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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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datasets: |
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- pritamdeka/cord-19-abstract |
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metrics: |
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- accuracy |
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base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
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model-index: |
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- name: pubmedbert-abstract-cord19 |
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results: |
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- task: |
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type: fill-mask |
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name: Masked Language Modeling |
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dataset: |
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name: pritamdeka/cord-19-abstract |
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type: pritamdeka/cord-19-abstract |
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args: fulltext |
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metrics: |
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- type: accuracy |
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value: 0.7246798699728464 |
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name: Accuracy |
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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-v2 |
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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](https://huggingface.co./datasets/pritamdeka/cord-19-abstract) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2371 |
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- Accuracy: 0.7247 |
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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=(0.9,0.95) 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: 4.0 |
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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 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 1.27 | 0.53 | 5000 | 1.2425 | 0.7236 | |
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| 1.2634 | 1.06 | 10000 | 1.3123 | 0.7141 | |
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| 1.3041 | 1.59 | 15000 | 1.3583 | 0.7072 | |
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| 1.3829 | 2.12 | 20000 | 1.3590 | 0.7121 | |
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| 1.3069 | 2.65 | 25000 | 1.3506 | 0.7154 | |
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| 1.2921 | 3.18 | 30000 | 1.3448 | 0.7160 | |
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| 1.2731 | 3.7 | 35000 | 1.3375 | 0.7178 | |
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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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