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

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

Files changed (6) hide show
  1. README.md +13 -13
  2. pytorch_model.bin +1 -1
  3. runs/Sep13_23-34-00_6b9fab2f6f7f/events.out.tfevents.1631577277.6b9fab2f6f7f.89.2 +3 -0
  4. runs/Sep13_23-54-45_6b9fab2f6f7f/1631577290.7849503/events.out.tfevents.1631577290.6b9fab2f6f7f.89.4 +3 -0
  5. runs/Sep13_23-54-45_6b9fab2f6f7f/events.out.tfevents.1631577290.6b9fab2f6f7f.89.3 +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: 0.7
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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
@@ -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: 0.9163
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- - Accuracy: 0.7
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  ## Model description
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@@ -57,16 +57,16 @@ 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 | 0.9288 | 0.56 |
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- | No log | 2.0 | 114 | 0.8651 | 0.58 |
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- | No log | 3.0 | 171 | 0.8420 | 0.68 |
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- | No log | 4.0 | 228 | 0.8642 | 0.64 |
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- | No log | 5.0 | 285 | 0.8165 | 0.68 |
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- | No log | 6.0 | 342 | 0.8293 | 0.72 |
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- | No log | 7.0 | 399 | 0.8527 | 0.66 |
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- | No log | 8.0 | 456 | 0.9009 | 0.7 |
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- | 0.5624 | 9.0 | 513 | 0.8983 | 0.7 |
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- | 0.5624 | 10.0 | 570 | 0.9163 | 0.7 |
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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: 0.68
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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: 0.8169
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+ - Accuracy: 0.68
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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 | 0.9375 | 0.54 |
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+ | No log | 2.0 | 114 | 0.9253 | 0.56 |
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+ | No log | 3.0 | 171 | 0.8817 | 0.58 |
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+ | No log | 4.0 | 228 | 0.8459 | 0.64 |
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+ | No log | 5.0 | 285 | 0.8158 | 0.62 |
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+ | No log | 6.0 | 342 | 0.8041 | 0.66 |
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+ | No log | 7.0 | 399 | 0.7734 | 0.68 |
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+ | No log | 8.0 | 456 | 0.7971 | 0.7 |
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+ | 0.6083 | 9.0 | 513 | 0.8099 | 0.7 |
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+ | 0.6083 | 10.0 | 570 | 0.8169 | 0.68 |
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  ### Framework versions
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