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Omartificial-Intelligence-Space
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AraEuroBert-2.1B

Feature Extraction
sentence-transformers
Safetensors
Arabic
eurobert
sentence-similarity
Generated from Trainer
dataset_size:2280319
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Arabic
EuroBert
Semantic
custom_code
Eval Results
Model card Files Community
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  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +926 -3
  4. config.json +45 -0
  5. config_sentence_transformers.json +10 -0
  6. model-00001-of-00002.safetensors +3 -0
  7. model-00002-of-00002.safetensors +3 -0
  8. model.safetensors.index.json +297 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. special_tokens_map.json +30 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +2069 -0
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+ }
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:2280319
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+ - loss:MatryoshkaLoss
9
+ - loss:MultipleNegativesRankingLoss
10
+ base_model: EuroBERT/EuroBERT-2.1B
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+ widget:
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+ - source_sentence: امرأة شقراء تطل على مشهد (سياتل سبيس نيدل)
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+ sentences:
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+ - رجل يستمتع بمناظر جسر البوابة الذهبية
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+ - فتاة بالخارج تلعب في الثلج
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+ - شخص ما يأخذ في نظرة إبرة الفضاء.
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+ - source_sentence: سوق الشرق الأوسط
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+ sentences:
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+ - مسرح أمريكي
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+ - متجر في الشرق الأوسط
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+ - البالغون صغار
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+ - source_sentence: رجلين يتنافسان في ملابس فنون الدفاع عن النفس
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+ sentences:
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+ - هناك العديد من الناس الحاضرين.
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+ - الكلب الأبيض على الشاطئ
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+ - هناك شخص واحد فقط موجود.
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+ - source_sentence: مجموعة من الناس تمشي بجانب شاحنة.
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+ sentences:
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+ - الناس يقفون
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+ - بعض الناس بالخارج
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+ - بعض الرجال يقودون على الطريق
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+ - source_sentence: لاعبة كرة ناعمة ترمي الكرة إلى زميلتها في الفريق
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+ sentences:
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+ - شخصان يلعبان كرة البيسبول
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+ - الرجل ينظف
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+ - لاعبين لكرة البيسبول يجلسان على مقعد
37
+ pipeline_tag: sentence-similarity
38
+ library_name: sentence-transformers
39
+ metrics:
40
+ - pearson_cosine
41
+ - spearman_cosine
42
+ model-index:
43
+ - name: SentenceTransformer based on EuroBERT/EuroBERT-2.1B
44
+ results:
45
+ - task:
46
+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev 2304
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+ type: sts-dev-2304
51
+ metrics:
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+ - type: pearson_cosine
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+ value: 0.7268081506530346
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.7297515677640904
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+ name: Spearman Cosine
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+ - task:
59
+ type: semantic-similarity
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+ name: Semantic Similarity
61
+ dataset:
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+ name: sts dev 1152
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+ type: sts-dev-1152
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+ metrics:
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+ - type: pearson_cosine
66
+ value: 0.7267206088516627
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.72990680019899
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+ name: Spearman Cosine
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+ - task:
72
+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev 960
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+ type: sts-dev-960
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+ metrics:
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+ - type: pearson_cosine
79
+ value: 0.7262504495546784
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+ name: Pearson Cosine
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+ - type: spearman_cosine
82
+ value: 0.7297095834107545
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+ name: Spearman Cosine
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+ - task:
85
+ type: semantic-similarity
86
+ name: Semantic Similarity
87
+ dataset:
88
+ name: sts dev 580
89
+ type: sts-dev-580
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+ metrics:
91
+ - type: pearson_cosine
92
+ value: 0.7246438630418187
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.7285747858670478
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+ name: Spearman Cosine
97
+ ---
98
+
99
+ # SentenceTransformer based on EuroBERT/EuroBERT-2.1B
100
+
101
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [EuroBERT/EuroBERT-2.1B](https://huggingface.co/EuroBERT/EuroBERT-2.1B). It maps sentences & paragraphs to a 2304-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
102
+
103
+ ## Model Details
104
+
105
+ ### Model Description
106
+ - **Model Type:** Sentence Transformer
107
+ - **Base model:** [EuroBERT/EuroBERT-2.1B](https://huggingface.co/EuroBERT/EuroBERT-2.1B) <!-- at revision f68df59dc13afc5851253fea84db9bb463bd8483 -->
108
+ - **Maximum Sequence Length:** 8192 tokens
109
+ - **Output Dimensionality:** 2304 dimensions
110
+ - **Similarity Function:** Cosine Similarity
111
+ <!-- - **Training Dataset:** Unknown -->
112
+ <!-- - **Language:** Unknown -->
113
+ <!-- - **License:** Unknown -->
114
+
115
+ ### Model Sources
116
+
117
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
118
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
119
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
120
+
121
+ ### Full Model Architecture
122
+
123
+ ```
124
+ SentenceTransformer(
125
+ (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: EuroBertModel
126
+ (1): Pooling({'word_embedding_dimension': 2304, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
127
+ )
128
+ ```
129
+
130
+ ## Usage
131
+
132
+ ### Direct Usage (Sentence Transformers)
133
+
134
+ First install the Sentence Transformers library:
135
+
136
+ ```bash
137
+ pip install -U sentence-transformers
138
+ ```
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+
140
+ Then you can load this model and run inference.
141
+ ```python
142
+ from sentence_transformers import SentenceTransformer
143
+
144
+ # Download from the 🤗 Hub
145
+ model = SentenceTransformer("sentence_transformers_model_id")
146
+ # Run inference
147
+ sentences = [
148
+ 'لاعبة كرة ناعمة ترمي الكرة إلى زميلتها في الفريق',
149
+ 'شخصان يلعبان كرة البيسبول',
150
+ 'لاعبين لكرة البيسبول يجلسان على مقعد',
151
+ ]
152
+ embeddings = model.encode(sentences)
153
+ print(embeddings.shape)
154
+ # [3, 2304]
155
+
156
+ # Get the similarity scores for the embeddings
157
+ similarities = model.similarity(embeddings, embeddings)
158
+ print(similarities.shape)
159
+ # [3, 3]
160
+ ```
161
+
162
+ <!--
163
+ ### Direct Usage (Transformers)
164
+
165
+ <details><summary>Click to see the direct usage in Transformers</summary>
166
+
167
+ </details>
168
+ -->
169
+
170
+ <!--
171
+ ### Downstream Usage (Sentence Transformers)
172
+
173
+ You can finetune this model on your own dataset.
