---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results: []
---

<!-- 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 [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3391
- Precision: 0.8826
- Recall: 0.9138
- F1: 0.8979
- Accuracy: 0.9518

## 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: 1e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0318        | 1.0   | 680  | 0.4800          | 0.8075    | 0.8632 | 0.8344 | 0.9183   |
| 0.0206        | 2.0   | 1360 | 0.4822          | 0.8332    | 0.8634 | 0.8480 | 0.9233   |
| 0.0116        | 3.0   | 2040 | 0.5227          | 0.8167    | 0.8683 | 0.8417 | 0.9211   |
| 0.0093        | 4.0   | 2720 | 0.5366          | 0.8230    | 0.8749 | 0.8482 | 0.9246   |
| 0.0077        | 5.0   | 3400 | 0.5384          | 0.8370    | 0.8688 | 0.8526 | 0.9249   |
| 0.0061        | 6.0   | 4080 | 0.5450          | 0.8418    | 0.8754 | 0.8583 | 0.9275   |
| 0.0048        | 7.0   | 4760 | 0.5570          | 0.8346    | 0.8765 | 0.8550 | 0.9262   |
| 0.0084        | 8.0   | 5440 | 0.5565          | 0.8353    | 0.8765 | 0.8554 | 0.9261   |
| 0.0073        | 9.0   | 6120 | 0.5693          | 0.8353    | 0.8751 | 0.8547 | 0.9261   |
| 0.0058        | 10.0  | 6800 | 0.5688          | 0.8361    | 0.8766 | 0.8559 | 0.9265   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1