---
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: 0.5707
- Precision: 0.8258
- Recall: 0.8753
- F1: 0.8498
- Accuracy: 0.9254

## 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1966        | 1.0   | 680   | 0.3961          | 0.7705    | 0.8096 | 0.7896 | 0.8971   |
| 0.1487        | 2.0   | 1360  | 0.4435          | 0.7607    | 0.8258 | 0.7919 | 0.8950   |
| 0.1258        | 3.0   | 2040  | 0.4180          | 0.7727    | 0.8450 | 0.8072 | 0.9052   |
| 0.1163        | 4.0   | 2720  | 0.4227          | 0.7890    | 0.8418 | 0.8145 | 0.9078   |
| 0.1019        | 5.0   | 3400  | 0.4721          | 0.7664    | 0.8630 | 0.8118 | 0.9068   |
| 0.0763        | 6.0   | 4080  | 0.4444          | 0.8033    | 0.8542 | 0.8280 | 0.9172   |
| 0.0641        | 7.0   | 4760  | 0.4551          | 0.8189    | 0.8550 | 0.8366 | 0.9179   |
| 0.0615        | 8.0   | 5440  | 0.4615          | 0.8167    | 0.8640 | 0.8397 | 0.9202   |
| 0.0468        | 9.0   | 6120  | 0.4922          | 0.8104    | 0.8668 | 0.8376 | 0.9205   |
| 0.0381        | 10.0  | 6800  | 0.4991          | 0.8213    | 0.8642 | 0.8422 | 0.9210   |
| 0.0381        | 11.0  | 7480  | 0.4950          | 0.8298    | 0.8593 | 0.8443 | 0.9230   |
| 0.0289        | 12.0  | 8160  | 0.5273          | 0.8220    | 0.8717 | 0.8461 | 0.9232   |
| 0.026         | 13.0  | 8840  | 0.5366          | 0.8207    | 0.8700 | 0.8446 | 0.9230   |
| 0.0227        | 14.0  | 9520  | 0.5466          | 0.8240    | 0.8698 | 0.8463 | 0.9230   |
| 0.02          | 15.0  | 10200 | 0.5530          | 0.8250    | 0.8710 | 0.8474 | 0.9247   |
| 0.0173        | 16.0  | 10880 | 0.5571          | 0.8235    | 0.8726 | 0.8473 | 0.9244   |
| 0.0152        | 17.0  | 11560 | 0.5551          | 0.8282    | 0.8700 | 0.8486 | 0.9248   |
| 0.0146        | 18.0  | 12240 | 0.5686          | 0.8270    | 0.8727 | 0.8492 | 0.9246   |
| 0.0142        | 19.0  | 12920 | 0.5687          | 0.8258    | 0.8753 | 0.8498 | 0.9259   |
| 0.0135        | 20.0  | 13600 | 0.5707          | 0.8258    | 0.8753 | 0.8498 | 0.9254   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1