pre-trained on a large Persian corpus with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 2M documents. A large subset of this corpus was crawled manually.
As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpus into a proper format. This process produces more than 40M true sentences.
## Evaluation
ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
### Sentiment Analysis (SA) task
| Dataset | ParsBERT | mBERT | DeepSentiPers |
|:--------------------------:|:---------:|:-----:|:-------------:|
| Digikala User Comments | 81.74* | 80.74 | - |
| SnappFood User Comments | 88.12* | 87.87 | - |
| SentiPers (Multi Class) | 71.11* | - | 69.33 |
| SentiPers (Binary Class) | 92.13* | - | 91.98 |
### Text Classification (TC) task
| Dataset | ParsBERT | mBERT |
|:-----------------:|:--------:|:-----:|
| Digikala Magazine | 93.59* | 90.72 |
| Persian News | 97.19* | 95.79 |
### Named Entity Recognition (NER) task
| Dataset | ParsBERT | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
| PEYMA | 93.10* | 86.64 | - | 90.59 | - | 84.00 | - |
| ARMAN | 98.79* | 95.89 | 89.9 | 84.03 | 86.55 | - | 77.45 |
**If you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference**
## How to use
### TensorFlow 2.0
```python
from transformers import AutoConfig, AutoTokenizer, TFAutoModel
config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد میتوانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است."
tokenizer.tokenize(text)
>>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.']
```
### Pytorch
```python
from transformers import AutoConfig, AutoTokenizer, AutoModel
config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
```
## NLP Tasks Tutorial
Coming soon stay tuned
## Cite
Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
```markdown
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Acknowledgments
We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
## Contributors
- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
- Mohammad Gharachorloo: [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
- Marzieh Farahani: [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
- Mohammad Manthouri: [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
- Hooshvare Team: [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
## Releases
### Release v0.1 (May 27, 2019)
This is the first version of our ParsBERT based on BERT<sub>BASE</sub>
|
HooshvareLab/bert-fa-base-uncased-clf-digimag | 2021-05-18T20:48:44.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 51 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian Text Classification [DigiMag, Persian News]
The task target is labeling texts in a supervised manner in both existing datasets `DigiMag` and `Persian News`.
### DigiMag
A total of 8,515 articles scraped from [Digikala Online Magazine](https://www.digikala.com/mag/). This dataset includes seven different classes.
1. Video Games
2. Shopping Guide
3. Health Beauty
4. Science Technology
5. General
6. Art Cinema
7. Books Literature
| Label | # |
|:------------------:|:----:|
| Video Games | 1967 |
| Shopping Guide | 125 |
| Health Beauty | 1610 |
| Science Technology | 2772 |
| General | 120 |
| Art Cinema | 1667 |
| Books Literature | 254 |
**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=1YgrCYY-Z0h2z0-PfWVfOGt1Tv0JDI-qz)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT |
|:-----------------:|:-----------:|:-----------:|:-----:|
| Digikala Magazine | 93.65* | 93.59 | 90.72 |
## How to use :hugs:
| Task | Notebook |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Text Classification | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/bert-fa-base-uncased-clf-persiannews | 2021-05-18T20:51:07.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 6,323 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian Text Classification [DigiMag, Persian News]
The task target is labeling texts in a supervised manner in both existing datasets `DigiMag` and `Persian News`.
### Persian News
A dataset of various news articles scraped from different online news agencies' websites. The total number of articles is 16,438, spread over eight different classes.
1. Economic
2. International
3. Political
4. Science Technology
5. Cultural Art
6. Sport
7. Medical
| Label | # |
|:------------------:|:----:|
| Social | 2170 |
| Economic | 1564 |
| International | 1975 |
| Political | 2269 |
| Science Technology | 2436 |
| Cultural Art | 2558 |
| Sport | 1381 |
| Medical | 2085 |
**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=1B6xotfXCcW9xS1mYSBQos7OCg0ratzKC)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT |
|:-----------------:|:-----------:|:-----------:|:-----:|
| Persian News | 97.44* | 97.19 | 95.79 |
## How to use :hugs:
| Task | Notebook |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Text Classification | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/bert-fa-base-uncased-ner-arman | 2021-05-18T20:52:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 120 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian NER [ARMAN, PEYMA]
This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`.
### ARMAN
ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.
1. Organization
2. Location
3. Facility
4. Event
5. Product
6. Person
| Label | # |
|:------------:|:-----:|
| Organization | 30108 |
| Location | 12924 |
| Facility | 4458 |
| Event | 7557 |
| Product | 4389 |
| Person | 15645 |
**Download**
You can download the dataset from [here](https://github.com/HaniehP/PersianNER)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|---------|-------------|-------------|-------|------------|--------------|----------|----------------|------------|
| ARMAN | 99.84* | 98.79 | 95.89 | 89.9 | 84.03 | 86.55 | - | 77.45 |
## How to use :hugs:
| Notebook | Description | |
|:----------|:-------------|------:|
| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/bert-fa-base-uncased-ner-peyma | 2021-05-18T20:55:10.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 50 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian NER [ARMAN, PEYMA]
This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`.
### PEYMA
PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent
| Label | # |
|:------------:|:-----:|
| Organization | 16964 |
| Money | 2037 |
| Location | 8782 |
| Date | 4259 |
| Time | 732 |
| Person | 7675 |
| Percent | 699 |
**Download**
You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|---------|-------------|-------------|-------|------------|--------------|----------|----------------|------------|
| PEYMA | 93.40* | 93.10 | 86.64 | - | 90.59 | - | 84.00 | - |
## How to use :hugs:
| Notebook | Description | |
|:----------|:-------------|------:|
| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary | 2021-05-18T20:56:29.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 223 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
### DeepSentiPers
which is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.
**Binary:**
1. Negative (Furious + Angry)
2. Positive (Happy + Delighted)
**Multi**
1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted
| Label | # |
|:---------:|:----:|
| Furious | 236 |
| Angry | 1357 |
| Neutral | 2874 |
| Happy | 2848 |
| Delighted | 2516 |
**Download**
You can download the dataset from:
- [SentiPers](https://github.com/phosseini/sentipers)
- [DeepSentiPers](https://github.com/JoyeBright/DeepSentiPers)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
| SentiPers (Multi Class) | 71.31* | 71.11 | - | 69.33 |
| SentiPers (Binary Class) | 92.42* | 92.13 | - | 91.98 |
## How to use :hugs:
| Task | Notebook |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Sentiment Analysis | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi | 2021-05-18T20:58:01.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 49 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
### DeepSentiPers
which is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.
**Binary:**
1. Negative (Furious + Angry)
2. Positive (Happy + Delighted)
**Multi**
1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted
| Label | # |
|:---------:|:----:|
| Furious | 236 |
| Angry | 1357 |
| Neutral | 2874 |
| Happy | 2848 |
| Delighted | 2516 |
**Download**
You can download the dataset from:
- [SentiPers](https://github.com/phosseini/sentipers)
- [DeepSentiPers](https://github.com/JoyeBright/DeepSentiPers)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
| SentiPers (Multi Class) | 71.31* | 71.11 | - | 69.33 |
| SentiPers (Binary Class) | 92.42* | 92.13 | - | 91.98 |
## How to use :hugs:
| Task | Notebook |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Sentiment Analysis | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/bert-fa-base-uncased-sentiment-digikala | 2021-05-18T20:59:17.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 184 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
### Digikala
Digikala user comments provided by [Open Data Mining Program (ODMP)](https://www.digikala.com/opendata/). This dataset contains 62,321 user comments with three labels:
| Label | # |
|:---------------:|:------:|
| no_idea | 10394 |
| not_recommended | 15885 |
| recommended | 36042 |
**Download**
You can download the dataset from [here](https://www.digikala.com/opendata/)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
| Digikala User Comments | 81.72 | 81.74* | 80.74 | - |
## How to use :hugs:
| Task | Notebook |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Sentiment Analysis | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/bert-fa-base-uncased-sentiment-snappfood | 2021-05-18T21:00:55.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"test_predictions.txt",
"test_results.txt",
"tf_model.h5",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| HooshvareLab | 115 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Persian Sentiment [Digikala, SnappFood, DeepSentiPers]
It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types.
