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BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1

Text Classification Transformers PyTorch TensorBoard bert generated_from_trainer Eval Results Inference Endpoints
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BATCH_SIZE=8

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LEARNING_RATE=1e-05
MAX_LENGTH=512
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Files changed (6) hide show
  1. README.md +13 -13
  2. pytorch_model.bin +1 -1
  3. runs/Sep15_07-46-00_6f87b01ed96f/events.out.tfevents.1631693014.6f87b01ed96f.89.26 +3 -0
  4. runs/Sep15_08-03-44_6f87b01ed96f/1631693029.8793652/events.out.tfevents.1631693029.6f87b01ed96f.89.28 +3 -0
  5. runs/Sep15_08-03-44_6f87b01ed96f/events.out.tfevents.1631693029.6f87b01ed96f.89.27 +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: 0.6
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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: 1.0467
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- - Accuracy: 0.6
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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 | 0.9542 | 0.54 |
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- | No log | 2.0 | 114 | 0.9610 | 0.54 |
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- | No log | 3.0 | 171 | 0.9574 | 0.56 |
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- | No log | 4.0 | 228 | 0.9761 | 0.5 |
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- | No log | 5.0 | 285 | 0.9585 | 0.64 |
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- | No log | 6.0 | 342 | 0.9418 | 0.64 |
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- | No log | 7.0 | 399 | 0.9941 | 0.64 |
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- | No log | 8.0 | 456 | 1.0317 | 0.62 |
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- | 0.6491 | 9.0 | 513 | 1.0513 | 0.6 |
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- | 0.6491 | 10.0 | 570 | 1.0467 | 0.6 |
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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: 0.7
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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: 0.6660
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+ - Accuracy: 0.7
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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 | 0.8471 | 0.58 |
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+ | No log | 2.0 | 114 | 0.8450 | 0.58 |
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+ | No log | 3.0 | 171 | 0.7846 | 0.58 |
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+ | No log | 4.0 | 228 | 0.8649 | 0.58 |
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+ | No log | 5.0 | 285 | 0.7220 | 0.68 |
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+ | No log | 6.0 | 342 | 0.7395 | 0.66 |
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+ | No log | 7.0 | 399 | 0.7198 | 0.72 |
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+ | No log | 8.0 | 456 | 0.6417 | 0.72 |
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+ | 0.7082 | 9.0 | 513 | 0.6265 | 0.74 |
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+ | 0.7082 | 10.0 | 570 | 0.6660 | 0.7 |
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  ### Framework versions
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