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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- null |
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metrics: |
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- accuracy |
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model-index: |
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- name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.48 |
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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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# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa |
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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: 3.1085 |
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- Accuracy: 0.48 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 57 | 0.9812 | 0.36 | |
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| No log | 2.0 | 114 | 0.9754 | 0.54 | |
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| No log | 3.0 | 171 | 0.9586 | 0.54 | |
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| No log | 4.0 | 228 | 1.0887 | 0.54 | |
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| No log | 5.0 | 285 | 1.3056 | 0.5 | |
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| No log | 6.0 | 342 | 1.9388 | 0.48 | |
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| No log | 7.0 | 399 | 2.4752 | 0.48 | |
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| No log | 8.0 | 456 | 2.8226 | 0.52 | |
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| 0.5686 | 9.0 | 513 | 3.0738 | 0.48 | |
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| 0.5686 | 10.0 | 570 | 3.1085 | 0.48 | |
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### Framework versions |
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- Transformers 4.10.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.3 |
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