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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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model-index: |
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- name: ec-biogpt-noised-pubmed-v4 |
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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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# ec-biogpt-noised-pubmed-v4 |
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This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8204 |
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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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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.7942 | 0.11 | 500 | 1.8358 | |
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| 1.9793 | 0.21 | 1000 | 1.8000 | |
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| 1.9244 | 0.32 | 1500 | 1.7763 | |
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| 1.8871 | 0.43 | 2000 | 1.7623 | |
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| 1.6525 | 0.54 | 2500 | 1.7511 | |
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| 1.6871 | 0.64 | 3000 | 1.7401 | |
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| 1.5771 | 0.75 | 3500 | 1.7315 | |
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| 1.732 | 0.86 | 4000 | 1.7278 | |
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| 1.9909 | 0.96 | 4500 | 1.7196 | |
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| 1.5173 | 1.07 | 5000 | 1.7204 | |
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| 1.6015 | 1.18 | 5500 | 1.7206 | |
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| 1.6817 | 1.28 | 6000 | 1.7183 | |
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| 1.6475 | 1.39 | 6500 | 1.7161 | |
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| 1.7425 | 1.5 | 7000 | 1.7114 | |
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| 1.4702 | 1.61 | 7500 | 1.7067 | |
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| 1.5635 | 1.71 | 8000 | 1.7078 | |
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| 1.574 | 1.82 | 8500 | 1.7020 | |
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| 1.6691 | 1.93 | 9000 | 1.6985 | |
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| 1.4796 | 2.03 | 9500 | 1.7339 | |
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| 1.472 | 2.14 | 10000 | 1.7354 | |
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| 1.4476 | 2.25 | 10500 | 1.7331 | |
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| 1.4402 | 2.35 | 11000 | 1.7327 | |
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| 1.5988 | 2.46 | 11500 | 1.7328 | |
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| 1.3682 | 2.57 | 12000 | 1.7299 | |
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| 1.4988 | 2.68 | 12500 | 1.7281 | |
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| 1.4514 | 2.78 | 13000 | 1.7257 | |
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| 1.6356 | 2.89 | 13500 | 1.7264 | |
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| 1.6653 | 3.0 | 14000 | 1.7240 | |
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| 1.2013 | 3.1 | 14500 | 1.7782 | |
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| 1.2864 | 3.21 | 15000 | 1.7770 | |
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| 1.4638 | 3.32 | 15500 | 1.7817 | |
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| 1.2501 | 3.43 | 16000 | 1.7787 | |
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| 1.4613 | 3.53 | 16500 | 1.7791 | |
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| 1.1816 | 3.64 | 17000 | 1.7767 | |
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| 1.1841 | 3.75 | 17500 | 1.7786 | |
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| 1.2382 | 3.85 | 18000 | 1.7743 | |
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| 1.2868 | 3.96 | 18500 | 1.7749 | |
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| 1.2074 | 4.07 | 19000 | 1.8167 | |
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| 1.1657 | 4.17 | 19500 | 1.8224 | |
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| 1.1851 | 4.28 | 20000 | 1.8197 | |
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| 1.1141 | 4.39 | 20500 | 1.8225 | |
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| 1.0628 | 4.5 | 21000 | 1.8202 | |
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| 1.0946 | 4.6 | 21500 | 1.8209 | |
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| 1.037 | 4.71 | 22000 | 1.8201 | |
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| 1.1277 | 4.82 | 22500 | 1.8206 | |
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| 1.2766 | 4.92 | 23000 | 1.8204 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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