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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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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: im-bin-tf-abstr |
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results: [] |
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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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# im-bin-tf-abstr |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1908 |
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- Accuracy: 0.9222 |
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- F1: 0.9220 |
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- Precision: 0.9267 |
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- Recall: 0.9174 |
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- Roc Auc: 0.9781 |
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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: 1e-05 |
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- train_batch_size: 640 |
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- eval_batch_size: 1280 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:| |
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| No log | 1.0 | 375 | 0.2136 | 0.9124 | 0.9131 | 0.9087 | 0.9175 | 0.9733 | |
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| 0.3086 | 2.0 | 750 | 0.1971 | 0.9195 | 0.9190 | 0.9277 | 0.9104 | 0.9770 | |
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| 0.1917 | 3.0 | 1125 | 0.1908 | 0.9222 | 0.9220 | 0.9267 | 0.9174 | 0.9781 | |
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| 0.1791 | 4.0 | 1500 | 0.1909 | 0.9224 | 0.9224 | 0.9247 | 0.9202 | 0.9785 | |
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### Framework versions |
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- Transformers 4.31.0.dev0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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