### SnappFood
[Snappfood](https://snappfood.ir/) (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):
1. Happy
2. Sad
| Label | # |
|:--------:|:-----:|
| Negative | 35000 |
| Positive | 35000 |
**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=15J4zPN1BD7Q_ZIQ39VeFquwSoW8qTxgu)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
| SnappFood User Comments | 87.98 | 88.12* | 87.87 | - |
## How to use :hugs:
| Task | Notebook |
|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Sentiment Analysis | [](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/bert-fa-base-uncased | 2021-05-18T21:02:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"masked-lm",
"fa",
"arxiv:2005.12515",
"transformers",
"bert-fa",
"bert-persian",
"persian-lm",
"license:apache-2.0",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"tf_model.h5",
"vocab.txt"
]
| HooshvareLab | 3,262 | transformers | ---
language: fa
tags:
- bert-fa
- bert-persian
- persian-lm
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
## Introduction
ParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than `3.9M` documents, `73M` sentences, and `1.3B` words.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=bert-fa) to look for
fine-tuned versions on a task that interests you.
### How to use
#### TensorFlow 2.0
```python
from transformers import AutoConfig, AutoTokenizer, TFAutoModel
config = AutoConfig.from_pretrained("HooshvareLab/bert-fa-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased")
model = TFAutoModel.from_pretrained("HooshvareLab/bert-fa-base-uncased")
text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد میتوانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است."
tokenizer.tokenize(text)
>>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.']
```
#### Pytorch
```python
from transformers import AutoConfig, AutoTokenizer, AutoModel
config = AutoConfig.from_pretrained("HooshvareLab/bert-fa-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased")
model = AutoModel.from_pretrained("HooshvareLab/bert-fa-base-uncased")
```
## Training
ParsBERT trained on a massive amount of public corpora ([Persian Wikidumps](https://dumps.wikimedia.org/fawiki/), [MirasText](https://github.com/miras-tech/MirasText)) and six other manually crawled text data from a various type of websites ([BigBang Page](https://bigbangpage.com/) `scientific`, [Chetor](https://www.chetor.com/) `lifestyle`, [Eligasht](https://www.eligasht.com/Blog/) `itinerary`, [Digikala](https://www.digikala.com/mag/) `digital magazine`, [Ted Talks](https://www.ted.com/talks) `general conversational`, Books `novels, storybooks, short stories from old to the contemporary era`).
As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpora into a proper format.
## Goals
Objective goals during training are as below (after 300k steps).
``` bash
***** Eval results *****
global_step = 300000
loss = 1.4392426
masked_lm_accuracy = 0.6865794
masked_lm_loss = 1.4469004
next_sentence_accuracy = 1.0
next_sentence_loss = 6.534152e-05
```
## Derivative models
### Base Config
#### ParsBERT v2.0 Model
- [HooshvareLab/bert-fa-base-uncased](https://huggingface.co/HooshvareLab/bert-fa-base-uncased)
#### ParsBERT v2.0 Sentiment Analysis
- [HooshvareLab/bert-fa-base-uncased-sentiment-digikala](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-digikala)
- [HooshvareLab/bert-fa-base-uncased-sentiment-snappfood](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood)
- [HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary)
- [HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi)
#### ParsBERT v2.0 Text Classification
- [HooshvareLab/bert-fa-base-uncased-clf-digimag](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-digimag)
- [HooshvareLab/bert-fa-base-uncased-clf-persiannews](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-persiannews)
#### ParsBERT v2.0 NER
- [HooshvareLab/bert-fa-base-uncased-ner-peyma](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-peyma)
- [HooshvareLab/bert-fa-base-uncased-ner-arman](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-arman)
## Eval results
ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.
### Sentiment Analysis (SA) Task
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
| Digikala User Comments | 81.72 | 81.74* | 80.74 | - |
| SnappFood User Comments | 87.98 | 88.12* | 87.87 | - |
| SentiPers (Multi Class) | 71.31* | 71.11 | - | 69.33 |
| SentiPers (Binary Class) | 92.42* | 92.13 | - | 91.98 |
### Text Classification (TC) Task
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT |
|:-----------------:|:-----------:|:-----------:|:-----:|
| Digikala Magazine | 93.65* | 93.59 | 90.72 |
| Persian News | 97.44* | 97.19 | 95.79 |
### Named Entity Recognition (NER) Task
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|:-------:|:-----------:|:-----------:|:-----:|:----------:|:------------:|:--------:|:--------------:|:----------:|
| PEYMA | 93.40* | 93.10 | 86.64 | - | 90.59 | - | 84.00 | - |
| ARMAN | 99.84* | 98.79 | 95.89 | 89.9 | 84.03 | 86.55 | - | 77.45 |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo.
|
HooshvareLab/bert-fa-zwnj-base-ner | 2021-05-18T21:04:35.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"transformers"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| HooshvareLab | 34 | transformers | ---
language: fa
---
# BertNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten types of entities:
- Date (DAT)
- Event (EVE)
- Facility (FAC)
- Location (LOC)
- Money (MON)
- Organization (ORG)
- Percent (PCT)
- Person (PER)
- Product (PRO)
- Time (TIM)
## Dataset Information
| | Records | B-DAT | B-EVE | B-FAC | B-LOC | B-MON | B-ORG | B-PCT | B-PER | B-PRO | B-TIM | I-DAT | I-EVE | I-FAC | I-LOC | I-MON | I-ORG | I-PCT | I-PER | I-PRO | I-TIM |
|:------|----------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Train | 29133 | 1423 | 1487 | 1400 | 13919 | 417 | 15926 | 355 | 12347 | 1855 | 150 | 1947 | 5018 | 2421 | 4118 | 1059 | 19579 | 573 | 7699 | 1914 | 332 |
| Valid | 5142 | 267 | 253 | 250 | 2362 | 100 | 2651 | 64 | 2173 | 317 | 19 | 373 | 799 | 387 | 717 | 270 | 3260 | 101 | 1382 | 303 | 35 |
| Test | 6049 | 407 | 256 | 248 | 2886 | 98 | 3216 | 94 | 2646 | 318 | 43 | 568 | 888 | 408 | 858 | 263 | 3967 | 141 | 1707 | 296 | 78 |
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
**Overall**
| Model | accuracy | precision | recall | f1 |
|:----------:|:--------:|:---------:|:--------:|:--------:|
| Bert | 0.995086 | 0.953454 | 0.961113 | 0.957268 |
**Per entities**
| | number | precision | recall | f1 |
|:---: |:------: |:---------: |:--------: |:--------: |
| DAT | 407 | 0.860636 | 0.864865 | 0.862745 |
| EVE | 256 | 0.969582 | 0.996094 | 0.982659 |
| FAC | 248 | 0.976190 | 0.991935 | 0.984000 |
| LOC | 2884 | 0.970232 | 0.971914 | 0.971072 |
| MON | 98 | 0.905263 | 0.877551 | 0.891192 |
| ORG | 3216 | 0.939125 | 0.954602 | 0.946800 |
| PCT | 94 | 1.000000 | 0.968085 | 0.983784 |
| PER | 2645 | 0.965244 | 0.965974 | 0.965608 |
| PRO | 318 | 0.981481 | 1.000000 | 0.990654 |
| TIM | 43 | 0.692308 | 0.837209 | 0.757895 |
## How To Use
You use this model with Transformers pipeline for NER.
### Installing requirements
```bash
pip install transformers
```
### How to predict using pipeline
```python
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification # for pytorch
from transformers import TFAutoModelForTokenClassification # for tensorflow
from transformers import pipeline
model_name_or_path = "HooshvareLab/bert-fa-zwnj-base-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch
# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند."
ner_results = nlp(example)
print(ner_results)
```
## Questions?
Post a Github issue on the [ParsNER Issues](https://github.com/hooshvare/parsner/issues) repo. |
HooshvareLab/bert-fa-zwnj-base | 2021-05-18T21:05:42.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"masked-lm",
"fa",
"arxiv:2005.12515",
"transformers",
"license:apache-2.0",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| HooshvareLab | 1,515 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v3.0)
A Transformer-based Model for Persian Language Understanding
The new version of BERT v3.0 for Persian is available today and can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.
## Introduction
ParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news).
