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Kobi-01
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tamil-qa-distilled

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Safetensors
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Kobi-01/distilled-tamil-qa-v2

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  1. README.md +105 -0
  2. adapter_config.json +35 -0
  3. adapter_model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: peft
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+ license: mit
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+ base_model: xlm-roberta-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - f1
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+ model-index:
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+ - name: tamil-qa-distilled
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # tamil-qa-distilled
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+
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.4959
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+ - Exact: 22.3153
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+ - F1: 37.4456
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+ - Total: 5848
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+ - Hasans Exact: 22.3153
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+ - Hasans F1: 37.4456
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+ - Hasans Total: 5848
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+ - Best Exact: 22.3153
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+ - Best Exact Thresh: 0.0
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+ - Best F1: 37.4456
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+ - Best F1 Thresh: 0.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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+ - train_batch_size: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 8
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+ - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Best Exact | Best Exact Thresh | Best F1 | Best F1 Thresh |
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+ |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:----------:|:-----------------:|:-------:|:--------------:|
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+ | 5.4467 | 0.0302 | 250 | 4.6445 | 0.4275 | 6.2970 | 5848 | 0.4275 | 6.2970 | 5848 | 0.4275 | 0.0 | 6.2970 | 0.0 |
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+ | 4.3568 | 0.0604 | 500 | 4.0214 | 4.8906 | 12.5359 | 5848 | 4.8906 | 12.5359 | 5848 | 4.8906 | 0.0 | 12.5359 | 0.0 |
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+ | 4.0656 | 0.0905 | 750 | 3.7904 | 12.5342 | 24.2632 | 5848 | 12.5342 | 24.2632 | 5848 | 12.5342 | 0.0 | 24.2632 | 0.0 |
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+ | 3.8889 | 0.1207 | 1000 | 3.3827 | 15.5438 | 27.3077 | 5848 | 15.5438 | 27.3077 | 5848 | 15.5438 | 0.0 | 27.3077 | 0.0 |
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+ | 3.4289 | 0.1509 | 1250 | 3.1672 | 13.8338 | 24.8643 | 5848 | 13.8338 | 24.8643 | 5848 | 13.8338 | 0.0 | 24.8643 | 0.0 |
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+ | 3.5355 | 0.1811 | 1500 | 3.0776 | 16.9802 | 30.2997 | 5848 | 16.9802 | 30.2997 | 5848 | 16.9802 | 0.0 | 30.2997 | 0.0 |
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+ | 3.1612 | 0.2112 | 1750 | 2.9511 | 19.4425 | 33.6814 | 5848 | 19.4425 | 33.6814 | 5848 | 19.4425 | 0.0 | 33.6814 | 0.0 |
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+ | 3.2976 | 0.2414 | 2000 | 3.0930 | 20.0752 | 34.6007 | 5848 | 20.0752 | 34.6007 | 5848 | 20.0752 | 0.0 | 34.6007 | 0.0 |
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+ | 3.2589 | 0.2716 | 2250 | 2.8639 | 18.9808 | 32.7633 | 5848 | 18.9808 | 32.7633 | 5848 | 18.9808 | 0.0 | 32.7633 | 0.0 |
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+ | 2.9672 | 0.3018 | 2500 | 2.9398 | 20.4001 | 35.6524 | 5848 | 20.4001 | 35.6524 | 5848 | 20.4001 | 0.0 | 35.6524 | 0.0 |
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+ | 3.7545 | 0.3319 | 2750 | 2.9454 | 19.2886 | 33.4999 | 5848 | 19.2886 | 33.4999 | 5848 | 19.2886 | 0.0 | 33.4999 | 0.0 |
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+ | 2.8654 | 0.3621 | 3000 | 2.7416 | 18.7244 | 31.0609 | 5848 | 18.7244 | 31.0609 | 5848 | 18.7244 | 0.0 | 31.0609 | 0.0 |
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+ | 3.0861 | 0.3923 | 3250 | 2.7491 | 21.0499 | 35.7036 | 5848 | 21.0499 | 35.7036 | 5848 | 21.0499 | 0.0 | 35.7036 | 0.0 |
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+ | 2.7695 | 0.4225 | 3500 | 2.8284 | 21.5287 | 36.4741 | 5848 | 21.5287 | 36.4741 | 5848 | 21.5287 | 0.0 | 36.4741 | 0.0 |
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+ | 3.0057 | 0.4526 | 3750 | 2.7043 | 20.1436 | 34.2750 | 5848 | 20.1436 | 34.2750 | 5848 | 20.1436 | 0.0 | 34.2750 | 0.0 |
