---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: retrained_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. -->

# retrained_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: 0.0829
- Precision: 0.9435
- Recall: 0.9497
- F1: 0.9466
- Accuracy: 0.9868

## 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: 5e-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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1292        | 1.0   | 878  | 0.0580          | 0.9201    | 0.9355 | 0.9277 | 0.9836   |
| 0.0394        | 2.0   | 1756 | 0.0613          | 0.9283    | 0.9415 | 0.9349 | 0.9847   |
| 0.0207        | 3.0   | 2634 | 0.0635          | 0.9398    | 0.9490 | 0.9444 | 0.9865   |
| 0.0117        | 4.0   | 3512 | 0.0688          | 0.9363    | 0.9455 | 0.9409 | 0.9857   |
| 0.0074        | 5.0   | 4390 | 0.0691          | 0.9416    | 0.9480 | 0.9448 | 0.9864   |
| 0.0043        | 6.0   | 5268 | 0.0803          | 0.9356    | 0.9466 | 0.9411 | 0.9861   |
| 0.0035        | 7.0   | 6146 | 0.0801          | 0.9435    | 0.9508 | 0.9471 | 0.9870   |
| 0.0021        | 8.0   | 7024 | 0.0825          | 0.9394    | 0.9491 | 0.9442 | 0.9860   |
| 0.0015        | 9.0   | 7902 | 0.0800          | 0.9421    | 0.9489 | 0.9455 | 0.9865   |
| 0.001         | 10.0  | 8780 | 0.0829          | 0.9435    | 0.9497 | 0.9466 | 0.9868   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3