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/distilbert-fa-zwnj-base-ner | 2021-03-21T14:32:29.000Z | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"fa",
"transformers"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| HooshvareLab | 47 | transformers | ---
language: fa
---
# DistilbertNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten types of entities:
- Date (DAT)
- Event (EVE)
- Facility (FAC)
- Location (LOC)
- Money (MON)
- Organization (ORG)
- Percent (PCT)
- Person (PER)
- Product (PRO)
- Time (TIM)
## Dataset Information
| | Records | B-DAT | B-EVE | B-FAC | B-LOC | B-MON | B-ORG | B-PCT | B-PER | B-PRO | B-TIM | I-DAT | I-EVE | I-FAC | I-LOC | I-MON | I-ORG | I-PCT | I-PER | I-PRO | I-TIM |
|:------|----------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Train | 29133 | 1423 | 1487 | 1400 | 13919 | 417 | 15926 | 355 | 12347 | 1855 | 150 | 1947 | 5018 | 2421 | 4118 | 1059 | 19579 | 573 | 7699 | 1914 | 332 |
| Valid | 5142 | 267 | 253 | 250 | 2362 | 100 | 2651 | 64 | 2173 | 317 | 19 | 373 | 799 | 387 | 717 | 270 | 3260 | 101 | 1382 | 303 | 35 |
| Test | 6049 | 407 | 256 | 248 | 2886 | 98 | 3216 | 94 | 2646 | 318 | 43 | 568 | 888 | 408 | 858 | 263 | 3967 | 141 | 1707 | 296 | 78 |
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
**Overall**
| Model | accuracy | precision | recall | f1 |
|:----------:|:--------:|:---------:|:--------:|:--------:|
| Distilbert | 0.994534 | 0.946326 | 0.95504 | 0.950663 |
**Per entities**
| | number | precision | recall | f1 |
|:---: |:------: |:---------: |:--------: |:--------: |
| DAT | 407 | 0.812048 | 0.828010 | 0.819951 |
| EVE | 256 | 0.955056 | 0.996094 | 0.975143 |
| FAC | 248 | 0.972549 | 1.000000 | 0.986083 |
| LOC | 2884 | 0.968403 | 0.967060 | 0.967731 |
| MON | 98 | 0.925532 | 0.887755 | 0.906250 |
| ORG | 3216 | 0.932095 | 0.951803 | 0.941846 |
| PCT | 94 | 0.936842 | 0.946809 | 0.941799 |
| PER | 2645 | 0.959818 | 0.957278 | 0.958546 |
| PRO | 318 | 0.963526 | 0.996855 | 0.979907 |
| TIM | 43 | 0.760870 | 0.813953 | 0.786517 |
## How To Use
You use this model with Transformers pipeline for NER.
### Installing requirements
```bash
pip install transformers
```
### How to predict using pipeline
```python
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification # for pytorch
from transformers import TFAutoModelForTokenClassification # for tensorflow
from transformers import pipeline
model_name_or_path = "HooshvareLab/distilbert-fa-zwnj-base-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch
# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند."
ner_results = nlp(example)
print(ner_results)
```
## Questions?
Post a Github issue on the [ParsNER Issues](https://github.com/hooshvare/parsner/issues) repo. |
HooshvareLab/distilbert-fa-zwnj-base | 2021-03-16T16:30:29.000Z | [
"pytorch",
"tf",
"distilbert",
"masked-lm",
"fa",
"transformers",
"license:apache-2.0",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| HooshvareLab | 303 | transformers | ---
language: fa
license: apache-2.0
---
# DistilBERT
This model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo. |
HooshvareLab/gpt2-fa-comment | 2021-05-21T10:47:25.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"fa",
"transformers",
"license:apache-2.0",
"text-generation"
]
| text-generation | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
]
| HooshvareLab | 33 | transformers | ---
language: fa
license: apache-2.0
widget:
- text: "<s>نمونه دیدگاه هم خوب هم بد به طور کلی <sep>"
- text: "<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم <sep>"
- text: "<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی <sep>"
- text: "<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و داستان <sep>"
- text: "<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم و کیفیت <sep>"
---
# Persian Comment Generator
The model can generate comments based on your aspects, and the model was fine-tuned on [persiannlp/parsinlu](https://github.com/persiannlp/parsinlu). Currently, the model only supports aspects in the food and movie scope. You can see the whole aspects in the following section.
## Comments Aspects
```text
<s>نمونه دیدگاه هم خوب هم بد به طور کلی <sep>
<s>نمونه دیدگاه خوب به طور کلی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب به طور کلی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی به طور کلی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارسال <sep>
<s>نمونه دیدگاه منفی از نظر طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم به طور کلی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم <sep>
<s>نمونه دیدگاه خیلی منفی به طور کلی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و ارسال و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و کیفیت و طعم <sep>
<s>نمونه دیدگاه خوب از نظر ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی و ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و کیفیت و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم و ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم از نظر بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خوب از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش غذایی و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارسال و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال <sep>
<s>نمونه دیدگاه نظری ندارم از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و بسته بندی و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه منفی از نظر موسیقی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صدا <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و فیلمبرداری و کارگردانی و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و بازی <sep>
<s>نمونه دیدگاه منفی از نظر بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خوب از نظر بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خوب از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و کارگردانی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صحنه و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و موسیقی <sep>
<s>نمونه دیدگاه خوب از نظر صحنه و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و موسیقی و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و موسیقی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان <sep>
<s>نمونه دیدگاه خوب از نظر بازی و داستان و موسیقی و کارگردانی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر فیلمبرداری و صحنه و موسیقی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه نظری ندارم از نظر بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی و صحنه <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی و داستان و بازی و فیلمبرداری <sep>
<s>نمونه دیدگاه خوب از نظر بازی و صحنه و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و موسیقی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و صحنه <sep>
<s>نمونه دیدگاه خیلی خوب از نظر فیلمبرداری و صحنه و داستان و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر فیلمبرداری و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و داستان و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر موسیقی و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر موسیقی و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر صحنه و فیلمبرداری و داستان و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان و موسیقی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی و صدا و صحنه و داستان <sep>
<s>نمونه دیدگاه خوب از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر داستان و بازی و موسیقی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی و بازی و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر صحنه و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خوب از نظر موسیقی و داستان <sep>
<s>نمونه دیدگاه منفی از نظر موسیقی و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صدا و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و فیلمبرداری و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر صحنه <sep>
<s>نمونه دیدگاه منفی از نظر داستان و صحنه <sep>
<s>نمونه دیدگاه منفی از نظر بازی و صحنه و صدا <sep>
<s>نمونه دیدگاه خیلی منفی از نظر فیلمبرداری و صدا <sep>
<s>نمونه دیدگاه خیلی خوب از نظر موسیقی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و فیلمبرداری و موسیقی و کارگردانی و داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر فیلمبرداری و داستان و بازی <sep>
<s>نمونه دیدگاه منفی از نظر صحنه و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی و داستان <sep>
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo. |
HooshvareLab/gpt2-fa-poetry | 2021-05-21T10:50:14.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"fa",
"transformers",
"license:apache-2.0",
"text-generation"
]
| text-generation | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
]
| HooshvareLab | 39 | transformers | ---
language: fa
license: apache-2.0
widget:
- text: "<s>رودکی<|startoftext|>"
- text: "<s>فردوسی<|startoftext|>"
- text: "<s>خیام<|startoftext|>"
- text: "<s>عطار<|startoftext|>"
- text: "<s>نظامی<|startoftext|>"
---
# Persian Poet GPT2
## Poets
The model can generate poetry based on your favorite poet, and you need to add one of the following lines as the input the box on the right side or follow the [fine-tuning notebook](https://colab.research.google.com/github/hooshvare/parsgpt/blob/master/notebooks/Persian_Poetry_FineTuning.ipynb).