174
+
175
+ <details><summary>Click to expand</summary>
176
+
177
+ </details>
178
+ -->
179
+
180
+ <!--
181
+ ### Out-of-Scope Use
182
+
183
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
184
+ -->
185
+
186
+ ## Evaluation
187
+
188
+ ### Metrics
189
+
190
+ #### Semantic Similarity
191
+
192
+ * Datasets: `sts-dev-2304`, `sts-dev-1152`, `sts-dev-960` and `sts-dev-580`
193
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
194
+
195
+ | Metric | sts-dev-2304 | sts-dev-1152 | sts-dev-960 | sts-dev-580 |
196
+ |:--------------------|:-------------|:-------------|:------------|:------------|
197
+ | pearson_cosine | 0.7268 | 0.7267 | 0.7263 | 0.7246 |
198
+ | **spearman_cosine** | **0.7298** | **0.7299** | **0.7297** | **0.7286** |
199
+
200
+ <!--
201
+ ## Bias, Risks and Limitations
202
+
203
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
204
+ -->
205
+
206
+ <!--
207
+ ### Recommendations
208
+
209
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
210
+ -->
211
+
212
+ ## Training Details
213
+
214
+ ### Training Dataset
215
+
216
+ #### Unnamed Dataset
217
+
218
+
219
+ * Size: 2,280,319 training samples
220
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
221
+ * Approximate statistics based on the first 1000 samples:
222
+ | | anchor | positive | negative |
223
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
224
+ | type | string | string | string |
225
+ | details | <ul><li>min: 5 tokens</li><li>mean: 18.35 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.52 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.06 tokens</li><li>max: 55 tokens</li></ul> |
226
+ * Samples:
227
+ | anchor | positive | negative |
228
+ |:-------------------------------------------------------------------------------|:-----------------------------------------------------|:------------------------------------------------------------------------|
229
+ | <code>صبي صغير وفتاة صغيرة يركبان دراجتيهما على الرصيف مع عجلات مساعدة.</code> | <code>فتى وفتاة يتعلمون ركوب الدراجات</code> | <code>الصبي الصغير يصل إلى العصا من الفتاة وهو يدير سباق التتابع</code> |
230
+ | <code>كيف أتجنب التفكير في نفسي أكثر من اللازم؟</code> | <code>كيف يمكنني تجنب التفكير أكثر من اللازم؟</code> | <code>كيف أتطوّر قدرة التفكير؟</code> |
231
+ | <code>ما هو أفضل كتاب يقرأه مراهق؟</code> | <code>ما هو أفضل كتاب للمراهقين؟</code> | <code>ما هي الكتب التي يمكن للطلاب قراءتها؟</code> |
232
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
233
+ ```json
234
+ {
235
+ "loss": "MultipleNegativesRankingLoss",
236
+ "matryoshka_dims": [
237
+ 2304,
238
+ 1152,
239
+ 960,
240
+ 580
241
+ ],
242
+ "matryoshka_weights": [
243
+ 1,
244
+ 1,
245
+ 1,
246
+ 1
247
+ ],
248
+ "n_dims_per_step": -1
249
+ }
250
+ ```
251
+
252
+ ### Evaluation Dataset
253
+
254
+ #### Unnamed Dataset
255
+
256
+
257
+ * Size: 6,609 evaluation samples
258
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
259
+ * Approximate statistics based on the first 1000 samples:
260
+ | | anchor | positive | negative |
261
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
262
+ | type | string | string | string |
263
+ | details | <ul><li>min: 6 tokens</li><li>mean: 25.73 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.99 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.9 tokens</li><li>max: 38 tokens</li></ul> |
264
+ * Samples:
265
+ | anchor | positive | negative |
266
+ |:----------------------------------------------------------------------------------------------|:-----------------------------------|:-------------------------------------------|
267
+ | <code>هذه الجوقة الكنيسة تغني للجماهير وهم يغنون الأغاني السعيدة من الكتاب في الكنيسة.</code> | <code>الكنيسة مليئة بالغناء</code> | <code>جوقة تغني في مباراة بيسبول</code> |
268
+ | <code>امرأة ترتدي حجاب أخضر، وقميص أزرق وابتسامة كبيرة</code> | <code>المرأة سعيدة جداً</code> | <code>لقد تم إطلاق النار على المرأة</code> |
269
+ | <code>رجل عجوز يحمل طردًا يتصور أمام إعلان.</code> | <code>رجل يتصور أمام إعلان.</code> | <code>رجل يمشي بجانب إعلان</code> |
270
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
271
+ ```json
272
+ {
273
+ "loss": "MultipleNegativesRankingLoss",
274
+ "matryoshka_dims": [
275
+ 2304,
276
+ 1152,
277
+ 960,
278
+ 580
279
+ ],
280
+ "matryoshka_weights": [
281
+ 1,
282
+ 1,
283
+ 1,
284
+ 1
285
+ ],
286
+ "n_dims_per_step": -1
287
+ }
288
+ ```
289
+
290
+ ### Training Hyperparameters
291
+ #### Non-Default Hyperparameters
292
+
293
+ - `eval_strategy`: steps
294
+ - `weight_decay`: 0.1
295
+ - `adam_beta2`: 0.95
296
+ - `adam_epsilon`: 1e-05
297
+ - `num_train_epochs`: 1
298
+ - `warmup_ratio`: 0.1
299
+ - `fp16`: True
300
+ - `load_best_model_at_end`: True
301
+ - `batch_sampler`: no_duplicates
302
+
303
+ #### All Hyperparameters
304
+ <details><summary>Click to expand</summary>
305
+
306
+ - `overwrite_output_dir`: False
307
+ - `do_predict`: False
308
+ - `eval_strategy`: steps
309
+ - `prediction_loss_only`: True
310
+ - `per_device_train_batch_size`: 8
311
+ - `per_device_eval_batch_size`: 8
312
+ - `per_gpu_train_batch_size`: None
313
+ - `per_gpu_eval_batch_size`: None
314
+ - `gradient_accumulation_steps`: 1
315
+ - `eval_accumulation_steps`: None
316
+ - `torch_empty_cache_steps`: None
317
+ - `learning_rate`: 5e-05
318
+ - `weight_decay`: 0.1
319
+ - `adam_beta1`: 0.9
320
+ - `adam_beta2`: 0.95
321
+ - `adam_epsilon`: 1e-05
322
+ - `max_grad_norm`: 1.0
323
+ - `num_train_epochs`: 1
324
+ - `max_steps`: -1
325
+ - `lr_scheduler_type`: linear
