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--- |
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license: apache-2.0 |
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tags: |
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- question-answering |
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- generated_from_trainer |
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base_model: nlpconnect/roberta-base-squad2-nq |
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model-index: |
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- name: roberta-base-squad2-nq-bioasq |
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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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# roberta-base-squad2-nq-bioasq |
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## Model description |
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This model is a fine-tuned version of [nlpconnect/roberta-base-squad2-nq](https://huggingface.co/nlpconnect/roberta-base-squad2-nq) on the BioASQ 10b dataset. |
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## Intended uses & limitations |
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Cross-domain question answering! |
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## Training and evaluation data |
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Training: BioASQ 10B with SQUAD sampled evenly to match the same samples as BioASQ 10B |
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Eval: BioASQ 9B Eval with SQUAD Eval sampled evenly to match the same samples as BioASQ 9B Eval |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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Went from untrained exact match: 60.9% (f1 71.8%) to exact match: 95.2% (96.6% f1) on BioASQ 9B held out training set. |
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Scores on SQUAD+BioASQ remained stable at exact match: 72.5% (f1 81.4%) to 88.5% (f1 93.3%). |
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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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