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README.md
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---
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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: biobert-base-cased-v1.2_ncbi_disease-sm-first-ner
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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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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: 0.8522139160437032
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- name: Recall
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type: recall
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value: 0.8826682549136391
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- name: F1
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type: f1
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value: 0.8671737858396723
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- name: Accuracy
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type: accuracy
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value: 0.9826972482743678
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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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# biobert-base-cased-v1.2_ncbi_disease-sm-first-ner
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This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the ncbi_disease dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0865
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- Precision: 0.8522
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- Recall: 0.8827
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- F1: 0.8672
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- Accuracy: 0.9827
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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: 4
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- eval_batch_size: 4
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0858 | 1.0 | 1359 | 0.0985 | 0.7929 | 0.8005 | 0.7967 | 0.9730 |
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| 0.042 | 2.0 | 2718 | 0.0748 | 0.8449 | 0.8856 | 0.8648 | 0.9820 |
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| 0.0124 | 3.0 | 4077 | 0.0865 | 0.8522 | 0.8827 | 0.8672 | 0.9827 |
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### Framework versions
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- Transformers 4.18.0
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- Pytorch 1.10.2+cu102
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- Datasets 2.3.2
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- Tokenizers 0.12.1
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