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---
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library_name: transformers
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license: mit
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base_model: microsoft/biogpt
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tags:
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- generated_from_trainer
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datasets:
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- ncbi_disease
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: biogpt-ner-checkpoint
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: ncbi_disease
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type: ncbi_disease
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config: ncbi_disease
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split: validation
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args: ncbi_disease
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metrics:
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- name: Precision
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type: precision
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value: 0.569364161849711
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- name: Recall
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type: recall
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value: 0.5412087912087912
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- name: F1
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type: f1
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value: 0.5549295774647887
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- name: Accuracy
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type: accuracy
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value: 0.8274946921443737
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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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# biogpt-ner-checkpoint
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This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
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It achieves the following results on the evaluation set:
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- Loss: nan
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- Precision: 0.5694
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- Recall: 0.5412
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- F1: 0.5549
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- Accuracy: 0.8275
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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: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Use 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
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### Training results
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### Framework versions
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- Transformers 4.51.2
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- Pytorch 2.5.1+cu121
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- Datasets 3.5.0
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- Tokenizers 0.21.1
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