326
+ - `lr_scheduler_kwargs`: {}
327
+ - `warmup_ratio`: 0.1
328
+ - `warmup_steps`: 0
329
+ - `log_level`: passive
330
+ - `log_level_replica`: warning
331
+ - `log_on_each_node`: True
332
+ - `logging_nan_inf_filter`: True
333
+ - `save_safetensors`: True
334
+ - `save_on_each_node`: False
335
+ - `save_only_model`: False
336
+ - `restore_callback_states_from_checkpoint`: False
337
+ - `no_cuda`: False
338
+ - `use_cpu`: False
339
+ - `use_mps_device`: False
340
+ - `seed`: 42
341
+ - `data_seed`: None
342
+ - `jit_mode_eval`: False
343
+ - `use_ipex`: False
344
+ - `bf16`: False
345
+ - `fp16`: True
346
+ - `fp16_opt_level`: O1
347
+ - `half_precision_backend`: auto
348
+ - `bf16_full_eval`: False
349
+ - `fp16_full_eval`: False
350
+ - `tf32`: None
351
+ - `local_rank`: 0
352
+ - `ddp_backend`: None
353
+ - `tpu_num_cores`: None
354
+ - `tpu_metrics_debug`: False
355
+ - `debug`: []
356
+ - `dataloader_drop_last`: False
357
+ - `dataloader_num_workers`: 0
358
+ - `dataloader_prefetch_factor`: None
359
+ - `past_index`: -1
360
+ - `disable_tqdm`: False
361
+ - `remove_unused_columns`: True
362
+ - `label_names`: None
363
+ - `load_best_model_at_end`: True
364
+ - `ignore_data_skip`: False
365
+ - `fsdp`: []
366
+ - `fsdp_min_num_params`: 0
367
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
368
+ - `fsdp_transformer_layer_cls_to_wrap`: None
369
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
370
+ - `deepspeed`: None
371
+ - `label_smoothing_factor`: 0.0
372
+ - `optim`: adamw_torch
373
+ - `optim_args`: None
374
+ - `adafactor`: False
375
+ - `group_by_length`: False
376
+ - `length_column_name`: length
377
+ - `ddp_find_unused_parameters`: None
378
+ - `ddp_bucket_cap_mb`: None
379
+ - `ddp_broadcast_buffers`: False
380
+ - `dataloader_pin_memory`: True
381
+ - `dataloader_persistent_workers`: False
382
+ - `skip_memory_metrics`: True
383
+ - `use_legacy_prediction_loop`: False
384
+ - `push_to_hub`: False
385
+ - `resume_from_checkpoint`: None
386
+ - `hub_model_id`: None
387
+ - `hub_strategy`: every_save
388
+ - `hub_private_repo`: None
389
+ - `hub_always_push`: False
390
+ - `gradient_checkpointing`: False
391
+ - `gradient_checkpointing_kwargs`: None
392
+ - `include_inputs_for_metrics`: False
393
+ - `include_for_metrics`: []
394
+ - `eval_do_concat_batches`: True
395
+ - `fp16_backend`: auto
396
+ - `push_to_hub_model_id`: None
397
+ - `push_to_hub_organization`: None
398
+ - `mp_parameters`:
399
+ - `auto_find_batch_size`: False
400
+ - `full_determinism`: False
401
+ - `torchdynamo`: None
402
+ - `ray_scope`: last
403
+ - `ddp_timeout`: 1800
404
+ - `torch_compile`: False
405
+ - `torch_compile_backend`: None
406
+ - `torch_compile_mode`: None
407
+ - `dispatch_batches`: None
408
+ - `split_batches`: None
409
+ - `include_tokens_per_second`: False
410
+ - `include_num_input_tokens_seen`: False
411
+ - `neftune_noise_alpha`: None
412
+ - `optim_target_modules`: None
413
+ - `batch_eval_metrics`: False
414
+ - `eval_on_start`: False
415
+ - `use_liger_kernel`: False
416
+ - `eval_use_gather_object`: False
417
+ - `average_tokens_across_devices`: False
418
+ - `prompts`: None
419
+ - `batch_sampler`: no_duplicates
420
+ - `multi_dataset_batch_sampler`: proportional
421
+
422
+ </details>
423
+
424
+ ### Training Logs
425
+ <details><summary>Click to expand</summary>
426
+
427
+ | Epoch | Step | Training Loss | Validation Loss | sts-dev-2304_spearman_cosine | sts-dev-1152_spearman_cosine | sts-dev-960_spearman_cosine | sts-dev-580_spearman_cosine |
428
+ |:----------:|:---------:|:-------------:|:---------------:|:----------------------------:|:----------------------------:|:---------------------------:|:---------------------------:|
429
+ | 0.0028 | 200 | 8.6672 | - | - | - | - | - |
430
+ | 0.0056 | 400 | 3.0256 | - | - | - | - | - |
431
+ | 0.0070 | 500 | - | 5.7010 | 0.7506 | 0.7465 | 0.7442 | 0.7402 |
432
+ | 0.0084 | 600 | 1.829 | - | - | - | - | - |
433
+ | 0.0112 | 800 | 1.448 | - | - | - | - | - |
434
+ | 0.0140 | 1000 | 1.3776 | 5.4530 | 0.7454 | 0.7449 | 0.7444 | 0.7409 |
435
+ | 0.0168 | 1200 | 1.4119 | - | - | - | - | - |
436
+ | 0.0196 | 1400 | 1.8278 | - | - | - | - | - |
437
+ | 0.0210 | 1500 | - | 4.3733 | 0.7749 | 0.7712 | 0.7696 | 0.7654 |
438
+ | 0.0225 | 1600 | 1.1536 | - | - | - | - | - |
439
+ | 0.0253 | 1800 | 1.1718 | - | - | - | - | - |
440
+ | 0.0281 | 2000 | 1.3609 | 4.3397 | 0.7931 | 0.7884 | 0.7877 | 0.7831 |
441
+ | 0.0309 | 2200 | 1.0669 | - | - | - | - | - |
442
+ | 0.0337 | 2400 | 1.1301 | - | - | - | - | - |
443
+ | 0.0351 | 2500 | - | 5.7124 | 0.7218 | 0.7237 | 0.7253 | 0.7243 |
444
+ | 0.0365 | 2600 | 1.2806 | - | - | - | - | - |
445
+ | 0.0393 | 2800 | 1.0904 | - | - | - | - | - |
446
+ | 0.0421 | 3000 | 1.0817 | 4.3673 | 0.7745 | 0.7727 | 0.7721 | 0.7709 |
447
+ | 0.0449 | 3200 | 1.2634 | - | - | - | - | - |
448
+ | 0.0477 | 3400 | 1.2321 | - | - | - | - | - |
449
+ | 0.0491 | 3500 | - | 4.2964 | 0.7719 | 0.7660 | 0.7652 | 0.7610 |
450
+ | 0.0505 | 3600 | 1.368 | - | - | - | - | - |
451
+ | 0.0533 | 3800 | 1.1745 | - | - | - | - | - |
452
+ | 0.0561 | 4000 | 1.2394 | 4.7314 | 0.7653 | 0.7653 | 0.7650 | 0.7638 |
453
+ | 0.0589 | 4200 | 1.1589 | - | - | - | - | - |
454
+ | 0.0617 | 4400 | 1.1747 | - | - | - | - | - |
455
+ | 0.0631 | 4500 | - | 5.7287 | 0.7312 | 0.7286 | 0.7277 | 0.7238 |
456
+ | 0.0646 | 4600 | 1.2614 | - | - | - | - | - |
457
+ | 0.0674 | 4800 | 1.1362 | - | - | - | - | - |
458