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+ | 2.9906 | 0.4828 | 4000 | 2.6032 | 20.1607 | 33.6974 | 5848 | 20.1607 | 33.6974 | 5848 | 20.1607 | 0.0 | 33.6974 | 0.0 |
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+ | 2.7957 | 0.5130 | 4250 | 2.7008 | 20.9815 | 36.0642 | 5848 | 20.9815 | 36.0642 | 5848 | 20.9815 | 0.0 | 36.0642 | 0.0 |
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+ | 2.8885 | 0.5432 | 4500 | 2.6539 | 21.5116 | 37.0821 | 5848 | 21.5116 | 37.0821 | 5848 | 21.5116 | 0.0 | 37.0821 | 0.0 |
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+ | 2.7088 | 0.5733 | 4750 | 2.6216 | 22.1956 | 37.0885 | 5848 | 22.1956 | 37.0885 | 5848 | 22.1956 | 0.0 | 37.0885 | 0.0 |
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+ | 2.5385 | 0.6035 | 5000 | 2.7195 | 22.1614 | 37.4103 | 5848 | 22.1614 | 37.4103 | 5848 | 22.1614 | 0.0 | 37.4103 | 0.0 |
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+ | 2.7104 | 0.6337 | 5250 | 2.5939 | 21.0841 | 36.2480 | 5848 | 21.0841 | 36.2480 | 5848 | 21.0841 | 0.0 | 36.2480 | 0.0 |
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+ | 2.9083 | 0.6639 | 5500 | 2.5586 | 21.8707 | 37.0920 | 5848 | 21.8707 | 37.0920 | 5848 | 21.8707 | 0.0 | 37.0920 | 0.0 |
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+ | 2.7255 | 0.6940 | 5750 | 2.5521 | 21.3406 | 36.6577 | 5848 | 21.3406 | 36.6577 | 5848 | 21.3406 | 0.0 | 36.6577 | 0.0 |
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+ | 2.6606 | 0.7242 | 6000 | 2.6845 | 22.7770 | 38.6065 | 5848 | 22.7770 | 38.6065 | 5848 | 22.7770 | 0.0 | 38.6065 | 0.0 |
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+ | 2.7477 | 0.7544 | 6250 | 2.5134 | 21.3919 | 36.2724 | 5848 | 21.3919 | 36.2724 | 5848 | 21.3919 | 0.0 | 36.2724 | 0.0 |
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+ | 2.6319 | 0.7846 | 6500 | 2.5086 | 21.2722 | 35.6261 | 5848 | 21.2722 | 35.6261 | 5848 | 21.2722 | 0.0 | 35.6261 | 0.0 |
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+ | 2.687 | 0.8147 | 6750 | 2.6354 | 23.4097 | 39.3830 | 5848 | 23.4097 | 39.3830 | 5848 | 23.4097 | 0.0 | 39.3830 | 0.0 |
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+ | 2.6415 | 0.8449 | 7000 | 2.4868 | 21.7339 | 36.0235 | 5848 | 21.7339 | 36.0235 | 5848 | 21.7339 | 0.0 | 36.0235 | 0.0 |
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+ | 2.6671 | 0.8751 | 7250 | 2.5276 | 22.5718 | 38.0853 | 5848 | 22.5718 | 38.0853 | 5848 | 22.5718 | 0.0 | 38.0853 | 0.0 |
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+ | 2.489 | 0.9053 | 7500 | 2.4679 | 21.7852 | 36.3686 | 5848 | 21.7852 | 36.3686 | 5848 | 21.7852 | 0.0 | 36.3686 | 0.0 |
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+ | 2.6783 | 0.9354 | 7750 | 2.4992 | 22.1101 | 37.3694 | 5848 | 22.1101 | 37.3694 | 5848 | 22.1101 | 0.0 | 37.3694 | 0.0 |
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+ | 2.75 | 0.9656 | 8000 | 2.5187 | 22.4692 | 37.8004 | 5848 | 22.4692 | 37.8004 | 5848 | 22.4692 | 0.0 | 37.8004 | 0.0 |
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+ | 2.7079 | 0.9958 | 8250 | 2.4959 | 22.3153 | 37.4456 | 5848 | 22.3153 | 37.4456 | 5848 | 22.3153 | 0.0 | 37.4456 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.14.0
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+ - Transformers 4.48.2
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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+ "bias": "none",
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
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+ "loftq_config": {},
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+ "lora_alpha": 32,
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+ "lora_bias": false,
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+ "lora_dropout": 0.05,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
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+ "modules_to_save": [
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+ "qa_outputs"
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+ ],
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+ "peft_type": "LORA",
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+ "r": 8,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "query",
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+ "value",
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+ "key"
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+ ],
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+ "task_type": "QUESTION_ANS",
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+ "use_dora": false,
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+ "use_rslora": false
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+ }
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