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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: 0.1823 |
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- Accuracy: 0.9261 |
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- F1: 0.9261 |
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- Precision: 0.9285 |
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- Recall: 0.9237 |
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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: 512 |
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- eval_batch_size: 1024 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 469 | 0.1895 | 0.9226 | 0.9222 | 0.9301 | 0.9145 | |
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| 0.1886 | 2.0 | 938 | 0.1844 | 0.9251 | 0.9253 | 0.9259 | 0.9247 | |
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| 0.1688 | 3.0 | 1407 | 0.1823 | 0.9261 | 0.9261 | 0.9285 | 0.9237 | |
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| 0.1558 | 4.0 | 1876 | 0.1838 | 0.9259 | 0.9261 | 0.9262 | 0.9260 | |
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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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