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--- |
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license: apache-2.0 |
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base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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- f1 |
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model-index: |
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- name: pretoxtm-sentence-classifier |
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results: [] |
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datasets: |
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- javicorvi/pretoxtm-dataset |
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language: |
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- en |
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pipeline_tag: text-classification |
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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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# pretoxtm-sentence-classifier |
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This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on [javicorvi/pretoxtm-dataset](https://huggingface.co/datasets/javicorvi/pretoxtm-dataset). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1181 |
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- Precision: 0.9788 |
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- Recall: 0.9800 |
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- Accuracy: 0.9795 |
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- F1: 0.9794 |
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## Model description |
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PretoxTM Sentence Classifier is a model trained on preclinical toxicology literature, designed to detect sentences that contain treatment-related findings. |
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## Training and evaluation data |
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The model was trained on [javicorvi/pretoxtm-dataset](https://huggingface.co/datasets/javicorvi/pretoxtm-dataset). |
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The dataset is divided in train, validation and test. |
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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: 1.1848183151867784e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 1 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:| |
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| 0.2543 | 1.0 | 514 | 0.1181 | 0.9788 | 0.9800 | 0.9795 | 0.9794 | |
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| 0.1344 | 2.0 | 1028 | 0.1488 | 0.9767 | 0.9775 | 0.9773 | 0.9771 | |
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| 0.0419 | 3.0 | 1542 | 0.1520 | 0.9767 | 0.9775 | 0.9773 | 0.9771 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
