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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: tmvar_0.0001_ES12 |
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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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# tmvar_0.0001_ES12 |
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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: 0.0194 |
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- Precision: 0.8877 |
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- Recall: 0.8973 |
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- F1: 0.8925 |
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- Accuracy: 0.9968 |
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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: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.2263 | 1.47 | 25 | 0.0788 | 0.0 | 0.0 | 0.0 | 0.9843 | |
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| 0.0492 | 2.94 | 50 | 0.0355 | 0.2576 | 0.3676 | 0.3029 | 0.9863 | |
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| 0.0258 | 4.41 | 75 | 0.0224 | 0.6 | 0.6811 | 0.6380 | 0.9933 | |
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| 0.013 | 5.88 | 100 | 0.0141 | 0.8267 | 0.9027 | 0.8630 | 0.9969 | |
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| 0.0031 | 7.35 | 125 | 0.0162 | 0.8218 | 0.8973 | 0.8579 | 0.9971 | |
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| 0.0028 | 8.82 | 150 | 0.0187 | 0.8449 | 0.8541 | 0.8495 | 0.9961 | |
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| 0.0024 | 10.29 | 175 | 0.0154 | 0.8267 | 0.9027 | 0.8630 | 0.9965 | |
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| 0.0014 | 11.76 | 200 | 0.0159 | 0.8221 | 0.9243 | 0.8702 | 0.9966 | |
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| 0.0013 | 13.24 | 225 | 0.0179 | 0.8579 | 0.8811 | 0.8693 | 0.9971 | |
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| 0.0009 | 14.71 | 250 | 0.0165 | 0.8807 | 0.8378 | 0.8587 | 0.9964 | |
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| 0.0005 | 16.18 | 275 | 0.0184 | 0.8549 | 0.8919 | 0.8730 | 0.9966 | |
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| 0.0003 | 17.65 | 300 | 0.0188 | 0.8777 | 0.8919 | 0.8847 | 0.9967 | |
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| 0.0002 | 19.12 | 325 | 0.0195 | 0.8474 | 0.8703 | 0.8587 | 0.9964 | |
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| 0.0002 | 20.59 | 350 | 0.0192 | 0.8836 | 0.9027 | 0.8930 | 0.9969 | |
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| 0.0003 | 22.06 | 375 | 0.0191 | 0.8889 | 0.9081 | 0.8984 | 0.9969 | |
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| 0.0002 | 23.53 | 400 | 0.0194 | 0.8877 | 0.8973 | 0.8925 | 0.9968 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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