---
library_name: transformers
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.7952941176470588
    - name: Recall
      type: recall
      value: 0.8589580686149937
    - name: F1
      type: f1
      value: 0.8259010384850336
    - name: Accuracy
      type: accuracy
      value: 0.9841210883090352
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-finetuned-ner

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0623
- Precision: 0.7953
- Recall: 0.8590
- F1: 0.8259
- Accuracy: 0.9841

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1204        | 1.0   | 680  | 0.0536          | 0.7417    | 0.8247 | 0.7810 | 0.9824   |
| 0.0386        | 2.0   | 1360 | 0.0542          | 0.7808    | 0.8463 | 0.8122 | 0.9831   |
| 0.0144        | 3.0   | 2040 | 0.0623          | 0.7953    | 0.8590 | 0.8259 | 0.9841   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1