---
library_name: transformers
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3910
- Precision: 0.9616
- Recall: 0.9637
- F1: 0.9627
- Accuracy: 0.9560

## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3052        | 1.0   | 3334  | 0.2630          | 0.9365    | 0.9367 | 0.9366 | 0.9228   |
| 0.2104        | 2.0   | 6668  | 0.2481          | 0.9418    | 0.9537 | 0.9477 | 0.9400   |
| 0.163         | 3.0   | 10002 | 0.2390          | 0.9495    | 0.9606 | 0.9550 | 0.9479   |
| 0.1151        | 4.0   | 13336 | 0.2516          | 0.9549    | 0.9616 | 0.9583 | 0.9515   |
| 0.0809        | 5.0   | 16670 | 0.2887          | 0.9590    | 0.9556 | 0.9573 | 0.9493   |
| 0.0625        | 6.0   | 20004 | 0.2912          | 0.9573    | 0.9611 | 0.9592 | 0.9520   |
| 0.0516        | 7.0   | 23338 | 0.3139          | 0.9581    | 0.9563 | 0.9572 | 0.9501   |
| 0.0388        | 8.0   | 26672 | 0.3070          | 0.9605    | 0.9600 | 0.9602 | 0.9531   |
| 0.0273        | 9.0   | 30006 | 0.3344          | 0.9607    | 0.9617 | 0.9612 | 0.9535   |
| 0.0252        | 10.0  | 33340 | 0.3547          | 0.9608    | 0.9638 | 0.9623 | 0.9554   |
| 0.0242        | 11.0  | 36674 | 0.3726          | 0.9600    | 0.9619 | 0.9610 | 0.9541   |
| 0.0119        | 12.0  | 40008 | 0.3727          | 0.9602    | 0.9623 | 0.9612 | 0.9546   |
| 0.0078        | 13.0  | 43342 | 0.3772          | 0.9617    | 0.9639 | 0.9628 | 0.9562   |
| 0.0078        | 14.0  | 46676 | 0.3904          | 0.9615    | 0.9638 | 0.9627 | 0.9560   |
| 0.0026        | 15.0  | 50010 | 0.3910          | 0.9616    | 0.9637 | 0.9627 | 0.9560   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1