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--- |
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base_model: haryoaw/scenario-TCR-NER_data-univner_full |
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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_full44 |
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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_full44 |
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This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 158.9407 |
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- Precision: 0.5487 |
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- Recall: 0.5432 |
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- F1: 0.5459 |
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- Accuracy: 0.9588 |
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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: 44 |
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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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| 460.9453 | 0.2911 | 500 | 385.8351 | 0.0 | 0.0 | 0.0 | 0.9241 | |
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| 363.4557 | 0.5822 | 1000 | 346.2052 | 0.4279 | 0.0544 | 0.0965 | 0.9263 | |
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| 332.2012 | 0.8732 | 1500 | 325.9525 | 0.3466 | 0.0752 | 0.1235 | 0.9270 | |
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| 311.0392 | 1.1643 | 2000 | 305.0690 | 0.2719 | 0.1538 | 0.1965 | 0.9302 | |
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| 293.6181 | 1.4554 | 2500 | 289.0764 | 0.2902 | 0.1616 | 0.2076 | 0.9319 | |
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| 278.0759 | 1.7465 | 3000 | 274.3208 | 0.3437 | 0.1727 | 0.2299 | 0.9351 | |
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| 263.7235 | 2.0375 | 3500 | 261.5710 | 0.3720 | 0.2477 | 0.2974 | 0.9395 | |
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| 250.8804 | 2.3286 | 4000 | 251.6802 | 0.4335 | 0.2371 | 0.3065 | 0.9399 | |
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| 241.6486 | 2.6197 | 4500 | 241.4983 | 0.3763 | 0.3122 | 0.3413 | 0.9431 | |
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| 232.1585 | 2.9108 | 5000 | 234.7022 | 0.4332 | 0.2502 | 0.3172 | 0.9419 | |
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| 223.428 | 3.2019 | 5500 | 225.8982 | 0.4283 | 0.3369 | 0.3771 | 0.9458 | |
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| 215.7432 | 3.4929 | 6000 | 218.6250 | 0.4172 | 0.3455 | 0.3780 | 0.9469 | |
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| 208.5821 | 3.7840 | 6500 | 212.5754 | 0.4244 | 0.4311 | 0.4277 | 0.9480 | |
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| 202.7347 | 4.0751 | 7000 | 206.2203 | 0.4440 | 0.4046 | 0.4233 | 0.9502 | |
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| 196.2593 | 4.3662 | 7500 | 200.8361 | 0.4877 | 0.4275 | 0.4556 | 0.9518 | |
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| 191.1748 | 4.6573 | 8000 | 196.1823 | 0.4735 | 0.4281 | 0.4496 | 0.9524 | |
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| 186.3328 | 4.9483 | 8500 | 191.5347 | 0.4679 | 0.4555 | 0.4616 | 0.9531 | |
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| 180.9869 | 5.2394 | 9000 | 187.5859 | 0.4850 | 0.4979 | 0.4914 | 0.9549 | |
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| 176.8171 | 5.5305 | 9500 | 183.7527 | 0.4858 | 0.5217 | 0.5031 | 0.9551 | |
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| 173.9635 | 5.8216 | 10000 | 180.3877 | 0.5310 | 0.4719 | 0.4997 | 0.9553 | |
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| 170.1918 | 6.1126 | 10500 | 177.5785 | 0.5234 | 0.4582 | 0.4887 | 0.9556 | |
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| 167.0458 | 6.4037 | 11000 | 174.8427 | 0.5411 | 0.4620 | 0.4984 | 0.9554 | |
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| 164.3432 | 6.6948 | 11500 | 171.9410 | 0.5348 | 0.5089 | 0.5215 | 0.9570 | |
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| 161.6384 | 6.9859 | 12000 | 169.9951 | 0.5304 | 0.5017 | 0.5156 | 0.9573 | |
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| 159.3906 | 7.2770 | 12500 | 167.7097 | 0.5450 | 0.5037 | 0.5235 | 0.9576 | |
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| 157.2167 | 7.5680 | 13000 | 165.9562 | 0.5248 | 0.5260 | 0.5254 | 0.9578 | |
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| 155.8673 | 7.8591 | 13500 | 164.2853 | 0.5485 | 0.5210 | 0.5344 | 0.9581 | |
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| 153.9819 | 8.1502 | 14000 | 162.9678 | 0.5385 | 0.5161 | 0.5271 | 0.9581 | |
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| 152.5708 | 8.4413 | 14500 | 161.7499 | 0.5424 | 0.5385 | 0.5404 | 0.9583 | |
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| 151.7945 | 8.7324 | 15000 | 160.7884 | 0.5528 | 0.5282 | 0.5402 | 0.9585 | |
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| 150.7972 | 9.0234 | 15500 | 160.0030 | 0.5441 | 0.5455 | 0.5448 | 0.9590 | |
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| 149.6132 | 9.3145 | 16000 | 159.5214 | 0.5446 | 0.5474 | 0.5460 | 0.9585 | |
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| 149.1168 | 9.6056 | 16500 | 159.1108 | 0.5540 | 0.5376 | 0.5457 | 0.9588 | |
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| 149.234 | 9.8967 | 17000 | 158.9407 | 0.5487 | 0.5432 | 0.5459 | 0.9588 | |
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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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