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--- |
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license: mit |
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base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
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tags: |
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- generated_from_trainer |
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datasets: |
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- covid_qa_deepset |
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model-index: |
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- name: bert-covid |
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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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# bert-covid |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co./microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the covid_qa_deepset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6900 |
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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: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 5.474 | 0.04 | 5 | 4.3730 | |
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| 3.9933 | 0.09 | 10 | 3.2783 | |
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| 3.0206 | 0.13 | 15 | 2.0289 | |
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| 1.9741 | 0.18 | 20 | 1.3879 | |
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| 1.4351 | 0.22 | 25 | 1.1733 | |
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| 1.5916 | 0.26 | 30 | 1.1623 | |
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| 0.5383 | 0.31 | 35 | 1.1952 | |
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| 0.7776 | 0.35 | 40 | 1.1920 | |
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| 1.1785 | 0.39 | 45 | 1.1216 | |
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| 1.1334 | 0.44 | 50 | 1.0412 | |
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| 0.7445 | 0.48 | 55 | 1.0829 | |
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| 0.6512 | 0.53 | 60 | 1.0443 | |
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| 0.7516 | 0.57 | 65 | 1.0089 | |
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| 0.5953 | 0.61 | 70 | 0.9273 | |
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| 0.8589 | 0.66 | 75 | 0.8947 | |
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| 0.7561 | 0.7 | 80 | 0.9009 | |
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| 0.9561 | 0.75 | 85 | 0.9006 | |
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| 0.7731 | 0.79 | 90 | 0.8482 | |
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| 0.8269 | 0.83 | 95 | 0.8380 | |
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| 0.9884 | 0.88 | 100 | 0.8200 | |
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| 0.9187 | 0.92 | 105 | 0.8775 | |
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| 0.585 | 0.96 | 110 | 0.8499 | |
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| 0.6835 | 1.01 | 115 | 0.8314 | |
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| 0.6668 | 1.05 | 120 | 0.7491 | |
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| 0.5558 | 1.1 | 125 | 0.7154 | |
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| 0.4491 | 1.14 | 130 | 0.8212 | |
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| 1.0667 | 1.18 | 135 | 0.8477 | |
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| 0.4472 | 1.23 | 140 | 0.7636 | |
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| 0.6892 | 1.27 | 145 | 0.7493 | |
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| 0.66 | 1.32 | 150 | 0.6932 | |
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| 0.5044 | 1.36 | 155 | 0.7675 | |
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| 0.5329 | 1.4 | 160 | 0.7406 | |
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| 0.2223 | 1.45 | 165 | 0.8099 | |
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| 0.5495 | 1.49 | 170 | 0.8758 | |
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| 0.5534 | 1.54 | 175 | 0.8476 | |
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| 0.4962 | 1.58 | 180 | 0.7953 | |
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| 0.7477 | 1.62 | 185 | 0.7610 | |
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| 0.7293 | 1.67 | 190 | 0.8357 | |
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| 0.6205 | 1.71 | 195 | 0.7339 | |
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| 0.5687 | 1.75 | 200 | 0.6908 | |
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| 0.884 | 1.8 | 205 | 0.6706 | |
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| 0.5928 | 1.84 | 210 | 0.6546 | |
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| 0.3209 | 1.89 | 215 | 0.6505 | |
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| 0.7585 | 1.93 | 220 | 0.6486 | |
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| 0.8501 | 1.97 | 225 | 0.6272 | |
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| 0.1664 | 2.02 | 230 | 0.6211 | |
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| 0.4483 | 2.06 | 235 | 0.6550 | |
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| 0.3361 | 2.11 | 240 | 0.6604 | |
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| 0.3085 | 2.15 | 245 | 0.6520 | |
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| 0.2407 | 2.19 | 250 | 0.6695 | |
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| 0.3418 | 2.24 | 255 | 0.6687 | |
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| 0.3165 | 2.28 | 260 | 0.6730 | |
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| 0.5811 | 2.32 | 265 | 0.6546 | |
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| 0.3516 | 2.37 | 270 | 0.6579 | |
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| 0.3136 | 2.41 | 275 | 0.6688 | |
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| 0.2508 | 2.46 | 280 | 0.6921 | |
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| 0.3463 | 2.5 | 285 | 0.7124 | |
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| 0.3603 | 2.54 | 290 | 0.7160 | |
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| 0.4455 | 2.59 | 295 | 0.6995 | |
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| 0.5433 | 2.63 | 300 | 0.6919 | |
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| 0.3411 | 2.68 | 305 | 0.6898 | |
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| 0.6065 | 2.72 | 310 | 0.6922 | |
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| 0.6258 | 2.76 | 315 | 0.6955 | |
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| 0.283 | 2.81 | 320 | 0.7008 | |
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| 0.6233 | 2.85 | 325 | 0.6988 | |
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| 0.3899 | 2.89 | 330 | 0.6949 | |
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| 0.238 | 2.94 | 335 | 0.6916 | |
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| 0.3166 | 2.98 | 340 | 0.6900 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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