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bert-base-uncased-finetuned-ner

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bert-base-uncased-finetuned-ner
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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: biobert_json
      type: biobert_json
      config: Biobert_json
      split: validation
      args: Biobert_json
    metrics:
    - name: Precision
      type: precision
      value: 0.9295349495330629
    - name: Recall
      type: recall
      value: 0.966362655683044
    - name: F1
      type: f1
      value: 0.9475911145302433
    - name: Accuracy
      type: accuracy
      value: 0.9731129864041257
---

<!-- 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-base-uncased-finetuned-ner

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1050
- Precision: 0.9295
- Recall: 0.9664
- F1: 0.9476
- Accuracy: 0.9731

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4544        | 1.0   | 612  | 0.1204          | 0.9242    | 0.9569 | 0.9403 | 0.9698   |
| 0.146         | 2.0   | 1224 | 0.1050          | 0.9295    | 0.9664 | 0.9476 | 0.9731   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1