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

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1691
- Precision: 0.9011
- Recall: 0.8913
- F1: 0.8962
- Accuracy: 0.9696

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 13   | 1.3502          | 0.125     | 0.0217 | 0.0370 | 0.7293   |
| No log        | 2.0   | 26   | 0.8543          | 0.2432    | 0.0978 | 0.1395 | 0.8065   |
| No log        | 3.0   | 39   | 0.5831          | 0.4556    | 0.4457 | 0.4505 | 0.8652   |
| No log        | 4.0   | 52   | 0.4247          | 0.6495    | 0.6848 | 0.6667 | 0.9185   |
| No log        | 5.0   | 65   | 0.3273          | 0.7474    | 0.7717 | 0.7594 | 0.95     |
| No log        | 6.0   | 78   | 0.2706          | 0.8021    | 0.8370 | 0.8191 | 0.9587   |
| No log        | 7.0   | 91   | 0.2278          | 0.8804    | 0.8804 | 0.8804 | 0.9663   |
| No log        | 8.0   | 104  | 0.2166          | 0.8901    | 0.8804 | 0.8852 | 0.9663   |
| No log        | 9.0   | 117  | 0.2013          | 0.8804    | 0.8804 | 0.8804 | 0.9663   |
| No log        | 10.0  | 130  | 0.1881          | 0.8817    | 0.8913 | 0.8865 | 0.9674   |
| No log        | 11.0  | 143  | 0.1835          | 0.8817    | 0.8913 | 0.8865 | 0.9674   |
| No log        | 12.0  | 156  | 0.1754          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |
| No log        | 13.0  | 169  | 0.1740          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |
| No log        | 14.0  | 182  | 0.1676          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |
| No log        | 15.0  | 195  | 0.1660          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |
| No log        | 16.0  | 208  | 0.1678          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |
| No log        | 17.0  | 221  | 0.1692          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |
| No log        | 18.0  | 234  | 0.1701          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |
| No log        | 19.0  | 247  | 0.1692          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |
| No log        | 20.0  | 260  | 0.1691          | 0.9011    | 0.8913 | 0.8962 | 0.9696   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cpu
- Datasets 2.19.1
- Tokenizers 0.19.1