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--- |
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base_model: haryoaw/scenario-TCR-NER_data-univner_half |
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library_name: transformers |
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license: mit |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: scenario-kd-scr-ner-full-xlmr_data-univner_half55 |
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results: [] |
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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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# scenario-kd-scr-ner-full-xlmr_data-univner_half55 |
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This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 239.5321 |
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- Precision: 0.3634 |
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- Recall: 0.2698 |
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- F1: 0.3097 |
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- Accuracy: 0.9371 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 55 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 443.8944 | 0.5828 | 500 | 368.5827 | 1.0 | 0.0003 | 0.0006 | 0.9241 | |
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| 344.6514 | 1.1655 | 1000 | 338.3108 | 0.4198 | 0.0238 | 0.0451 | 0.9249 | |
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| 317.7911 | 1.7483 | 1500 | 323.1518 | 0.3373 | 0.0781 | 0.1268 | 0.9266 | |
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| 295.7283 | 2.3310 | 2000 | 304.1432 | 0.3776 | 0.0879 | 0.1426 | 0.9282 | |
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| 279.1692 | 2.9138 | 2500 | 298.1003 | 0.3030 | 0.1619 | 0.2110 | 0.9301 | |
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| 265.46 | 3.4965 | 3000 | 283.4411 | 0.3299 | 0.1756 | 0.2292 | 0.9326 | |
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| 253.3522 | 4.0793 | 3500 | 276.4803 | 0.3419 | 0.1991 | 0.2517 | 0.9335 | |
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| 243.6295 | 4.6620 | 4000 | 268.1132 | 0.3623 | 0.2144 | 0.2694 | 0.9355 | |
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| 235.7751 | 5.2448 | 4500 | 260.5050 | 0.3808 | 0.1952 | 0.2581 | 0.9358 | |
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| 229.31 | 5.8275 | 5000 | 255.4243 | 0.3822 | 0.2135 | 0.2740 | 0.9358 | |
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| 222.7415 | 6.4103 | 5500 | 253.6783 | 0.3210 | 0.2489 | 0.2804 | 0.9345 | |
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| 218.7321 | 6.9930 | 6000 | 250.1186 | 0.3372 | 0.2663 | 0.2976 | 0.9354 | |
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| 213.8638 | 7.5758 | 6500 | 245.7943 | 0.3533 | 0.2519 | 0.2941 | 0.9362 | |
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| 211.1232 | 8.1585 | 7000 | 241.6974 | 0.3942 | 0.2450 | 0.3022 | 0.9382 | |
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| 208.2374 | 8.7413 | 7500 | 241.2330 | 0.3854 | 0.2630 | 0.3127 | 0.9375 | |
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| 206.2932 | 9.3240 | 8000 | 240.2229 | 0.3769 | 0.2672 | 0.3127 | 0.9373 | |
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| 205.5458 | 9.9068 | 8500 | 239.5321 | 0.3634 | 0.2698 | 0.3097 | 0.9371 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
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