+ | 0.0702 | 5000 | 1.3345 | 5.1328 | 0.7250 | 0.7165 | 0.7166 | 0.7111 |
459
+ | 0.0730 | 5200 | 1.3154 | - | - | - | - | - |
460
+ | 0.0758 | 5400 | 1.5714 | - | - | - | - | - |
461
+ | 0.0772 | 5500 | - | 5.3582 | 0.7288 | 0.7327 | 0.7339 | 0.7309 |
462
+ | 0.0786 | 5600 | 1.2832 | - | - | - | - | - |
463
+ | 0.0814 | 5800 | 1.3681 | - | - | - | - | - |
464
+ | 0.0842 | 6000 | 1.3606 | 5.8801 | 0.7272 | 0.7211 | 0.7206 | 0.7158 |
465
+ | 0.0870 | 6200 | 2.4398 | - | - | - | - | - |
466
+ | 0.0898 | 6400 | 2.0559 | - | - | - | - | - |
467
+ | 0.0912 | 6500 | - | 5.9716 | 0.7353 | 0.7269 | 0.7244 | 0.7162 |
468
+ | 0.0926 | 6600 | 1.5146 | - | - | - | - | - |
469
+ | 0.0954 | 6800 | 1.6127 | - | - | - | - | - |
470
+ | 0.0982 | 7000 | 1.3651 | 6.5403 | 0.7050 | 0.7003 | 0.6983 | 0.6934 |
471
+ | 0.1010 | 7200 | 1.4202 | - | - | - | - | - |
472
+ | 0.1038 | 7400 | 1.5096 | - | - | - | - | - |
473
+ | 0.1052 | 7500 | - | 6.3858 | 0.7188 | 0.7133 | 0.7130 | 0.7100 |
474
+ | 0.1067 | 7600 | 1.5439 | - | - | - | - | - |
475
+ | 0.1095 | 7800 | 1.4804 | - | - | - | - | - |
476
+ | 0.1123 | 8000 | 1.4775 | 8.8635 | 0.6288 | 0.6226 | 0.6200 | 0.6166 |
477
+ | 0.1151 | 8200 | 1.5338 | - | - | - | - | - |
478
+ | 0.1179 | 8400 | 1.3936 | - | - | - | - | - |
479
+ | 0.1193 | 8500 | - | 6.2709 | 0.7195 | 0.7151 | 0.7144 | 0.7134 |
480
+ | 0.1207 | 8600 | 1.2914 | - | - | - | - | - |
481
+ | 0.1235 | 8800 | 1.216 | - | - | - | - | - |
482
+ | 0.1263 | 9000 | 1.4346 | 6.2630 | 0.7311 | 0.7273 | 0.7265 | 0.7257 |
483
+ | 0.1291 | 9200 | 1.3064 | - | - | - | - | - |
484
+ | 0.1319 | 9400 | 1.3259 | - | - | - | - | - |
485
+ | 0.1333 | 9500 | - | 5.5211 | 0.7480 | 0.7432 | 0.7423 | 0.7410 |
486
+ | 0.1347 | 9600 | 1.21 | - | - | - | - | - |
487
+ | 0.1375 | 9800 | 1.3016 | - | - | - | - | - |
488
+ | 0.1403 | 10000 | 1.2321 | 6.0965 | 0.7145 | 0.7078 | 0.7060 | 0.7033 |
489
+ | 0.1431 | 10200 | 1.1932 | - | - | - | - | - |
490
+ | 0.1459 | 10400 | 1.2179 | - | - | - | - | - |
491
+ | 0.1473 | 10500 | - | 5.5334 | 0.7434 | 0.7391 | 0.7377 | 0.7373 |
492
+ | 0.1488 | 10600 | 1.1615 | - | - | - | - | - |
493
+ | 0.1516 | 10800 | 1.1436 | - | - | - | - | - |
494
+ | 0.1544 | 11000 | 1.1566 | 5.5874 | 0.7466 | 0.7411 | 0.7409 | 0.7381 |
495
+ | 0.1572 | 11200 | 1.0792 | - | - | - | - | - |
496
+ | 0.1600 | 11400 | 1.0939 | - | - | - | - | - |
497
+ | 0.1614 | 11500 | - | 6.5490 | 0.6879 | 0.6937 | 0.6944 | 0.6928 |
498
+ | 0.1628 | 11600 | 1.1303 | - | - | - | - | - |
499
+ | 0.1656 | 11800 | 1.1054 | - | - | - | - | - |
500
+ | 0.1684 | 12000 | 0.9837 | 5.1535 | 0.7471 | 0.7458 | 0.7444 | 0.7410 |
501
+ | 0.1712 | 12200 | 1.0494 | - | - | - | - | - |
502
+ | 0.1740 | 12400 | 1.079 | - | - | - | - | - |
503
+ | 0.1754 | 12500 | - | 5.0645 | 0.7474 | 0.7430 | 0.7429 | 0.7434 |
504
+ | 0.1768 | 12600 | 1.0539 | - | - | - | - | - |
505
+ | 0.1796 | 12800 | 1.0047 | - | - | - | - | - |
506
+ | 0.1824 | 13000 | 0.9906 | 5.6798 | 0.7294 | 0.7266 | 0.7261 | 0.7225 |
507
+ | 0.1852 | 13200 | 1.0253 | - | - | - | - | - |
508
+ | 0.1880 | 13400 | 1.0636 | - | - | - | - | - |
509
+ | 0.1894 | 13500 | - | 5.3916 | 0.7445 | 0.7478 | 0.7477 | 0.7467 |
510
+ | 0.1909 | 13600 | 0.9638 | - | - | - | - | - |
511
+ | 0.1937 | 13800 | 1.0367 | - | - | - | - | - |
512
+ | 0.1965 | 14000 | 0.9925 | 5.3012 | 0.7420 | 0.7406 | 0.7403 | 0.7392 |
513
+ | 0.1993 | 14200 | 0.8927 | - | - | - | - | - |
514
+ | 0.2021 | 14400 | 0.889 | - | - | - | - | - |
515
+ | 0.2035 | 14500 | - | 5.7766 | 0.7343 | 0.7339 | 0.7344 | 0.7334 |
516
+ | 0.2049 | 14600 | 0.9546 | - | - | - | - | - |
517
+ | 0.2077 | 14800 | 0.9425 | - | - | - | - | - |
518
+ | 0.2105 | 15000 | 0.9252 | 5.2777 | 0.7453 | 0.7433 | 0.7429 | 0.7417 |
519
+ | 0.2133 | 15200 | 0.8891 | - | - | - | - | - |
520
+ | 0.2161 | 15400 | 0.8424 | - | - | - | - | - |
521
+ | 0.2175 | 15500 | - | 5.3595 | 0.7448 | 0.7388 | 0.7378 | 0.7369 |
522
+ | 0.2189 | 15600 | 0.9341 | - | - | - | - | - |
523
+ | 0.2217 | 15800 | 0.8581 | - | - | - | - | - |
524
+ | 0.2245 | 16000 | 0.9067 | 5.1673 | 0.7503 | 0.7473 | 0.7469 | 0.7455 |
525
+ | 0.2273 | 16200 | 0.8753 | - | - | - | - | - |
526
+ | 0.2301 | 16400 | 0.8869 | - | - | - | - | - |
527
+ | 0.2315 | 16500 | - | 4.9738 | 0.7581 | 0.7605 | 0.7604 | 0.7592 |
528
+ | 0.2329 | 16600 | 0.8676 | - | - | - | - | - |
529
+ | 0.2358 | 16800 | 0.9172 | - | - | - | - | - |
530
+ | 0.2386 | 17000 | 0.8285 | 5.0234 | 0.7506 | 0.7524 | 0.7516 | 0.7507 |
531
+ | 0.2414 | 17200 | 0.8981 | - | - | - | - | - |
532
+ | 0.2442 | 17400 | 0.8483 | - | - | - | - | - |
533
+ | 0.2456 | 17500 | - | 5.1658 | 0.7417 | 0.7387 | 0.7380 | 0.7372 |
534
+ | 0.2470 | 17600 | 0.8006 | - | - | - | - | - |
535
+ | 0.2498 | 17800 | 0.862 | - | - | - | - | - |
536
+ | 0.2526 | 18000 | 0.8282 | 4.7983 | 0.7651 | 0.7627 | 0.7623 | 0.7604 |
537
+ | 0.2554 | 18200 | 0.7603 | - | - | - | - | - |
538
+ | 0.2582 | 18400 | 0.8196 | - | - | - | - | - |
539
+ | 0.2596 | 18500 | - | 5.2434 | 0.7545 | 0.7534 | 0.7536 | 0.7536 |
540
+ | 0.2610 | 18600 | 0.7364 | - | - | - | - | - |
541
+ | 0.2638 | 18800 | 0.804 | - | - | - | - | - |
542
+ | 0.2666 | 19000 | 0.83 | 5.8281 | 0.7240 | 0.7538 | 0.7589 | 0.7610 |
543
+ | 0.2694 | 19200 | 0.8331 | - | - | - | - | - |
544
+ | 0.2722 | 19400 | 0.7517 | - | - | - | - | - |
545
+ | 0.2736 | 19500 | - | 4.8993 | 0.7560 | 0.7525 | 0.7516 | 0.7497 |
546
+ | 0.2750 | 19600 | 0.7884 | - | - | - | - | - |
547
+ | 0.2779 | 19800 | 0.7378 | - | - | - | - | - |
548
+ | 0.2807 | 20000 | 0.7655 | 4.8857 | 0.7506 | 0.7480 | 0.7471 | 0.7446 |