```text
<s>رودکی<|startoftext|>
<s>فردوسی<|startoftext|>
<s>کسایی<|startoftext|>
<s>ناصرخسرو<|startoftext|>
<s>منوچهری<|startoftext|>
<s>فرخی سیستانی<|startoftext|>
<s>مسعود سعد سلمان<|startoftext|>
<s>ابوسعید ابوالخیر<|startoftext|>
<s>باباطاهر<|startoftext|>
<s>فخرالدین اسعد گرگانی<|startoftext|>
<s>اسدی توسی<|startoftext|>
<s>هجویری<|startoftext|>
<s>خیام<|startoftext|>
<s>نظامی<|startoftext|>
<s>عطار<|startoftext|>
<s>سنایی<|startoftext|>
<s>خاقانی<|startoftext|>
<s>انوری<|startoftext|>
<s>عبدالواسع جبلی<|startoftext|>
<s>نصرالله منشی<|startoftext|>
<s>مهستی گنجوی<|startoftext|>
<s>باباافضل کاشانی<|startoftext|>
<s>مولوی<|startoftext|>
<s>سعدی<|startoftext|>
<s>خواجوی کرمانی<|startoftext|>
<s>عراقی<|startoftext|>
<s>سیف فرغانی<|startoftext|>
<s>حافظ<|startoftext|>
<s>اوحدی<|startoftext|>
<s>شیخ محمود شبستری<|startoftext|>
<s>عبید زاکانی<|startoftext|>
<s>امیرخسرو دهلوی<|startoftext|>
<s>سلمان ساوجی<|startoftext|>
<s>شاه نعمتالله ولی<|startoftext|>
<s>جامی<|startoftext|>
<s>هلالی جغتایی<|startoftext|>
<s>وحشی<|startoftext|>
<s>محتشم کاشانی<|startoftext|>
<s>شیخ بهایی<|startoftext|>
<s>عرفی<|startoftext|>
<s>رضیالدین آرتیمانی<|startoftext|>
<s>صائب تبریزی<|startoftext|>
<s>فیض کاشانی<|startoftext|>
<s>بیدل دهلوی<|startoftext|>
<s>هاتف اصفهانی<|startoftext|>
<s>فروغی بسطامی<|startoftext|>
<s>قاآنی<|startoftext|>
<s>ملا هادی سبزواری<|startoftext|>
<s>پروین اعتصامی<|startoftext|>
<s>ملکالشعرای بهار<|startoftext|>
<s>شهریار<|startoftext|>
<s>رهی معیری<|startoftext|>
<s>اقبال لاهوری<|startoftext|>
<s>خلیلالله خلیلی<|startoftext|>
<s>شاطرعباس صبوحی<|startoftext|>
<s>نیما یوشیج ( آوای آزاد )<|startoftext|>
<s>احمد شاملو<|startoftext|>
<s>سهراب سپهری<|startoftext|>
<s>فروغ فرخزاد<|startoftext|>
<s>سیمین بهبهانی<|startoftext|>
<s>مهدی اخوان ثالث<|startoftext|>
<s>محمدحسن بارق شفیعی<|startoftext|>
<s>شیون فومنی<|startoftext|>
<s>کامبیز صدیقی کسمایی<|startoftext|>
<s>بهرام سالکی<|startoftext|>
<s>عبدالقهّار عاصی<|startoftext|>
<s>اِ لیـــار (جبار محمدی )<|startoftext|>
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo. |
HooshvareLab/gpt2-fa | 2021-05-21T10:51:23.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"fa",
"transformers",
"license:apache-2.0",
"text-generation"
]
| text-generation | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
]
| HooshvareLab | 373 | transformers | ---
language: fa
license: apache-2.0
widget:
- text: "در یک اتفاق شگفت انگیز، پژوهشگران"
- text: "گرفتگی بینی در کودکان و بهخصوص نوزادان باعث میشود"
- text: "امیدواریم نوروز امسال سالی"
---
# ParsGPT2
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ParsGPT2,
author = {Hooshvare Team},
title = {ParsGPT2 the Persian version of GPT2},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/hooshvare/parsgpt}},
}
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo. |
HooshvareLab/roberta-fa-zwnj-base-ner | 2021-05-20T11:55:34.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"token-classification",
"fa",
"transformers"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
]
| HooshvareLab | 11 | transformers | ---
language: fa
---
# RobertaNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten types of entities:
- Date (DAT)
- Event (EVE)
- Facility (FAC)
- Location (LOC)
- Money (MON)
- Organization (ORG)
- Percent (PCT)
- Person (PER)
- Product (PRO)
- Time (TIM)
## Dataset Information
| | Records | B-DAT | B-EVE | B-FAC | B-LOC | B-MON | B-ORG | B-PCT | B-PER | B-PRO | B-TIM | I-DAT | I-EVE | I-FAC | I-LOC | I-MON | I-ORG | I-PCT | I-PER | I-PRO | I-TIM |
|:------|----------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Train | 29133 | 1423 | 1487 | 1400 | 13919 | 417 | 15926 | 355 | 12347 | 1855 | 150 | 1947 | 5018 | 2421 | 4118 | 1059 | 19579 | 573 | 7699 | 1914 | 332 |
| Valid | 5142 | 267 | 253 | 250 | 2362 | 100 | 2651 | 64 | 2173 | 317 | 19 | 373 | 799 | 387 | 717 | 270 | 3260 | 101 | 1382 | 303 | 35 |
| Test | 6049 | 407 | 256 | 248 | 2886 | 98 | 3216 | 94 | 2646 | 318 | 43 | 568 | 888 | 408 | 858 | 263 | 3967 | 141 | 1707 | 296 | 78 |
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
**Overall**
| Model | accuracy | precision | recall | f1 |
|:----------:|:--------:|:---------:|:--------:|:--------:|
| Roberta | 0.994849 | 0.949816 | 0.960235 | 0.954997 |
**Per entities**
| | number | precision | recall | f1 |
|:---: |:------: |:---------: |:--------: |:--------: |
| DAT | 407 | 0.844869 | 0.869779 | 0.857143 |
| EVE | 256 | 0.948148 | 1.000000 | 0.973384 |
| FAC | 248 | 0.957529 | 1.000000 | 0.978304 |
| LOC | 2884 | 0.965422 | 0.968100 | 0.966759 |
| MON | 98 | 0.937500 | 0.918367 | 0.927835 |
| ORG | 3216 | 0.943662 | 0.958333 | 0.950941 |
| PCT | 94 | 1.000000 | 0.968085 | 0.983784 |
| PER | 2646 | 0.957030 | 0.959562 | 0.958294 |
| PRO | 318 | 0.963636 | 1.000000 | 0.981481 |
| TIM | 43 | 0.739130 | 0.790698 | 0.764045 |
## How To Use
You use this model with Transformers pipeline for NER.
### Installing requirements
```bash
pip install transformers
```
### How to predict using pipeline
```python
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification # for pytorch
from transformers import TFAutoModelForTokenClassification # for tensorflow
from transformers import pipeline
model_name_or_path = "HooshvareLab/roberta-fa-zwnj-base-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch
# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند."
ner_results = nlp(example)
print(ner_results)
```
## Questions?
Post a Github issue on the [ParsNER Issues](https://github.com/hooshvare/parsner/issues) repo. |
HooshvareLab/roberta-fa-zwnj-base | 2021-05-20T11:56:49.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"masked-lm",
"fa",
"transformers",
"license:apache-2.0",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
]
| HooshvareLab | 61 | transformers | ---
language: fa
license: apache-2.0
---
# Roberta
This model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.
