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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_half66 |
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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_half66 |
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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: 239.7216 |
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- Precision: 0.3760 |
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- Recall: 0.2925 |
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- F1: 0.3290 |
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- Accuracy: 0.9383 |
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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: 66 |
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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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| 445.7648 | 0.5828 | 500 | 369.2686 | 0.0 | 0.0 | 0.0 | 0.9241 | |
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| 345.7662 | 1.1655 | 1000 | 344.5894 | 0.3114 | 0.0688 | 0.1127 | 0.9255 | |
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| 317.4014 | 1.7483 | 1500 | 321.3392 | 0.3477 | 0.0814 | 0.1319 | 0.9268 | |
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| 296.2663 | 2.3310 | 2000 | 304.3693 | 0.4262 | 0.0837 | 0.1399 | 0.9278 | |
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| 278.6608 | 2.9138 | 2500 | 293.9949 | 0.3311 | 0.1453 | 0.2020 | 0.9306 | |
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| 265.0553 | 3.4965 | 3000 | 282.5093 | 0.3451 | 0.1672 | 0.2253 | 0.9328 | |
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| 253.4131 | 4.0793 | 3500 | 275.8793 | 0.3284 | 0.2137 | 0.2589 | 0.9337 | |
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| 243.5783 | 4.6620 | 4000 | 268.4692 | 0.3237 | 0.2345 | 0.2719 | 0.9341 | |
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| 235.5956 | 5.2448 | 4500 | 262.3924 | 0.3467 | 0.2506 | 0.2909 | 0.9355 | |
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| 228.406 | 5.8275 | 5000 | 257.0986 | 0.3634 | 0.2381 | 0.2877 | 0.9361 | |
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| 222.6923 | 6.4103 | 5500 | 250.0558 | 0.3799 | 0.2350 | 0.2904 | 0.9380 | |
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| 218.056 | 6.9930 | 6000 | 246.7546 | 0.3904 | 0.2479 | 0.3032 | 0.9383 | |
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| 213.6749 | 7.5758 | 6500 | 245.7390 | 0.3713 | 0.2721 | 0.3141 | 0.9373 | |
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| 210.549 | 8.1585 | 7000 | 242.9818 | 0.3644 | 0.2611 | 0.3043 | 0.9367 | |
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| 207.9283 | 8.7413 | 7500 | 240.6766 | 0.3721 | 0.2789 | 0.3188 | 0.9376 | |
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| 206.1746 | 9.3240 | 8000 | 239.1118 | 0.3990 | 0.2772 | 0.3271 | 0.9391 | |
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| 205.302 | 9.9068 | 8500 | 239.7216 | 0.3760 | 0.2925 | 0.3290 | 0.9383 | |
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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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