549
+ | 0.2835 | 20200 | 0.726 | - | - | - | - | - |
550
+ | 0.2863 | 20400 | 0.792 | - | - | - | - | - |
551
+ | 0.2877 | 20500 | - | 4.7750 | 0.7642 | 0.7604 | 0.7594 | 0.7570 |
552
+ | 0.2891 | 20600 | 0.7392 | - | - | - | - | - |
553
+ | 0.2919 | 20800 | 0.7082 | - | - | - | - | - |
554
+ | 0.2947 | 21000 | 0.7135 | 4.8135 | 0.7750 | 0.7718 | 0.7710 | 0.7692 |
555
+ | 0.2975 | 21200 | 0.7151 | - | - | - | - | - |
556
+ | 0.3003 | 21400 | 0.7756 | - | - | - | - | - |
557
+ | 0.3017 | 21500 | - | 4.7934 | 0.7658 | 0.7631 | 0.7622 | 0.7603 |
558
+ | 0.3031 | 21600 | 0.733 | - | - | - | - | - |
559
+ | 0.3059 | 21800 | 0.716 | - | - | - | - | - |
560
+ | 0.3087 | 22000 | 0.6908 | 4.7519 | 0.7696 | 0.7692 | 0.7689 | 0.7688 |
561
+ | 0.3115 | 22200 | 0.7475 | - | - | - | - | - |
562
+ | 0.3143 | 22400 | 0.6582 | - | - | - | - | - |
563
+ | 0.3157 | 22500 | - | 4.6717 | 0.7597 | 0.7592 | 0.7591 | 0.7584 |
564
+ | 0.3171 | 22600 | 0.6647 | - | - | - | - | - |
565
+ | 0.3200 | 22800 | 0.662 | - | - | - | - | - |
566
+ | 0.3228 | 23000 | 0.6804 | 4.6219 | 0.7567 | 0.7577 | 0.7576 | 0.7573 |
567
+ | 0.3256 | 23200 | 0.6446 | - | - | - | - | - |
568
+ | 0.3284 | 23400 | 0.6511 | - | - | - | - | - |
569
+ | 0.3298 | 23500 | - | 4.5629 | 0.7702 | 0.7679 | 0.7671 | 0.7657 |
570
+ | 0.3312 | 23600 | 0.6372 | - | - | - | - | - |
571
+ | 0.3340 | 23800 | 0.674 | - | - | - | - | - |
572
+ | 0.3368 | 24000 | 0.6632 | 4.7420 | 0.7612 | 0.7596 | 0.7594 | 0.7580 |
573
+ | 0.3396 | 24200 | 0.6613 | - | - | - | - | - |
574
+ | 0.3424 | 24400 | 0.6365 | - | - | - | - | - |
575
+ | 0.3438 | 24500 | - | 4.8394 | 0.7637 | 0.7624 | 0.7622 | 0.7605 |
576
+ | 0.3452 | 24600 | 0.6333 | - | - | - | - | - |
577
+ | 0.3480 | 24800 | 0.6445 | - | - | - | - | - |
578
+ | 0.3508 | 25000 | 0.6128 | 4.6870 | 0.7575 | 0.7546 | 0.7538 | 0.7519 |
579
+ | 0.3536 | 25200 | 0.5989 | - | - | - | - | - |
580
+ | 0.3564 | 25400 | 0.6503 | - | - | - | - | - |
581
+ | 0.3578 | 25500 | - | 4.6317 | 0.7655 | 0.7671 | 0.7669 | 0.7645 |
582
+ | 0.3592 | 25600 | 0.6488 | - | - | - | - | - |
583
+ | 0.3621 | 25800 | 0.6799 | - | - | - | - | - |
584
+ | 0.3649 | 26000 | 0.5918 | 4.6720 | 0.7667 | 0.7667 | 0.7674 | 0.7658 |
585
+ | 0.3677 | 26200 | 0.5804 | - | - | - | - | - |
586
+ | 0.3705 | 26400 | 0.6285 | - | - | - | - | - |
587
+ | 0.3719 | 26500 | - | 4.7815 | 0.7603 | 0.7628 | 0.7626 | 0.7618 |
588
+ | 0.3733 | 26600 | 0.6277 | - | - | - | - | - |
589
+ | 0.3761 | 26800 | 0.6436 | - | - | - | - | - |
590
+ | 0.3789 | 27000 | 0.5994 | 4.7073 | 0.7690 | 0.7678 | 0.7678 | 0.7658 |
591
+ | 0.3817 | 27200 | 0.6306 | - | - | - | - | - |
592
+ | 0.3845 | 27400 | 0.5807 | - | - | - | - | - |
593
+ | 0.3859 | 27500 | - | 4.4573 | 0.7745 | 0.7718 | 0.7718 | 0.7701 |
594
+ | 0.3873 | 27600 | 0.6009 | - | - | - | - | - |
595
+ | 0.3901 | 27800 | 0.6097 | - | - | - | - | - |
596
+ | 0.3929 | 28000 | 0.5864 | 4.6003 | 0.7744 | 0.7715 | 0.7715 | 0.7704 |
597
+ | 0.3957 | 28200 | 0.6073 | - | - | - | - | - |
598
+ | 0.3985 | 28400 | 0.5602 | - | - | - | - | - |
599
+ | 0.3999 | 28500 | - | 4.5174 | 0.7708 | 0.7678 | 0.7677 | 0.7663 |
600
+ | 0.4013 | 28600 | 0.5685 | - | - | - | - | - |
601
+ | 0.4042 | 28800 | 0.5218 | - | - | - | - | - |
602
+ | 0.4070 | 29000 | 0.5791 | 4.5126 | 0.7766 | 0.7754 | 0.7759 | 0.7743 |
603
+ | 0.4098 | 29200 | 0.5396 | - | - | - | - | - |
604
+ | 0.4126 | 29400 | 0.5527 | - | - | - | - | - |
605
+ | 0.4140 | 29500 | - | 4.5247 | 0.7770 | 0.7765 | 0.7769 | 0.7755 |
606
+ | 0.4154 | 29600 | 0.5597 | - | - | - | - | - |
607
+ | 0.4182 | 29800 | 0.5272 | - | - | - | - | - |
608
+ | 0.4210 | 30000 | 0.5222 | 4.6327 | 0.7739 | 0.7724 | 0.7728 | 0.7716 |
609
+ | 0.4238 | 30200 | 0.5604 | - | - | - | - | - |
610
+ | 0.4266 | 30400 | 0.5073 | - | - | - | - | - |
611
+ | 0.4280 | 30500 | - | 4.5656 | 0.7600 | 0.7619 | 0.7627 | 0.7607 |
612
+ | 0.4294 | 30600 | 0.5505 | - | - | - | - | - |
613
+ | 0.4322 | 30800 | 0.5098 | - | - | - | - | - |
614
+ | 0.4350 | 31000 | 0.5444 | 4.3964 | 0.7756 | 0.7755 | 0.7764 | 0.7749 |
615
+ | 0.4378 | 31200 | 0.5175 | - | - | - | - | - |
616
+ | 0.4406 | 31400 | 0.4972 | - | - | - | - | - |
617
+ | 0.4420 | 31500 | - | 4.5159 | 0.7792 | 0.7794 | 0.7794 | 0.7781 |
618
+ | 0.4434 | 31600 | 0.561 | - | - | - | - | - |
619
+ | 0.4463 | 31800 | 1.7794 | - | - | - | - | - |
620
+ | 0.4491 | 32000 | 2.1237 | 3.7693 | 0.7732 | 0.7758 | 0.7759 | 0.7756 |
621
+ | 0.4519 | 32200 | 2.0415 | - | - | - | - | - |
622
+ | 0.4547 | 32400 | 2.0031 | - | - | - | - | - |
623
+ | 0.4561 | 32500 | - | 3.5650 | 0.7601 | 0.7657 | 0.7659 | 0.7645 |
624
+ | 0.4575 | 32600 | 1.93 | - | - | - | - | - |
625
+ | 0.4603 | 32800 | 1.8959 | - | - | - | - | - |
626
+ | 0.4631 | 33000 | 1.8395 | 3.5279 | 0.7855 | 0.7827 | 0.7826 | 0.7812 |
627
+ | 0.4659 | 33200 | 1.8249 | - | - | - | - | - |
628
+ | 0.4687 | 33400 | 1.7914 | - | - | - | - | - |
629
+ | 0.4701 | 33500 | - | 3.5319 | 0.7808 | 0.7782 | 0.7782 | 0.7755 |
630
+ | 0.4715 | 33600 | 1.7951 | - | - | - | - | - |
631
+ | 0.4743 | 33800 | 1.8376 | - | - | - | - | - |
632
+ | 0.4771 | 34000 | 1.7843 | 3.3522 | 0.7869 | 0.7855 | 0.7853 | 0.7837 |
633
+ | 0.4799 | 34200 | 1.7331 | - | - | - | - | - |
634
+ | 0.4827 | 34400 | 1.6813 | - | - | - | - | - |
635
+ | 0.4841 | 34500 | - | 3.3318 | 0.7940 | 0.7932 | 0.7928 | 0.7917 |
636
+ | 0.4855 | 34600 | 1.6846 | - | - | - | - | - |
637
+ | 0.4884 | 34800 | 1.7417 | - | - | - | - | - |
638