## Questions?
Post a Github issue on the [ParsRoBERTa Issues](https://github.com/hooshvare/roberta/issues) repo. |
Hoya/jjmodel | 2021-04-01T12:12:02.000Z | []
| [
".gitattributes"
]
| Hoya | 0 | |||
Huffon/klue-roberta-base-nli | 2021-06-18T22:03:23.000Z | [
"pytorch",
"roberta",
"text-classification",
"ko",
"dataset:klue",
"sentence-transformers",
"nli"
]
| text-classification | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| Huffon | 0 | sentence-transformers | |
Huffon/sentence-klue-roberta-base | 2021-06-18T18:13:40.000Z | [
"pytorch",
"roberta",
"ko",
"dataset:klue",
"arxiv:1908.10084",
"sentence-transformers"
]
| [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| Huffon | 0 | sentence-transformers | ||
Huntersx/cola_model | 2021-05-18T21:06:41.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"config.json",
"eval_results_cola.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"training_args.bin"
]
| Huntersx | 13 | transformers | |
HyeonSang/kobert-sentiment | 2021-05-19T11:17:24.000Z | [
"tf",
"bert",
"transformers"
]
| [
".gitattributes",
"config.json",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_78b3253a26.model",
"tokenizer_config.json",
"vocab.txt"
]
| HyeonSang | 16 | transformers | ||
HyeonSang/test | 2021-05-19T11:17:43.000Z | [
"tf",
"bert",
"transformers"
]
| [
".gitattributes",
"config.json",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer_78b3253a26.model",
"tokenizer_config.json",
"vocab.txt"
]
| HyeonSang | 6 | transformers | ||
ITworkonline/twitter_tac_model | 2021-04-02T20:17:28.000Z | []
| [
".gitattributes"
]
| ITworkonline | 0 | |||
IceeSoHighYetSoLow19/Luna | 2021-01-25T04:34:58.000Z | []
| [
".gitattributes"
]
| IceeSoHighYetSoLow19 | 0 | |||
Id405/Adam | 2021-04-15T23:15:47.000Z | []
| [
".gitattributes",
"README.md"
]
| Id405 | 0 | |||
IlyaGusev/gen_title_ria_rubert_250000 | 2020-12-03T15:56:23.000Z | [
"pytorch",
"encoder-decoder",
"seq2seq",
"transformers",
"text2text-generation"
]
| text2text-generation | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"vocab.txt"
]
| IlyaGusev | 20 | transformers | |
IlyaGusev/gen_title_tg_bottleneck | 2020-11-28T11:45:25.000Z | [
"pytorch",
"encoder-decoder",
"transformers"
]
| [
".gitattributes",
"config.json",
"pytorch_model.bin",
"vocab.txt"
]
| IlyaGusev | 9 | transformers | ||
IlyaGusev/gen_title_tg_bottleneck_encoder | 2021-05-18T21:08:31.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
]
| [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"vocab.txt"
]
| IlyaGusev | 16 | transformers | ||
IlyaGusev/mbart_ru_sum_gazeta | 2021-01-06T18:45:13.000Z | [
"pytorch",
"mbart",
"seq2seq",
"ru",
"arxiv:2006.11063",
"transformers",
"summarization",
"license:apache-2.0",
"text2text-generation"
]
| summarization | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer_config.json"
]
| IlyaGusev | 1,100 | transformers | ---
language:
- ru
tags:
- summarization
- mbart
license: apache-2.0
---
# MBARTRuSumGazeta
## Model description
This is a ported version of [fairseq model](https://www.dropbox.com/s/fijtntnifbt9h0k/gazeta_mbart_v2_fairseq.tar.gz).
For more details, please see, [Dataset for Automatic Summarization of Russian News](https://arxiv.org/abs/2006.11063).
## Intended uses & limitations
#### How to use
```python
from transformers import MBartTokenizer, MBartForConditionalGeneration
article_text = "..."
model_name = "IlyaGusev/mbart_ru_sum_gazeta"
tokenizer = MBartTokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)
input_ids = tokenizer.prepare_seq2seq_batch(
[article_text],
src_lang="en_XX", # fairseq training artifact
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=600
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=162,
no_repeat_ngram_size=3,
num_beams=5,
top_k=0
)[0]
summary = tokenizer.decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(summary)
```
#### Limitations and bias
- The model should work well with Gazeta.ru articles, but for any other agencies it can suffer from domain shift
## Training data
- Dataset: https://github.com/IlyaGusev/gazeta
## Training procedure
- Fairseq training script: https://github.com/IlyaGusev/summarus/blob/master/external/bart_scripts/train.sh
- Porting: https://colab.research.google.com/drive/13jXOlCpArV-lm4jZQ0VgOpj6nFBYrLAr
## Eval results
| Model | R-1-f | R-2-f | R-L-f | METEOR | BLEU |
|:--------------------------|:------|:------|:------|:-------|:-----|
| gazeta_mbart | 32.3 | 14.3 | 27.9 | 25.5 | 48.9 |
Predicting all summaries:
```python
import json
import torch
from transformers import MBartTokenizer, MBartForConditionalGeneration
def gen_batch(inputs, batch_size):
batch_start = 0
while batch_start < len(inputs):
yield inputs[batch_start: batch_start + batch_size]
batch_start += batch_size
def predict(
model_name,
test_file,
predictions_file,
targets_file,
max_source_tokens_count=600,
max_target_tokens_count=160,
use_cuda=True,
batch_size=4
):
inputs = []
targets = []
with open(test_file, "r") as r:
for line in r:
record = json.loads(line)
inputs.append(record["text"])
targets.append(record["summary"])
tokenizer = MBartTokenizer.from_pretrained(model_name)
device = torch.device("cuda:0") if use_cuda else torch.device("cpu")
model = MBartForConditionalGeneration.from_pretrained(model_name).to(device)
predictions = []
for batch in gen_batch(inputs, batch_size):
input_ids = tokenizer.prepare_seq2seq_batch(
batch,
src_lang="en_XX",
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=max_source_tokens_count
)["input_ids"].to(device)
output_ids = model.generate(
input_ids=input_ids,
max_length=max_target_tokens_count + 2,
no_repeat_ngram_size=3,
num_beams=5,
top_k=0
)
summaries = tokenizer.batch_decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
for s in summaries:
print(s)
predictions.extend(summaries)
with open(predictions_file, "w") as w:
for p in predictions:
w.write(p.strip() + "\n")
with open(targets_file, "w") as w:
for t in targets:
w.write(t.strip() + "\n")
predict("IlyaGusev/mbart_ru_sum_gazeta", "gazeta_test.jsonl", "predictions.txt", "targets.txt")
```
Evaluation: https://github.com/IlyaGusev/summarus/blob/master/evaluate.py
Flags: --language ru --tokenize-after --lower
### BibTeX entry and citation info
```bibtex
@InProceedings{10.1007/978-3-030-59082-6_9,
author="Gusev, Ilya",
editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia",
title="Dataset for Automatic Summarization of Russian News",
booktitle="Artificial Intelligence and Natural Language",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="122--134",
isbn="978-3-030-59082-6"
}
```
|
IlyaGusev/news_tg_rubert | 2021-06-16T19:43:26.000Z | [
"pytorch",
"ru",
"license:apache-2.0"
]
| [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"vocab.txt"
]
| IlyaGusev | 184 | ---
language:
- ru
license: apache-2.0
---
# NewsTgRuBERT
Training script: https://github.com/dialogue-evaluation/Russian-News-Clustering-and-Headline-Generation/blob/main/train_mlm.py |
||
IlyaGusev/rubert_telegram_headlines | 2021-03-16T12:13:34.000Z | [
"pytorch",
"encoder-decoder",
"seq2seq",
"ru",
"transformers",
"summarization",
"license:apache-2.0",
"text2text-generation"
]
| summarization | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"vocab.txt"
]
| IlyaGusev | 443 | transformers | ---
language:
- ru
tags:
- summarization
license: apache-2.0
---
# RuBertTelegramHeadlines
## Model description
Example model for [Headline generation competition](https://competitions.codalab.org/competitions/29905)
Based on [RuBERT](http://docs.deeppavlov.ai/en/master/features/models/bert.html) model
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, EncoderDecoderModel
model_name = "IlyaGusev/rubert_telegram_headlines"
tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False, do_basic_tokenize=False, strip_accents=False)
model = EncoderDecoderModel.from_pretrained(model_name)
article_text = "..."
