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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: Variome_0.0001_29_03 |
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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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# Variome_0.0001_29_03 |
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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: 0.1367 |
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- Precision: 0.6567 |
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- Recall: 0.3308 |
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- F1: 0.44 |
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- Accuracy: 0.9842 |
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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: 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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| 0.4296 | 5.0 | 25 | 0.1570 | 0.0 | 0.0 | 0.0 | 0.9794 | |
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| 0.1679 | 10.0 | 50 | 0.1546 | 0.0 | 0.0 | 0.0 | 0.9794 | |
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| 0.1632 | 15.0 | 75 | 0.1375 | 0.0 | 0.0 | 0.0 | 0.9794 | |
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| 0.1332 | 20.0 | 100 | 0.1357 | 0.2381 | 0.0376 | 0.0649 | 0.9799 | |
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| 0.0886 | 25.0 | 125 | 0.1250 | 0.2222 | 0.0602 | 0.0947 | 0.9805 | |
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| 0.0714 | 30.0 | 150 | 0.1278 | 0.3333 | 0.1053 | 0.16 | 0.9809 | |
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| 0.0479 | 35.0 | 175 | 0.1220 | 0.5 | 0.2256 | 0.3109 | 0.9831 | |
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| 0.0301 | 40.0 | 200 | 0.1259 | 0.6154 | 0.3008 | 0.4040 | 0.9841 | |
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| 0.0198 | 45.0 | 225 | 0.1257 | 0.6364 | 0.3158 | 0.4221 | 0.9846 | |
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| 0.0138 | 50.0 | 250 | 0.1240 | 0.6184 | 0.3534 | 0.4498 | 0.9847 | |
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| 0.0099 | 55.0 | 275 | 0.1301 | 0.5823 | 0.3459 | 0.4340 | 0.9837 | |
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| 0.008 | 60.0 | 300 | 0.1343 | 0.5584 | 0.3233 | 0.4095 | 0.9832 | |
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| 0.0066 | 65.0 | 325 | 0.1290 | 0.5625 | 0.3383 | 0.4225 | 0.9830 | |
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| 0.0054 | 70.0 | 350 | 0.1366 | 0.6061 | 0.3008 | 0.4020 | 0.9838 | |
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| 0.0047 | 75.0 | 375 | 0.1334 | 0.6111 | 0.3308 | 0.4293 | 0.9841 | |
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| 0.0044 | 80.0 | 400 | 0.1367 | 0.6567 | 0.3308 | 0.44 | 0.9842 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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