+ | 0.4912 | 35000 | 1.6533 | 3.1987 | 0.7881 | 0.7868 | 0.7867 | 0.7853 |
639
+ | 0.4940 | 35200 | 1.5908 | - | - | - | - | - |
640
+ | 0.4968 | 35400 | 1.6191 | - | - | - | - | - |
641
+ | 0.4982 | 35500 | - | 3.1554 | 0.7813 | 0.7797 | 0.7798 | 0.7783 |
642
+ | 0.4996 | 35600 | 1.64 | - | - | - | - | - |
643
+ | 0.5024 | 35800 | 1.5388 | - | - | - | - | - |
644
+ | 0.5052 | 36000 | 1.5755 | 3.2367 | 0.7983 | 0.7975 | 0.7972 | 0.7962 |
645
+ | 0.5080 | 36200 | 1.5129 | - | - | - | - | - |
646
+ | 0.5108 | 36400 | 1.4919 | - | - | - | - | - |
647
+ | 0.5122 | 36500 | - | 3.0538 | 0.7905 | 0.7901 | 0.7896 | 0.7880 |
648
+ | 0.5136 | 36600 | 1.5285 | - | - | - | - | - |
649
+ | 0.5164 | 36800 | 1.5921 | - | - | - | - | - |
650
+ | 0.5192 | 37000 | 1.5392 | 3.0423 | 0.7953 | 0.7945 | 0.7943 | 0.7926 |
651
+ | 0.5220 | 37200 | 1.5065 | - | - | - | - | - |
652
+ | 0.5248 | 37400 | 1.4468 | - | - | - | - | - |
653
+ | 0.5262 | 37500 | - | 2.9644 | 0.7954 | 0.7939 | 0.7940 | 0.7924 |
654
+ | 0.5276 | 37600 | 1.4902 | - | - | - | - | - |
655
+ | 0.5305 | 37800 | 1.5384 | - | - | - | - | - |
656
+ | 0.5333 | 38000 | 1.445 | 3.0091 | 0.7901 | 0.7884 | 0.7882 | 0.7864 |
657
+ | 0.5361 | 38200 | 1.4131 | - | - | - | - | - |
658
+ | 0.5389 | 38400 | 1.4992 | - | - | - | - | - |
659
+ | 0.5403 | 38500 | - | 3.0077 | 0.8001 | 0.7984 | 0.7981 | 0.7965 |
660
+ | 0.5417 | 38600 | 1.4512 | - | - | - | - | - |
661
+ | 0.5445 | 38800 | 1.5067 | - | - | - | - | - |
662
+ | 0.5473 | 39000 | 1.4276 | 2.9891 | 0.7913 | 0.7891 | 0.7888 | 0.7874 |
663
+ | 0.5501 | 39200 | 1.489 | - | - | - | - | - |
664
+ | 0.5529 | 39400 | 1.3922 | - | - | - | - | - |
665
+ | 0.5543 | 39500 | - | 2.9370 | 0.7872 | 0.7881 | 0.7878 | 0.7867 |
666
+ | 0.5557 | 39600 | 1.4333 | - | - | - | - | - |
667
+ | 0.5585 | 39800 | 1.4339 | - | - | - | - | - |
668
+ | 0.5613 | 40000 | 1.3992 | 2.8982 | 0.7920 | 0.7904 | 0.7902 | 0.7887 |
669
+ | 0.5641 | 40200 | 1.3983 | - | - | - | - | - |
670
+ | 0.5669 | 40400 | 1.4295 | - | - | - | - | - |
671
+ | 0.5683 | 40500 | - | 2.9107 | 0.7959 | 0.7933 | 0.7925 | 0.7910 |
672
+ | 0.5697 | 40600 | 1.3922 | - | - | - | - | - |
673
+ | 0.5726 | 40800 | 1.3259 | - | - | - | - | - |
674
+ | 0.5754 | 41000 | 1.3746 | 2.8836 | 0.7931 | 0.7905 | 0.7900 | 0.7885 |
675
+ | 0.5782 | 41200 | 1.3153 | - | - | - | - | - |
676
+ | 0.5810 | 41400 | 1.3919 | - | - | - | - | - |
677
+ | 0.5824 | 41500 | - | 2.7995 | 0.7961 | 0.7943 | 0.7935 | 0.7917 |
678
+ | 0.5838 | 41600 | 1.3561 | - | - | - | - | - |
679
+ | 0.5866 | 41800 | 1.3234 | - | - | - | - | - |
680
+ | 0.5894 | 42000 | 1.4133 | 2.8020 | 0.7913 | 0.7914 | 0.7908 | 0.7887 |
681
+ | 0.5922 | 42200 | 1.3389 | - | - | - | - | - |
682
+ | 0.5950 | 42400 | 1.3156 | - | - | - | - | - |
683
+ | 0.5964 | 42500 | - | 2.7450 | 0.7979 | 0.7966 | 0.7960 | 0.7947 |
684
+ | 0.5978 | 42600 | 1.3181 | - | - | - | - | - |
685
+ | 0.6006 | 42800 | 1.2783 | - | - | - | - | - |
686
+ | 0.6034 | 43000 | 1.3009 | 2.8012 | 0.7877 | 0.7857 | 0.7852 | 0.7830 |
687
+ | 0.6062 | 43200 | 1.3252 | - | - | - | - | - |
688
+ | 0.6090 | 43400 | 1.3439 | - | - | - | - | - |
689
+ | 0.6104 | 43500 | - | 2.7135 | 0.7954 | 0.7936 | 0.7932 | 0.7914 |
690
+ | 0.6118 | 43600 | 1.3808 | - | - | - | - | - |
691
+ | 0.6147 | 43800 | 1.235 | - | - | - | - | - |
692
+ | 0.6175 | 44000 | 1.2864 | 2.7114 | 0.7934 | 0.7914 | 0.7904 | 0.7887 |
693
+ | 0.6203 | 44200 | 1.2656 | - | - | - | - | - |
694
+ | 0.6231 | 44400 | 1.2762 | - | - | - | - | - |
695
+ | 0.6245 | 44500 | - | 2.7026 | 0.7913 | 0.7924 | 0.7922 | 0.7906 |
696
+ | 0.6259 | 44600 | 1.32 | - | - | - | - | - |
697
+ | 0.6287 | 44800 | 1.3085 | - | - | - | - | - |
698
+ | 0.6315 | 45000 | 1.212 | 2.6961 | 0.7936 | 0.7951 | 0.7948 | 0.7939 |
699
+ | 0.6343 | 45200 | 1.3381 | - | - | - | - | - |
700
+ | 0.6371 | 45400 | 1.1723 | - | - | - | - | - |
701
+ | 0.6385 | 45500 | - | 2.6414 | 0.8027 | 0.8024 | 0.8022 | 0.8012 |
702
+ | 0.6399 | 45600 | 1.2188 | - | - | - | - | - |
703
+ | 0.6427 | 45800 | 1.2384 | - | - | - | - | - |
704
+ | 0.6455 | 46000 | 1.2436 | 2.6409 | 0.7989 | 0.7966 | 0.7959 | 0.7937 |
705
+ | 0.6483 | 46200 | 1.2392 | - | - | - | - | - |
706
+ | 0.6511 | 46400 | 1.1917 | - | - | - | - | - |
707
+ | 0.6525 | 46500 | - | 2.6848 | 0.7986 | 0.7986 | 0.7982 | 0.7967 |
708
+ | 0.6539 | 46600 | 1.1568 | - | - | - | - | - |
709
+ | 0.6567 | 46800 | 1.1815 | - | - | - | - | - |
710
+ | 0.6596 | 47000 | 1.2146 | 2.6849 | 0.7952 | 0.7972 | 0.7973 | 0.7966 |
711
+ | 0.6624 | 47200 | 1.186 | - | - | - | - | - |
712
+ | 0.6652 | 47400 | 1.2519 | - | - | - | - | - |
713
+ | 0.6666 | 47500 | - | 2.6569 | 0.7973 | 0.7967 | 0.7960 | 0.7952 |
714
+ | 0.6680 | 47600 | 1.1762 | - | - | - | - | - |
715
+ | 0.6708 | 47800 | 1.1631 | - | - | - | - | - |
716
+ | 0.6736 | 48000 | 1.1893 | 2.5887 | 0.7868 | 0.7863 | 0.7856 | 0.7853 |
717
+ | 0.6764 | 48200 | 1.184 | - | - | - | - | - |
718
+ | 0.6792 | 48400 | 1.1334 | - | - | - | - | - |
719
+ | 0.6806 | 48500 | - | 2.6003 | 0.7924 | 0.7928 | 0.7923 | 0.7916 |
720
+ | 0.6820 | 48600 | 1.1908 | - | - | - | - | - |
721
+ | 0.6848 | 48800 | 1.044 | - | - | - | - | - |
722
+ | 0.6876 | 49000 | 1.2168 | 2.5521 | 0.7981 | 0.7987 | 0.7980 | 0.7976 |
723
+ | 0.6904 | 49200 | 1.1499 | - | - | - | - | - |
724
+ | 0.6932 | 49400 | 1.1207 | - | - | - | - | - |
725
+ | 0.6946 | 49500 | - | 2.5369 | 0.8007 | 0.8000 | 0.7995 | 0.7986 |
726
+ | 0.6960 | 49600 | 1.0915 | - | - | - | - | - |
727
+ | 0.6988 | 49800 | 1.1167 | - | - | - | - | - |
