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README.md CHANGED
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  ---
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- language: en
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  license: mit
 
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  tags:
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- - question-answering
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- - pytorch
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- - bert
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- datasets:
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- - rajpurkar/squad_v2
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- - Eladio/emrqa-msquad
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  ---
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  # tiny-clinicalbert-qa
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- A tiny clinical QA model that is trained using the merged [squad_v2](https://huggingface.co/datasets/rajpurkar/squad_v2) and [emrqa-msquad](https://huggingface.co/datasets/Eladio/emrqa-msquad) datasets.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- Source code for the training script is available [on GitHub](https://github.com/jon-edward/tiny-clinicalbert-qa). See [eval_results.json](https://huggingface.co/jon-t/tiny-clinicalbert-qa/blob/main/eval_results.json) for evaluation results, and [train_results.json](https://huggingface.co/jon-t/tiny-clinicalbert-qa/blob/main/train_results.json) for training results.
 
 
 
 
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  ---
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+ library_name: transformers
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  license: mit
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+ base_model: nlpie/tiny-clinicalbert
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  tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: tiny-clinicalbert-qa
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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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  # tiny-clinicalbert-qa
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+ This model is a fine-tuned version of [nlpie/tiny-clinicalbert](https://huggingface.co/nlpie/tiny-clinicalbert) on the None dataset.
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 8e-05
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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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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3.0
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+ - Transformers 4.53.0
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+ - Pytorch 2.7.1+cu118
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+ - Datasets 3.6.0
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+ - Tokenizers 0.21.2
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