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biobert-v1.1-finetuned-pubmedqa

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BATCH_SIZE=8

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LEARNING_RATE=1e-05
MAX_LENGTH=512
FOLD=8

Files changed (6) hide show
  1. README.md +13 -13
  2. pytorch_model.bin +1 -1
  3. runs/Sep13_16-50-26_1428212fe7ba/events.out.tfevents.1631553301.1428212fe7ba.3883.23 +3 -0
  4. runs/Sep13_17-15-11_1428212fe7ba/1631553316.9448335/events.out.tfevents.1631553316.1428212fe7ba.3883.25 +3 -0
  5. runs/Sep13_17-15-11_1428212fe7ba/events.out.tfevents.1631553316.1428212fe7ba.3883.24 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -14,7 +14,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.66
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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
@@ -24,8 +24,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2100
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- - Accuracy: 0.66
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  ## Model description
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@@ -56,16 +56,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 57 | 0.9464 | 0.62 |
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- | No log | 2.0 | 114 | 0.8952 | 0.6 |
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- | No log | 3.0 | 171 | 0.8903 | 0.68 |
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- | No log | 4.0 | 228 | 0.9128 | 0.68 |
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- | No log | 5.0 | 285 | 0.9903 | 0.68 |
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- | No log | 6.0 | 342 | 1.0209 | 0.66 |
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- | No log | 7.0 | 399 | 1.0988 | 0.68 |
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- | No log | 8.0 | 456 | 1.1902 | 0.66 |
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- | 0.5665 | 9.0 | 513 | 1.1923 | 0.68 |
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- | 0.5665 | 10.0 | 570 | 1.2100 | 0.66 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.58
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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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  This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2591
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+ - Accuracy: 0.58
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 57 | 0.9383 | 0.54 |
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+ | No log | 2.0 | 114 | 0.8907 | 0.6 |
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+ | No log | 3.0 | 171 | 0.9136 | 0.56 |
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+ | No log | 4.0 | 228 | 0.9369 | 0.6 |
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+ | No log | 5.0 | 285 | 1.0082 | 0.54 |
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+ | No log | 6.0 | 342 | 1.0751 | 0.58 |
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+ | No log | 7.0 | 399 | 1.1822 | 0.6 |
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+ | No log | 8.0 | 456 | 1.2448 | 0.58 |
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+ | 0.5684 | 9.0 | 513 | 1.2731 | 0.56 |
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+ | 0.5684 | 10.0 | 570 | 1.2591 | 0.58 |
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  ### Framework versions
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