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Parent(s):
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Browse files- README.md +28 -6
- all_results.json +10 -10
- eval_results.json +6 -6
- pytorch_model.bin +1 -1
- train_results.json +5 -5
- trainer_state.json +207 -335
- training_args.bin +1 -1
README.md
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datasets:
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- pritamdeka/cord-19-abstract
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model-index:
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- name:
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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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#
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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
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It achieves the following results on the evaluation set:
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- Loss: 1.
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## Model description
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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: 10000
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- num_epochs:
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- mixed_precision_training: Native AMP
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### Training results
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### Framework versions
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- Transformers 4.17.0.dev0
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- Pytorch 1.10.0+cu111
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- Datasets 1.18.
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- Tokenizers 0.11.0
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datasets:
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- pritamdeka/cord-19-abstract
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model-index:
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- name: pubmedbert-abstract-cord19
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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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# pubmedbert-abstract-cord19
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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 pritamdeka/cord-19-abstract dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3005
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## Model description
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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: 10000
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- num_epochs: 3.0
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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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| 1.3774 | 0.15 | 5000 | 1.3212 |
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| 1.3937 | 0.29 | 10000 | 1.4059 |
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| 1.6812 | 0.44 | 15000 | 1.6174 |
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| 1.4712 | 0.59 | 20000 | 1.4383 |
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| 1.4293 | 0.73 | 25000 | 1.4356 |
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| 1.4155 | 0.88 | 30000 | 1.4283 |
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| 1.3963 | 1.03 | 35000 | 1.4135 |
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| 1.3718 | 1.18 | 40000 | 1.3948 |
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| 1.369 | 1.32 | 45000 | 1.3961 |
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| 1.354 | 1.47 | 50000 | 1.3788 |
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| 1.3399 | 1.62 | 55000 | 1.3866 |
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| 1.3289 | 1.76 | 60000 | 1.3630 |
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| 1.3155 | 1.91 | 65000 | 1.3609 |
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| 1.2976 | 2.06 | 70000 | 1.3489 |
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| 1.2783 | 2.2 | 75000 | 1.3333 |
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| 1.2696 | 2.35 | 80000 | 1.3260 |
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| 1.2607 | 2.5 | 85000 | 1.3232 |
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| 1.2547 | 2.64 | 90000 | 1.3034 |
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| 1.2495 | 2.79 | 95000 | 1.3035 |
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| 1.2404 | 2.94 | 100000 | 1.3029 |
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### Framework versions
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- Transformers 4.17.0.dev0
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- Pytorch 1.10.0+cu111
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- Datasets 1.18.3
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- Tokenizers 0.11.0
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