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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_5e-05 |
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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_5e-05 |
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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: 0.0165 |
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- Precision: 0.8814 |
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- Recall: 0.9243 |
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- F1: 0.9024 |
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- Accuracy: 0.9977 |
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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: 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: 500 |
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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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| 0.2905 | 1.47 | 25 | 0.0978 | 0.0 | 0.0 | 0.0 | 0.9843 | |
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| 0.0551 | 2.94 | 50 | 0.0382 | 0.3893 | 0.6270 | 0.4803 | 0.9887 | |
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| 0.0239 | 4.41 | 75 | 0.0192 | 0.5915 | 0.7514 | 0.6619 | 0.9947 | |
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| 0.0111 | 5.88 | 100 | 0.0153 | 0.8564 | 0.8703 | 0.8633 | 0.9964 | |
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| 0.0031 | 7.35 | 125 | 0.0126 | 0.8731 | 0.9297 | 0.9005 | 0.9975 | |
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| 0.002 | 8.82 | 150 | 0.0129 | 0.865 | 0.9351 | 0.8987 | 0.9978 | |
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| 0.0013 | 10.29 | 175 | 0.0163 | 0.8830 | 0.8973 | 0.8901 | 0.9968 | |
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| 0.0011 | 11.76 | 200 | 0.0171 | 0.9 | 0.9243 | 0.912 | 0.9970 | |
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| 0.001 | 13.24 | 225 | 0.0165 | 0.8808 | 0.9189 | 0.8995 | 0.9973 | |
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| 0.0008 | 14.71 | 250 | 0.0138 | 0.8923 | 0.9405 | 0.9158 | 0.9981 | |
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| 0.0007 | 16.18 | 275 | 0.0165 | 0.8763 | 0.9189 | 0.8971 | 0.9975 | |
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| 0.0005 | 17.65 | 300 | 0.0170 | 0.8854 | 0.9189 | 0.9019 | 0.9974 | |
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| 0.0005 | 19.12 | 325 | 0.0148 | 0.8731 | 0.9297 | 0.9005 | 0.9979 | |
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| 0.0005 | 20.59 | 350 | 0.0171 | 0.8848 | 0.9135 | 0.8989 | 0.9973 | |
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| 0.0005 | 22.06 | 375 | 0.0176 | 0.8848 | 0.9135 | 0.8989 | 0.9973 | |
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| 0.0005 | 23.53 | 400 | 0.0167 | 0.8860 | 0.9243 | 0.9048 | 0.9975 | |
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| 0.0004 | 25.0 | 425 | 0.0166 | 0.8860 | 0.9243 | 0.9048 | 0.9976 | |
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| 0.0004 | 26.47 | 450 | 0.0165 | 0.8814 | 0.9243 | 0.9024 | 0.9977 | |
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| 0.0004 | 27.94 | 475 | 0.0165 | 0.8814 | 0.9243 | 0.9024 | 0.9977 | |
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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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