728
+ | 0.7017 | 50000 | 1.0985 | 2.5389 | 0.8015 | 0.8005 | 0.7999 | 0.7991 |
729
+ | 0.7045 | 50200 | 1.0896 | - | - | - | - | - |
730
+ | 0.7073 | 50400 | 1.0531 | - | - | - | - | - |
731
+ | 0.7087 | 50500 | - | 2.5144 | 0.8015 | 0.8010 | 0.8005 | 0.7999 |
732
+ | 0.7101 | 50600 | 1.1239 | - | - | - | - | - |
733
+ | 0.7129 | 50800 | 1.1217 | - | - | - | - | - |
734
+ | 0.7157 | 51000 | 1.103 | 2.5027 | 0.7991 | 0.7997 | 0.7992 | 0.7986 |
735
+ | 0.7185 | 51200 | 1.0682 | - | - | - | - | - |
736
+ | 0.7213 | 51400 | 1.0217 | - | - | - | - | - |
737
+ | 0.7227 | 51500 | - | 2.5182 | 0.8082 | 0.8071 | 0.8062 | 0.8054 |
738
+ | 0.7241 | 51600 | 1.0847 | - | - | - | - | - |
739
+ | 0.7269 | 51800 | 1.0591 | - | - | - | - | - |
740
+ | 0.7297 | 52000 | 1.0355 | 2.5327 | 0.7934 | 0.7928 | 0.7923 | 0.7916 |
741
+ | 0.7325 | 52200 | 1.0651 | - | - | - | - | - |
742
+ | 0.7353 | 52400 | 1.0746 | - | - | - | - | - |
743
+ | 0.7367 | 52500 | - | 2.4470 | 0.7960 | 0.7946 | 0.7940 | 0.7927 |
744
+ | 0.7381 | 52600 | 1.0487 | - | - | - | - | - |
745
+ | 0.7409 | 52800 | 1.0092 | - | - | - | - | - |
746
+ | **0.7438** | **53000** | **1.0229** | **2.4314** | **0.8055** | **0.8045** | **0.8042** | **0.8035** |
747
+ | 0.7466 | 53200 | 1.0878 | - | - | - | - | - |
748
+ | 0.7494 | 53400 | 1.0188 | - | - | - | - | - |
749
+ | 0.7508 | 53500 | - | 2.4194 | 0.7965 | 0.7955 | 0.7947 | 0.7936 |
750
+ | 0.7522 | 53600 | 1.1071 | - | - | - | - | - |
751
+ | 0.7550 | 53800 | 0.9727 | - | - | - | - | - |
752
+ | 0.7578 | 54000 | 1.0945 | 2.4324 | 0.8043 | 0.8034 | 0.8029 | 0.8015 |
753
+ | 0.7606 | 54200 | 0.9613 | - | - | - | - | - |
754
+ | 0.7634 | 54400 | 0.97 | - | - | - | - | - |
755
+ | 0.7648 | 54500 | - | 2.4932 | 0.7996 | 0.7977 | 0.7974 | 0.7960 |
756
+ | 0.7662 | 54600 | 1.0016 | - | - | - | - | - |
757
+ | 0.7690 | 54800 | 0.9434 | - | - | - | - | - |
758
+ | 0.7718 | 55000 | 0.9496 | 2.4044 | 0.8049 | 0.8040 | 0.8034 | 0.8020 |
759
+ | 0.7746 | 55200 | 1.0111 | - | - | - | - | - |
760
+ | 0.7774 | 55400 | 1.0368 | - | - | - | - | - |
761
+ | 0.7788 | 55500 | - | 2.4003 | 0.8053 | 0.8045 | 0.8042 | 0.8029 |
762
+ | 0.7802 | 55600 | 0.996 | - | - | - | - | - |
763
+ | 0.7830 | 55800 | 1.0245 | - | - | - | - | - |
764
+ | 0.7859 | 56000 | 1.0316 | 2.4212 | 0.8001 | 0.8000 | 0.7996 | 0.7989 |
765
+ | 0.7887 | 56200 | 0.981 | - | - | - | - | - |
766
+ | 0.7915 | 56400 | 0.9407 | - | - | - | - | - |
767
+ | 0.7929 | 56500 | - | 2.4154 | 0.8026 | 0.8020 | 0.8016 | 0.8006 |
768
+ | 0.7943 | 56600 | 0.9691 | - | - | - | - | - |
769
+ | 0.7971 | 56800 | 0.9952 | - | - | - | - | - |
770
+ | 0.7999 | 57000 | 0.9495 | 2.3981 | 0.8005 | 0.8007 | 0.8006 | 0.8000 |
771
+ | 0.8027 | 57200 | 0.912 | - | - | - | - | - |
772
+ | 0.8055 | 57400 | 1.0533 | - | - | - | - | - |
773
+ | 0.8069 | 57500 | - | 2.3602 | 0.7987 | 0.7984 | 0.7982 | 0.7973 |
774
+ | 0.8083 | 57600 | 0.9728 | - | - | - | - | - |
775
+ | 0.8111 | 57800 | 0.9779 | - | - | - | - | - |
776
+ | 0.8139 | 58000 | 0.9316 | 2.3773 | 0.8043 | 0.8049 | 0.8047 | 0.8039 |
777
+ | 0.8167 | 58200 | 0.9249 | - | - | - | - | - |
778
+ | 0.8195 | 58400 | 3.0211 | - | - | - | - | - |
779
+ | 0.8209 | 58500 | - | 2.5352 | 0.7968 | 0.7943 | 0.7934 | 0.7920 |
780
+ | 0.8223 | 58600 | 0.8472 | - | - | - | - | - |
781
+ | 0.8251 | 58800 | 0.4561 | - | - | - | - | - |
782
+ | 0.8280 | 59000 | 0.3012 | 2.8672 | 0.7633 | 0.7613 | 0.7606 | 0.7600 |
783
+ | 0.8308 | 59200 | 0.2192 | - | - | - | - | - |
784
+ | 0.8336 | 59400 | 0.1796 | - | - | - | - | - |
785
+ | 0.8350 | 59500 | - | 3.2244 | 0.7432 | 0.7427 | 0.7421 | 0.7421 |
786
+ | 0.8364 | 59600 | 0.1348 | - | - | - | - | - |
787
+ | 0.8392 | 59800 | 0.1202 | - | - | - | - | - |
788
+ | 0.8420 | 60000 | 0.1124 | 3.1473 | 0.7401 | 0.7406 | 0.7402 | 0.7401 |
789
+ | 0.8448 | 60200 | 0.09 | - | - | - | - | - |
790
+ | 0.8476 | 60400 | 0.0823 | - | - | - | - | - |
791
+ | 0.8490 | 60500 | - | 3.2532 | 0.7289 | 0.7308 | 0.7304 | 0.7300 |
792
+ | 0.8504 | 60600 | 0.0675 | - | - | - | - | - |
793
+ | 0.8532 | 60800 | 0.0625 | - | - | - | - | - |
794
+ | 0.8560 | 61000 | 0.0646 | 3.4939 | 0.7217 | 0.7237 | 0.7234 | 0.7226 |
795
+ | 0.8588 | 61200 | 0.0635 | - | - | - | - | - |
796
+ | 0.8616 | 61400 | 0.0561 | - | - | - | - | - |
797
+ | 0.8630 | 61500 | - | 3.3607 | 0.7250 | 0.7257 | 0.7254 | 0.7243 |
798
+ | 0.8644 | 61600 | 0.0432 | - | - | - | - | - |
799
+ | 0.8672 | 61800 | 0.0481 | - | - | - | - | - |
800
+ | 0.8701 | 62000 | 0.0537 | 3.3917 | 0.7195 | 0.7215 | 0.7213 | 0.7207 |
801
+ | 0.8729 | 62200 | 0.0406 | - | - | - | - | - |
802
+ | 0.8757 | 62400 | 0.0358 | - | - | - | - | - |
803
+ | 0.8771 | 62500 | - | 3.4483 | 0.7221 | 0.7235 | 0.7232 | 0.7224 |
804
+ | 0.8785 | 62600 | 0.0349 | - | - | - | - | - |
805
+ | 0.8813 | 62800 | 0.0344 | - | - | - | - | - |
806
+ | 0.8841 | 63000 | 0.0305 | 3.4047 | 0.7142 | 0.7153 | 0.7150 | 0.7141 |
807
+ | 0.8869 | 63200 | 0.0291 | - | - | - | - | - |
808
+ | 0.8897 | 63400 | 0.0295 | - | - | - | - | - |
809
+ | 0.8911 | 63500 | - | 3.5096 | 0.7171 | 0.7169 | 0.7166 | 0.7162 |
810
+ | 0.8925 | 63600 | 0.0363 | - | - | - | - | - |
811
+ | 0.8953 | 63800 | 0.0185 | - | - | - | - | - |
812
+ | 0.8981 | 64000 | 0.0188 | 3.3848 | 0.7185 | 0.7184 | 0.7180 | 0.7172 |
813
+ | 0.9009 | 64200 | 0.027 | - | - | - | - | - |
814
+ | 0.9037 | 64400 | 0.0197 | - | - | - | - | - |
815
+ | 0.9051 | 64500 | - | 3.4922 | 0.7167 | 0.7164 | 0.7161 | 0.7150 |
816
+ | 0.9065 | 64600 | 0.0231 | - | - | - | - | - |