input_ids = tokenizer(
[article_text],
add_special_tokens=True,
max_length=256,
padding="max_length",
truncation=True,
return_tensors="pt",
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=64,
no_repeat_ngram_size=3,
num_beams=10,
top_p=0.95
)[0]
headline = tokenizer.decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(headline)
```
## Training data
- Dataset: [ru_all_split.tar.gz](https://www.dropbox.com/s/ykqk49a8avlmnaf/ru_all_split.tar.gz)
## Training procedure
```python
import random
import torch
from torch.utils.data import Dataset
from tqdm.notebook import tqdm
from transformers import BertTokenizer, EncoderDecoderModel, Trainer, TrainingArguments, logging
def convert_to_tensors(
tokenizer,
text,
max_text_tokens_count,
max_title_tokens_count = None,
title = None
):
inputs = tokenizer(
text,
add_special_tokens=True,
max_length=max_text_tokens_count,
padding="max_length",
truncation=True
)
result = {
"input_ids": torch.tensor(inputs["input_ids"]),
"attention_mask": torch.tensor(inputs["attention_mask"]),
}
if title is not None:
outputs = tokenizer(
title,
add_special_tokens=True,
max_length=max_title_tokens_count,
padding="max_length",
truncation=True
)
decoder_input_ids = torch.tensor(outputs["input_ids"])
decoder_attention_mask = torch.tensor(outputs["attention_mask"])
labels = decoder_input_ids.clone()
labels[decoder_attention_mask == 0] = -100
result.update({
"labels": labels,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask
})
return result
class GetTitleDataset(Dataset):
def __init__(
self,
original_records,
sample_rate,
tokenizer,
max_text_tokens_count,
max_title_tokens_count
):
self.original_records = original_records
self.sample_rate = sample_rate
self.tokenizer = tokenizer
self.max_text_tokens_count = max_text_tokens_count
self.max_title_tokens_count = max_title_tokens_count
self.records = []
for record in tqdm(original_records):
if random.random() > self.sample_rate:
continue
tensors = convert_to_tensors(
tokenizer=tokenizer,
title=record["title"],
text=record["text"],
max_title_tokens_count=self.max_title_tokens_count,
max_text_tokens_count=self.max_text_tokens_count
)
self.records.append(tensors)
def __len__(self):
return len(self.records)
def __getitem__(self, index):
return self.records[index]
def train(
train_records,
val_records,
pretrained_model_path,
train_sample_rate=1.0,
val_sample_rate=1.0,
output_model_path="models",
checkpoint=None,
max_text_tokens_count=256,
max_title_tokens_count=64,
batch_size=8,
logging_steps=1000,
eval_steps=10000,
save_steps=10000,
learning_rate=0.00003,
warmup_steps=2000,
num_train_epochs=3
):
logging.set_verbosity_info()
tokenizer = BertTokenizer.from_pretrained(
pretrained_model_path,
do_lower_case=False,
do_basic_tokenize=False,
strip_accents=False
)
train_dataset = GetTitleDataset(
train_records,
train_sample_rate,
tokenizer,
max_text_tokens_count=max_text_tokens_count,
max_title_tokens_count=max_title_tokens_count
)
val_dataset = GetTitleDataset(
val_records,
val_sample_rate,
tokenizer,
max_text_tokens_count=max_text_tokens_count,
max_title_tokens_count=max_title_tokens_count
)
model = EncoderDecoderModel.from_encoder_decoder_pretrained(pretrained_model_path, pretrained_model_path)
training_args = TrainingArguments(
output_dir=output_model_path,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
do_train=True,
do_eval=True,
overwrite_output_dir=False,
logging_steps=logging_steps,
eval_steps=eval_steps,
evaluation_strategy="steps",
save_steps=save_steps,
learning_rate=learning_rate,
warmup_steps=warmup_steps,
num_train_epochs=num_train_epochs,
max_steps=-1,
save_total_limit=1,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset
)
trainer.train(checkpoint)
model.save_pretrained(output_model_path)
``` |
Ilyes/wav2vec2-large-xlsr-53-french | 2021-06-14T11:00:04.000Z | [
"fr",
"dataset:common_voice",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"v0.1/config.json",
"v0.1/preprocessor_config.json",
"v0.1/pytorch_model.bin",
"v0.1/special_tokens_map.json",
"v0.1/tokenizer_config.json",
"v0.1/vocab.json",
"v1.0/config.json",
"v1.0/preprocessor_config.json",
"v1.0/pytorch_model.bin",
"v1.0/results.txt",
"v1.0/special_tokens_map.json",
"v1.0/text.txt",
"v1.0/tokenizer_config.json",
"v1.0/trans.txt",
"v1.0/vocab.json",
"v2.0/config.json",
"v2.0/preprocessor_config.json",
"v2.0/pytorch_model.bin",
"v2.0/results.txt",
"v2.0/special_tokens_map.json",
"v2.0/text.txt",
"v2.0/tokenizer_config.json",
"v2.0/trans.txt",
"v2.0/vocab.json",
"v3.0/config.json",
"v3.0/preprocessor_config.json",
"v3.0/pytorch_model.bin",
"v3.0/results.txt",
"v3.0/special_tokens_map.json",
"v3.0/text.txt",
"v3.0/tokenizer_config.json",
"v3.0/trans.txt",
"v3.0/vocab.json"
]
| Ilyes | 31 | ---
language: fr
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-large-xlsr-53-French by Ilyes Rebai
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
args: fr
metrics:
- name: Test WER
type: wer
value: 20.89%
---
## Evaluation on Common Voice FR Test
The script used for training and evaluation can be found here: https://github.com/irebai/wav2vec2
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import re
model_name = "Ilyes/wav2vec2-large-xlsr-53-french"
model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda')
processor = Wav2Vec2Processor.from_pretrained(model_name)
ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr")
chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\!\ǃ\?\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\.\,\;\:\*\—\–\─\―\_\/\:\ː\;\,\=\«\»\→]'
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
ds = ds.map(map_to_array)
resampler = torchaudio.transforms.Resample(48_000, 16_000)
def map_to_pred(batch):
features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)
batch["target"] = batch["sentence"]
return batch
result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys()))
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
## Results
# v0.1
WER=18.29%
SER=71.44%
# v1.0
WER=15.97%
CER=5.51%
# v2.0
WER=14.71%
CER=5.06%
# v3.0
WER=12.82%
CER=4.40%
|
|
Ilyes/wav2vec2-large-xlsr-53-french_BPE | 2021-06-02T20:04:24.000Z | [
"pytorch",
"wav2vec2",
"transformers"
]
| [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"results.txt",
"special_tokens_map.json",
"text.txt",
"tokenizer.model",
"tokenizer_config.json",
"trans.txt",
"vocab.json"
]
| Ilyes | 6 | transformers | The script used for training can be found here: https://github.com/irebai/wav2vec2
## Results
WER=17.32%
CER=7.03%
|
|
Ilyes/wav2vec2-large-xlsr-53-french_punctuation | 2021-05-11T13:10:55.000Z | [
"pytorch",
"wav2vec2",
"fr",
"dataset:common_voice",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning",
"license:apache-2.0"
]
| automatic-speech-recognition | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"refs.txt",
"special_tokens_map.json",
"trs.txt",
"vocab.json"
]
| Ilyes | 82 | transformers | ---
language: fr
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning
license: apache-2.0
model-index:
- name: wav2vec2-large-xlsr-53-French_punctuation by Ilyes Rebai
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
args: fr
metrics:
- name: Test WER and CER on text and puctuation prediction
types: [wer, cer]
values: [19.47%, 6.66%]
- name: Test WER and CER on text without punctuation
types: [wer, cer]
values: [17.88%, 6.37%]
---
## Evaluation on Common Voice FR Test
```python
import re
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
model_name = "Ilyes/wav2vec2-large-xlsr-53-french_punctuation"
model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda')
processor = Wav2Vec2Processor.from_pretrained(model_name)
ds = load_dataset("common_voice", "fr", split="test")
chars_to_ignore_regex = '[\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\ǃ\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\|\;\:\*\—\–\─\―\_\/\:\ː\;\=\«\»\→]'
def normalize_text(text):
text = text.lower().strip()
text = re.sub('œ', 'oe', text)
text = re.sub('æ', 'ae', text)
text = re.sub("’|´|′|ʼ|‘|ʻ|`", "'", text)
text = re.sub("'+ ", " ", text)
text = re.sub(" '+", " ", text)
text = re.sub("'$", " ", text)
text = re.sub("' ", " ", text)
text = re.sub("−|‐", "-", text)
text = re.sub(" -", "", text)
text = re.sub("- ", "", text)
text = re.sub(chars_to_ignore_regex, '', text)
return text
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = normalize_text(batch["sentence"])
return batch
ds = ds.map(map_to_array)
resampler = torchaudio.transforms.Resample(48_000, 16_000)
def map_to_pred(batch):
features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)
batch["target"] = batch["sentence"]
# remove duplicates
batch["target"] = re.sub('\.+', '.', batch["target"])
batch["target"] = re.sub('\?+', '?', batch["target"])
batch["target"] = re.sub('!+', '!', batch["target"])
batch["target"] = re.sub(',+', ',', batch["target"])
return batch
result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys()))
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
## Some results
| Reference | Prediction |
| ------------- | ------------- |
| il vécut à new york et y enseigna une grande partie de sa vie. | il a vécu à new york et y enseigna une grande partie de sa vie. |
| au classement par nations, l'allemagne est la tenante du titre. | au classement der nation l'allemagne est la tenante du titre. |
| voici un petit calcul pour fixer les idées. | voici un petit calcul pour fixer les idées. |
| oh! tu dois être beau avec | oh! tu dois être beau avec. |
| babochet vous le voulez? | baboche, vous le voulez? |
| la commission est, par conséquent, défavorable à cet amendement. | la commission est, par conséquent, défavorable à cet amendement. |
All the references and predictions of the test corpus are already available in this repository.
