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BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa

Text Classification
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bert
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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/Sep14_12-15-13_6b7023bf0e37/events.out.tfevents.1631622834.6b7023bf0e37.89.23 +3 -0
  4. runs/Sep14_12-34-03_6b7023bf0e37/1631622849.3334026/events.out.tfevents.1631622849.6b7023bf0e37.89.25 +3 -0
  5. runs/Sep14_12-34-03_6b7023bf0e37/events.out.tfevents.1631622849.6b7023bf0e37.89.24 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -15,7 +15,7 @@ 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
@@ -25,8 +25,8 @@ should probably proofread and complete it, then remove this comment. -->
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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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0381
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- - Accuracy: 0.66
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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 | 0.9537 | 0.54 |
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- | No log | 2.0 | 114 | 0.9605 | 0.54 |
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- | No log | 3.0 | 171 | 0.9402 | 0.6 |
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- | No log | 4.0 | 228 | 0.9207 | 0.6 |
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- | No log | 5.0 | 285 | 0.9157 | 0.68 |
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- | No log | 6.0 | 342 | 0.9116 | 0.66 |
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- | No log | 7.0 | 399 | 0.9434 | 0.66 |
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- | No log | 8.0 | 456 | 1.0131 | 0.62 |
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- | 0.6196 | 9.0 | 513 | 1.0314 | 0.64 |
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- | 0.6196 | 10.0 | 570 | 1.0381 | 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 [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1384
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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 | 0.9521 | 0.54 |
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+ | No log | 2.0 | 114 | 0.9366 | 0.54 |
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+ | No log | 3.0 | 171 | 0.9259 | 0.54 |
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+ | No log | 4.0 | 228 | 0.9125 | 0.64 |
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+ | No log | 5.0 | 285 | 0.9583 | 0.58 |
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+ | No log | 6.0 | 342 | 0.9831 | 0.6 |
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+ | No log | 7.0 | 399 | 1.0094 | 0.62 |
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+ | No log | 8.0 | 456 | 1.0786 | 0.62 |
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+ | 0.5983 | 9.0 | 513 | 1.1478 | 0.58 |
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+ | 0.5983 | 10.0 | 570 | 1.1384 | 0.58 |
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  ### Framework versions
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