---
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: 0.1340
- Precision: 0.9582
- Recall: 0.9500
- F1: 0.9541
- Accuracy: 0.9499

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1595        | 0.5   | 7000  | 0.1539          | 0.9469    | 0.9377 | 0.9423 | 0.9375   |
| 0.1497        | 0.99  | 14000 | 0.1383          | 0.9549    | 0.9418 | 0.9483 | 0.9437   |
| 0.1185        | 1.49  | 21000 | 0.1314          | 0.9557    | 0.9464 | 0.9510 | 0.9467   |
| 0.1153        | 1.99  | 28000 | 0.1306          | 0.9553    | 0.9503 | 0.9528 | 0.9487   |
| 0.0977        | 2.49  | 35000 | 0.1340          | 0.9582    | 0.9500 | 0.9541 | 0.9499   |
| 0.0948        | 2.98  | 42000 | 0.1325          | 0.9584    | 0.9512 | 0.9548 | 0.9506   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2