## Results
text + punctuation
WER=21.47% CER=7.21%
text (without punctuation)
WER=19.71% CER=6.91%
|
Irene/blenderbot | 2021-03-27T19:22:56.000Z | []
| [
".gitattributes"
]
| Irene | 0 | |||
Itcast/bert-base-cnc | 2021-05-18T21:09:34.000Z | [
"pytorch",
"jax",
"bert",
"masked-lm",
"transformers",
"fill-mask"
]
| fill-mask | [
".gitattributes",
"added_tokens.json",
"config.json",
"eval_results.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| Itcast | 18 | transformers | |
Itcast/cnc_output | 2020-01-01T15:20:04.000Z | [
"pytorch",
"transformers"
]
| [
".gitattributes",
"added_tokens.json",
"config.json",
"eval_results.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| Itcast | 15 | transformers | ||
ItcastAI/bert_cn_finetuning | 2021-05-18T21:10:29.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"added_tokens.json",
"config.json",
"eval_results.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| ItcastAI | 21 | transformers | |
ItcastAI/bert_cn_finetunning | 2021-05-18T21:11:28.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"config.json",
"eval_results.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| ItcastAI | 19 | transformers | |
ItcastAI/bert_finetuning_test | 2021-05-18T21:12:26.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"added_tokens.json",
"config.json",
"eval_results.txt",
"eval_results_mrpc.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| ItcastAI | 39 | transformers | |
ItcastAI/bert_finetunning_test | 2021-05-18T21:13:27.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"config.json",
"eval_results.txt",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
]
| ItcastAI | 18 | transformers | |
Ivo/emscad-skill-extraction-conference-token-classification | 2021-06-15T09:20:03.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"transformers"
]
| token-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| Ivo | 21 | transformers | |
Ivo/emscad-skill-extraction-conference | 2021-06-15T07:59:57.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| Ivo | 8 | transformers | |
Ivo/emscad-skill-extraction-token-classification | 2021-06-15T09:37:47.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"transformers"
]
| token-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| Ivo | 21 | transformers | |
Ivo/emscad-skill-extraction | 2021-06-09T12:15:44.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
]
| Ivo | 66 | transformers | |
JDBN/t5-base-fr-qg-fquad | 2020-10-18T14:13:37.000Z | [
"pytorch",
"t5",
"seq2seq",
"fr",
"dataset:fquad",
"dataset:piaf",
"arxiv:1910.10683",
"arxiv:2002.06071",
"transformers",
"question-generation",
"text2text-generation"
]
| text2text-generation | [
"._config.json",
"._pytorch_model.bin",
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer_config.json"
]
| JDBN | 492 | transformers | ---
language: fr
widget:
- text: "generate question: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu <hl> en 2009 <hl> pour devenir le 44ème président des Etats-Unis d'Amérique. </s>"
- text: "question: Quand Barack Obama a t'il été élu président? context: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu en 2009 pour devenir le 44ème président des Etats-Unis d'Amérique. </s>"
tags:
- pytorch
- t5
- question-generation
- seq2seq
datasets:
- fquad
- piaf
---
# T5 Question Generation and Question Answering
## Model description
This model is a T5 Transformers model (airklizz/t5-base-multi-fr-wiki-news) that was fine-tuned in french on 3 different tasks
* question generation
* question answering
* answer extraction
It obtains quite good results on FQuAD validation dataset.
## Intended uses & limitations
This model functions for the 3 tasks mentionned earlier and was not tested on other tasks.
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("JDBN/t5-base-fr-qg-fquad")
tokenizer = T5Tokenizer.from_pretrained("JDBN/t5-base-fr-qg-fquad")
```
## Training data
The initial model used was https://huggingface.co/airKlizz/t5-base-multi-fr-wiki-news. This model was finetuned on a dataset composed of FQuAD and PIAF on the 3 tasks mentioned previously.
The data were preprocessed like this
* question generation: "generate question: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu <hl> en 2009 <hl> pour devenir le 44ème président des Etats-Unis d'Amérique."
* question answering: "question: Quand Barack Hussein Obamaa-t-il été élu président des Etats-Unis d’Amérique? context: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu en 2009 pour devenir le 44ème président des Etats-Unis d’Amérique."
* answer extraction: "extract_answers: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. <hl> Il a été élu en 2009 pour devenir le 44ème président des Etats-Unis d’Amérique <hl>."
The preprocessing we used was implemented in https://github.com/patil-suraj/question_generation
## Eval results
#### On FQuAD validation set
| BLEU_1 | BLEU_2 | BLEU_3 | BLEU_4 | METEOR | ROUGE_L | CIDEr |
|--------|--------|--------|--------|--------|---------|-------|
| 0.290 | 0.203 | 0.149 | 0.111 | 0.197 | 0.284 | 1.038 |
#### Question Answering metrics
For these metrics, the performance of this question answering model (https://huggingface.co/illuin/camembert-base-fquad) on FQuAD original question and on T5 generated questions are compared.
| Questions | Exact Match | F1 Score |
|------------------|--------|--------|
|Original FQuAD | 54.015 | 77.466 |
|Generated | 45.765 | 67.306 |
### BibTeX entry and citation info
```bibtex
@misc{githubPatil,
author = {Patil Suraj},
title = {question generation GitHub repository},
year = {2020},
howpublished={\url{https://github.com/patil-suraj/question_generation}}
}
@article{T5,
title={Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
author={Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
year={2019},
eprint={1910.10683},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{dhoffschmidt2020fquad,
title={FQuAD: French Question Answering Dataset},
author={Martin d'Hoffschmidt and Wacim Belblidia and Tom Brendlé and Quentin Heinrich and Maxime Vidal},
year={2020},
eprint={2002.06071},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
JDT/my-bert | 2021-04-19T02:06:16.000Z | []
| [
".gitattributes",
"README.md"
]
| JDT | 0 | |||
JP040/bert-german-sentiment-twitter | 2021-05-18T21:14:38.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.txt"
]
| JP040 | 13 | transformers | |
Jade/bert_base_law | 2021-03-08T06:59:50.000Z | []
| [
".gitattributes",
"README.md",
"readme.md"
]
| Jade | 0 | ---
language: "zh_CN"
thumbnail: "url to a thumbnail used in social sharing"
tags:
- NLP
- LAW
license: "MIT"
datasets:
- WIP
metrics:
- WIP
--- |
||
Jamesr227/Mod-bot-ai-small | 2021-06-03T15:56:22.000Z | []
| [
".gitattributes"
]
| Jamesr227 | 0 | |||
JasonCheung/gpt2 | 2021-03-26T07:56:33.000Z | []
| [
".gitattributes"
]
| JasonCheung | 0 | |||
JasonYe/Covid_19_NLP_twitter | 2021-05-21T23:07:16.000Z | []
| [
".gitattributes"
]
| JasonYe | 0 | |||
Javel/linkedin_post_t5 | 2020-12-19T17:24:45.000Z | [
"pytorch",
"t5",
"seq2seq",
"transformers",
"text2text-generation"
]
| text2text-generation | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"training_args.bin"
]
| Javel | 35 | transformers | |
Javel/t5_linkedin_post | 2020-12-18T17:47:21.000Z | []
| [
".gitattributes"
]
| Javel | 0 | |||
Jean-Baptiste/camembert-ner-with-dates | 2021-05-21T19:59:31.000Z | [
"pytorch",
"camembert",
"token-classification",
"fr",
"dataset:Jean-Baptiste/wikiner_fr",
"transformers"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer_config.json"
]
| Jean-Baptiste | 765 | transformers | ---
language: fr
datasets:
- Jean-Baptiste/wikiner_fr
widget:
- text: "Je m'appelle jean-baptiste et j'habite à montréal depuis fevr 2012"
---
# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag).