817
+ | 0.9093 | 64800 | 0.0189 | - | - | - | - | - |
818
+ | 0.9122 | 65000 | 0.0229 | 3.4452 | 0.7115 | 0.7114 | 0.7110 | 0.7102 |
819
+ | 0.9150 | 65200 | 0.0174 | - | - | - | - | - |
820
+ | 0.9178 | 65400 | 0.5224 | - | - | - | - | - |
821
+ | 0.9192 | 65500 | - | 3.5335 | 0.7288 | 0.7300 | 0.7296 | 0.7290 |
822
+ | 0.9206 | 65600 | 0.4617 | - | - | - | - | - |
823
+ | 0.9234 | 65800 | 0.4472 | - | - | - | - | - |
824
+ | 0.9262 | 66000 | 0.4843 | 3.5009 | 0.7282 | 0.7299 | 0.7295 | 0.7293 |
825
+ | 0.9290 | 66200 | 0.3962 | - | - | - | - | - |
826
+ | 0.9318 | 66400 | 0.3606 | - | - | - | - | - |
827
+ | 0.9332 | 66500 | - | 3.5724 | 0.7354 | 0.7373 | 0.7370 | 0.7365 |
828
+ | 0.9346 | 66600 | 0.3062 | - | - | - | - | - |
829
+ | 0.9374 | 66800 | 0.3025 | - | - | - | - | - |
830
+ | 0.9402 | 67000 | 0.3344 | 3.6107 | 0.7289 | 0.7303 | 0.7300 | 0.7292 |
831
+ | 0.9430 | 67200 | 0.2965 | - | - | - | - | - |
832
+ | 0.9458 | 67400 | 0.2627 | - | - | - | - | - |
833
+ | 0.9472 | 67500 | - | 3.5568 | 0.7304 | 0.7304 | 0.7302 | 0.7292 |
834
+ | 0.9486 | 67600 | 0.2691 | - | - | - | - | - |
835
+ | 0.9514 | 67800 | 0.2493 | - | - | - | - | - |
836
+ | 0.9543 | 68000 | 0.2687 | 3.6124 | 0.7319 | 0.7328 | 0.7324 | 0.7315 |
837
+ | 0.9571 | 68200 | 0.2383 | - | - | - | - | - |
838
+ | 0.9599 | 68400 | 0.231 | - | - | - | - | - |
839
+ | 0.9613 | 68500 | - | 3.4644 | 0.7321 | 0.7328 | 0.7327 | 0.7316 |
840
+ | 0.9627 | 68600 | 0.213 | - | - | - | - | - |
841
+ | 0.9655 | 68800 | 0.214 | - | - | - | - | - |
842
+ | 0.9683 | 69000 | 0.2428 | 3.6995 | 0.7323 | 0.7324 | 0.7322 | 0.7309 |
843
+ | 0.9711 | 69200 | 0.1836 | - | - | - | - | - |
844
+ | 0.9739 | 69400 | 0.1901 | - | - | - | - | - |
845
+ | 0.9753 | 69500 | - | 3.6661 | 0.7304 | 0.7307 | 0.7303 | 0.7290 |
846
+ | 0.9767 | 69600 | 0.2028 | - | - | - | - | - |
847
+ | 0.9795 | 69800 | 0.1753 | - | - | - | - | - |
848
+ | 0.9823 | 70000 | 0.207 | 3.5721 | 0.7279 | 0.7282 | 0.7280 | 0.7269 |
849
+ | 0.9851 | 70200 | 0.1945 | - | - | - | - | - |
850
+ | 0.9879 | 70400 | 0.1806 | - | - | - | - | - |
851
+ | 0.9893 | 70500 | - | 3.5963 | 0.7291 | 0.7293 | 0.7291 | 0.7279 |
852
+ | 0.9907 | 70600 | 0.1838 | - | - | - | - | - |
853
+ | 0.9935 | 70800 | 0.1938 | - | - | - | - | - |
854
+ | 0.9964 | 71000 | 0.1723 | 3.5996 | 0.7298 | 0.7299 | 0.7297 | 0.7286 |
855
+ | 0.9992 | 71200 | 0.1779 | - | - | - | - | - |
856
+
857
+ * The bold row denotes the saved checkpoint.
858
+ </details>
859
+
860
+ ### Framework Versions
861
+ - Python: 3.10.12
862
+ - Sentence Transformers: 3.3.1
863
+ - Transformers: 4.49.0
864
+ - PyTorch: 2.5.1+cu124
865
+ - Accelerate: 1.2.1
866
+ - Datasets: 2.21.0
867
+ - Tokenizers: 0.21.0
868
+
869
+ ## Citation
870
+
871
+ ### BibTeX
872
+
873
+ #### Sentence Transformers
874
+ ```bibtex
875
+ @inproceedings{reimers-2019-sentence-bert,
876
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
877
+ author = "Reimers, Nils and Gurevych, Iryna",
878
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
879
+ month = "11",
880
+ year = "2019",
881
+ publisher = "Association for Computational Linguistics",
882
+ url = "https://arxiv.org/abs/1908.10084",
883
+ }
884
+ ```
885
+
886
+ #### MatryoshkaLoss
887
+ ```bibtex
888
+ @misc{kusupati2024matryoshka,
889
+ title={Matryoshka Representation Learning},
890
+ author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
891
+ year={2024},
892
+ eprint={2205.13147},
893
+ archivePrefix={arXiv},
894
+ primaryClass={cs.LG}
895
+ }
896
+ ```
897
+
898
+ #### MultipleNegativesRankingLoss
899
+ ```bibtex
900
+ @misc{henderson2017efficient,
901
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
902
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
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+ year={2017},
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+ eprint={1705.00652},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```
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+ "single_word": false,
2009
+ "special": true
2010
+ },
2011
+ "128251": {
2012
+ "content": "<|reserved_special_token_243|>",
2013
+ "lstrip": false,
2014
+ "normalized": false,
2015
+ "rstrip": false,
2016
+ "single_word": false,
2017
+ "special": true
2018
+ },
2019
+ "128252": {
2020
+ "content": "<|reserved_special_token_244|>",
2021
+ "lstrip": false,
2022
+ "normalized": false,
2023
+ "rstrip": false,
2024
+ "single_word": false,
2025
+ "special": true
2026
+ },
2027
+ "128253": {
2028
+ "content": "<|reserved_special_token_245|>",
2029
+ "lstrip": false,
2030
+ "normalized": false,
2031
+ "rstrip": false,
2032
+ "single_word": false,
2033
+ "special": true
2034
+ },
2035
+ "128254": {
2036
+ "content": "<|reserved_special_token_246|>",
2037
+ "lstrip": false,
2038
+ "normalized": false,
2039
+ "rstrip": false,
2040
+ "single_word": false,
2041
+ "special": true
2042
+ },
2043
+ "128255": {
2044
+ "content": "<|reserved_special_token_247|>",
2045
+ "lstrip": false,
2046
+ "normalized": false,
2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|end_of_text|>",
2056
+ "extra_special_tokens": {},
2057
+ "mask_token": "<|mask|>",
2058
+ "max_length": 512,
2059
+ "model_input_names": [
2060
+ "input_ids",
2061
+ "attention_mask"
2062
+ ],
2063
+ "model_max_length": 1000000000000000019884624838656,
2064
+ "pad_to_multiple_of": null,
2065
+ "pad_token": "<|end_of_text|>",
2066
+ "pad_token_type_id": 0,
2067
+ "padding_side": "right",
2068
+ "tokenizer_class": "PreTrainedTokenizer"
2069
+ }