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End of training
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README.md
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---
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base_model: dmis-lab/biobert-v1.1
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tags:
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- generated_from_trainer
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model-index:
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- name: CRAFT_bioBERT_NER
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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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# CRAFT_bioBERT_NER
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This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1106
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- Seqeval classification report: precision recall f1-score support
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CHEBI 0.83 0.76 0.80 1109
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CL 0.91 0.90 0.90 3871
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GGP 0.76 0.66 0.71 600
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GO 0.87 0.84 0.85 1061
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SO 0.99 0.99 0.99 87954
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Taxon 0.83 0.87 0.85 3104
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micro avg 0.98 0.97 0.97 97699
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macro avg 0.87 0.84 0.85 97699
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weighted avg 0.98 0.97 0.97 97699
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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| No log | 1.0 | 347 | 0.1141 | precision recall f1-score support
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CHEBI 0.82 0.65 0.72 1109
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CL 0.90 0.87 0.89 3871
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GGP 0.75 0.62 0.68 600
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GO 0.88 0.77 0.82 1061
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SO 0.99 0.99 0.99 87954
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Taxon 0.79 0.88 0.83 3104
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micro avg 0.97 0.97 0.97 97699
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macro avg 0.86 0.80 0.82 97699
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weighted avg 0.97 0.97 0.97 97699
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| 0.1705 | 2.0 | 695 | 0.1121 | precision recall f1-score support
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CHEBI 0.86 0.73 0.79 1109
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CL 0.90 0.90 0.90 3871
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GGP 0.73 0.65 0.69 600
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GO 0.87 0.82 0.85 1061
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SO 0.99 0.99 0.99 87954
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Taxon 0.79 0.89 0.84 3104
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micro avg 0.97 0.97 0.97 97699
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macro avg 0.86 0.83 0.84 97699
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weighted avg 0.97 0.97 0.97 97699
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| 0.04 | 3.0 | 1041 | 0.1106 | precision recall f1-score support
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CHEBI 0.83 0.76 0.80 1109
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CL 0.91 0.90 0.90 3871
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GGP 0.76 0.66 0.71 600
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GO 0.87 0.84 0.85 1061
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SO 0.99 0.99 0.99 87954
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Taxon 0.83 0.87 0.85 3104
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micro avg 0.98 0.97 0.97 97699
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macro avg 0.87 0.84 0.85 97699
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weighted avg 0.98 0.97 0.97 97699
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu118
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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