## Introduction
[camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates.
Model was trained on enriched version of wikiner-fr dataset (~170 634 sentences).
On my test data (mix of chat and email), this model got an f1 score of ~83% (in comparison dateparser was ~70%).
Dateparser library can still be be used on the output of this model in order to convert text to python datetime object
(https://dateparser.readthedocs.io/en/latest/).
## How to use camembert-ner-with-dates with HuggingFace
##### Load camembert-ner-with-dates and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner-with-dates")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner-with-dates")
##### Process text sample (from wikipedia)
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True)
nlp("Apple est créée le 1er avril 1976 dans le garage de la maison d'enfance de Steve Jobs à Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constituée sous forme de société le 3 janvier 1977 à l'origine sous le nom d'Apple Computer, mais pour ses 30 ans et pour refléter la diversification de ses produits, le mot « computer » est retiré le 9 janvier 2015.")
[{'entity_group': 'ORG',
'score': 0.9776379466056824,
'word': 'Apple',
'start': 0,
'end': 5},
{'entity_group': 'DATE',
'score': 0.9793774570737567,
'word': 'le 1er avril 1976 dans le',
'start': 15,
'end': 41},
{'entity_group': 'PER',
'score': 0.9958226680755615,
'word': 'Steve Jobs',
'start': 74,
'end': 85},
{'entity_group': 'LOC',
'score': 0.995087186495463,
'word': 'Los Altos',
'start': 87,
'end': 97},
{'entity_group': 'LOC',
'score': 0.9953305125236511,
'word': 'Californie',
'start': 100,
'end': 111},
{'entity_group': 'PER',
'score': 0.9961076378822327,
'word': 'Steve Jobs',
'start': 115,
'end': 126},
{'entity_group': 'PER',
'score': 0.9960325956344604,
'word': 'Steve Wozniak',
'start': 127,
'end': 141},
{'entity_group': 'PER',
'score': 0.9957776467005411,
'word': 'Ronald Wayne',
'start': 144,
'end': 157},
{'entity_group': 'DATE',
'score': 0.994030773639679,
'word': 'le 3 janvier 1977 à',
'start': 198,
'end': 218},
{'entity_group': 'ORG',
'score': 0.9720810294151306,
'word': "d'Apple Computer",
'start': 240,
'end': 257},
{'entity_group': 'DATE',
'score': 0.9924157659212748,
'word': '30 ans et',
'start': 272,
'end': 282},
{'entity_group': 'DATE',
'score': 0.9934852868318558,
'word': 'le 9 janvier 2015.',
'start': 363,
'end': 382}]
```
## Model performances (metric: seqeval)
Global
```
'precision': 0.928
'recall': 0.928
'f1': 0.928
```
By entity
```
Label LOC: (precision:0.929, recall:0.932, f1:0.931, support:9510)
Label PER: (precision:0.952, recall:0.965, f1:0.959, support:9399)
Label MISC: (precision:0.878, recall:0.844, f1:0.860, support:5364)
Label ORG: (precision:0.848, recall:0.883, f1:0.865, support:2299)
Label DATE: Not relevant because of method used to add date tag on wikiner dataset (estimated f1 ~90%)
```
|
Jean-Baptiste/camembert-ner | 2021-04-28T03:39:34.000Z | [
"pytorch",
"camembert",
"token-classification",
"fr",
"dataset:Jean-Baptiste/wikiner_fr",
"transformers"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer_config.json"
]
| Jean-Baptiste | 4,838 | transformers | ---
language: fr
datasets:
- Jean-Baptiste/wikiner_fr
widget:
- text: "Je m'appelle jean-baptiste et je vis à montréal"
---
# camembert-ner: model fine-tuned from camemBERT for NER task.
## Introduction
[camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset.
Model was trained on wikiner-fr dataset (~170 634 sentences).
Model was validated on emails/chat data and overperformed other models on this type of data specifically.
In particular the model seems to work better on entity that don't start with an upper case.
## How to use camembert-ner with HuggingFace
##### Load camembert-ner and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner")
##### Process text sample (from wikipedia)
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True)
nlp("Apple est créée le 1er avril 1976 dans le garage de la maison d'enfance de Steve Jobs à Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constituée sous forme de société le 3 janvier 1977 à l'origine sous le nom d'Apple Computer, mais pour ses 30 ans et pour refléter la diversification de ses produits, le mot « computer » est retiré le 9 janvier 2015.")
[{'entity_group': 'ORG',
'score': 0.9472818374633789,
'word': 'Apple',
'start': 0,
'end': 5},
{'entity_group': 'PER',
'score': 0.9838564991950989,
'word': 'Steve Jobs',
'start': 74,
'end': 85},
{'entity_group': 'LOC',
'score': 0.9831605950991312,
'word': 'Los Altos',
'start': 87,
'end': 97},
{'entity_group': 'LOC',
'score': 0.9834540486335754,
'word': 'Californie',
'start': 100,
'end': 111},
{'entity_group': 'PER',
'score': 0.9841555754343668,
'word': 'Steve Jobs',
'start': 115,
'end': 126},
{'entity_group': 'PER',
'score': 0.9843501806259155,
'word': 'Steve Wozniak',
'start': 127,
'end': 141},
{'entity_group': 'PER',
'score': 0.9841533899307251,
'word': 'Ronald Wayne',
'start': 144,
'end': 157},
{'entity_group': 'ORG',
'score': 0.9468960364659628,
'word': 'Apple Computer',
'start': 243,
'end': 257}]
```
## Model performances (metric: seqeval)
Global
```
'precision': 0.8859
'recall': 0.8971
'f1': 0.8914
```
By entity
```
'LOC': {'precision': 0.8905576596578294,
'recall': 0.900554675118859,
'f1': 0.8955282684352223},
'MISC': {'precision': 0.8175627240143369,
'recall': 0.8117437722419929,
'f1': 0.8146428571428571},
'ORG': {'precision': 0.8099480326651819,
'recall': 0.8265151515151515,
'f1': 0.8181477315335584},
'PER': {'precision': 0.9372509960159362,
'recall': 0.959812321501428,
'f1': 0.9483975005039308}
```
|
Jean-Baptiste/roberta-ticker | 2021-06-08T20:19:46.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"transformers"
]
| token-classification | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json"
]
| Jean-Baptiste | 14 | transformers | ---
language: en
widget:
- text: "I am going to buy 100 shares of cake tomorrow"
---
# roberta-ticker: model was fine-tuned from Roberta to detect financial tickers
## Introduction
This is a model specifically designed to identify tickers in text.
Model was trained on transformed dataset from following Kaggle dataset:
https://www.kaggle.com/omermetinn/tweets-about-the-top-companies-from-2015-to-2020
## How to use roberta-ticker with HuggingFace
##### Load roberta-ticker and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-ticker")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-ticker")
##### Process text sample
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True, ignore_labels='O')
nlp("I am going to buy 100 shares of cake tomorrow")
[{'entity_group': 'TICKER',
'score': 0.9612462520599365,
'word': ' cake',
'start': 32,
'end': 36}]
nlp("I am going to eat a cake tomorrow")
[]
```
## Model performances
```
precision: 0.914157
recall: 0.788824
f1: 0.846878
```
|
Jerr/model_name | 2021-02-15T18:51:21.000Z | []
| [
".gitattributes"
]
| Jerr | 0 | |||
Jerry/bert-analysis | 2021-03-07T03:18:41.000Z | []
| [
".gitattributes"
]
| Jerry | 0 | |||
JerryQu/v2-distilgpt2 | 2021-05-21T10:52:22.000Z | [
"pytorch",
"jax",
"gpt2",
"lm-head",
"causal-lm",
"transformers",
"text-generation"
]
| text-generation | [
".gitattributes",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin"
]
| JerryQu | 10 | transformers |
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