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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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- enoriega/keyword_pubmed |
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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: kw_pubmed_vanilla_sentence_10000_0.0003_2 |
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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: enoriega/keyword_pubmed sentence |
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type: enoriega/keyword_pubmed |
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args: sentence |
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metrics: |
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- type: accuracy |
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value: 0.6767448105720579 |
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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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# kw_pubmed_vanilla_sentence_10000_0.0003_2 |
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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 enoriega/keyword_pubmed sentence dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5883 |
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- Accuracy: 0.6767 |
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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: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 500 |
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- total_train_batch_size: 8000 |
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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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- num_epochs: 3.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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