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--- |
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license: mit |
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base_model: emilyalsentzer/Bio_ClinicalBERT |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: clinical_bert |
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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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# clinical_bert |
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This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6020 |
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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: 0.0005 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 5000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 0.78 | 100 | 1.9485 | |
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| No log | 1.56 | 200 | 1.8681 | |
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| No log | 2.34 | 300 | 1.8152 | |
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| No log | 3.12 | 400 | 1.7886 | |
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| 1.9285 | 3.91 | 500 | 1.7309 | |
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| 1.9285 | 4.69 | 600 | 1.6810 | |
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| 1.9285 | 5.47 | 700 | 1.7065 | |
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| 1.9285 | 6.25 | 800 | 1.7067 | |
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| 1.9285 | 7.03 | 900 | 1.7312 | |
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| 1.6644 | 7.81 | 1000 | 1.7006 | |
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| 1.6644 | 8.59 | 1100 | 1.6736 | |
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| 1.6644 | 9.38 | 1200 | 1.6846 | |
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| 1.6644 | 10.16 | 1300 | 1.6621 | |
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| 1.6644 | 10.94 | 1400 | 1.6381 | |
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| 1.5247 | 11.72 | 1500 | 1.6281 | |
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| 1.5247 | 12.5 | 1600 | 1.6605 | |
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| 1.5247 | 13.28 | 1700 | 1.6770 | |
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| 1.5247 | 14.06 | 1800 | 1.6666 | |
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| 1.5247 | 14.84 | 1900 | 1.6620 | |
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| 1.4334 | 15.62 | 2000 | 1.6677 | |
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| 1.4334 | 16.41 | 2100 | 1.6311 | |
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| 1.4334 | 17.19 | 2200 | 1.6743 | |
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| 1.4334 | 17.97 | 2300 | 1.6586 | |
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| 1.4334 | 18.75 | 2400 | 1.6086 | |
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| 1.3423 | 19.53 | 2500 | 1.6229 | |
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| 1.3423 | 20.31 | 2600 | 1.6475 | |
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| 1.3423 | 21.09 | 2700 | 1.6388 | |
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| 1.3423 | 21.88 | 2800 | 1.6275 | |
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| 1.3423 | 22.66 | 2900 | 1.6372 | |
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| 1.2712 | 23.44 | 3000 | 1.6345 | |
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| 1.2712 | 24.22 | 3100 | 1.6442 | |
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| 1.2712 | 25.0 | 3200 | 1.6864 | |
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| 1.2712 | 25.78 | 3300 | 1.6139 | |
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| 1.2712 | 26.56 | 3400 | 1.6161 | |
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| 1.215 | 27.34 | 3500 | 1.6491 | |
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| 1.215 | 28.12 | 3600 | 1.6442 | |
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| 1.215 | 28.91 | 3700 | 1.6409 | |
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| 1.215 | 29.69 | 3800 | 1.6539 | |
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| 1.215 | 30.47 | 3900 | 1.6052 | |
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| 1.1652 | 31.25 | 4000 | 1.6459 | |
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| 1.1652 | 32.03 | 4100 | 1.6362 | |
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| 1.1652 | 32.81 | 4200 | 1.6413 | |
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| 1.1652 | 33.59 | 4300 | 1.6377 | |
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| 1.1652 | 34.38 | 4400 | 1.6344 | |
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| 1.1213 | 35.16 | 4500 | 1.6406 | |
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| 1.1213 | 35.94 | 4600 | 1.6113 | |
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| 1.1213 | 36.72 | 4700 | 1.6410 | |
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| 1.1213 | 37.5 | 4800 | 1.6378 | |
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| 1.1213 | 38.28 | 4900 | 1.6341 | |
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| 1.0939 | 39.06 | 5000 | 1.6020 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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