---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-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-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: 0.3788
- Precision: 0.5395
- Recall: 0.5234
- F1: 0.5313
- Accuracy: 0.9307

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 121  | 0.4099          | 0.2393    | 0.2383 | 0.2388 | 0.8962   |
| No log        | 2.0   | 242  | 0.3394          | 0.4340    | 0.3220 | 0.3697 | 0.9180   |
| No log        | 3.0   | 363  | 0.2952          | 0.5017    | 0.4170 | 0.4555 | 0.9271   |
| No log        | 4.0   | 484  | 0.3419          | 0.5301    | 0.4    | 0.4559 | 0.9284   |
| 0.321         | 5.0   | 605  | 0.3269          | 0.5354    | 0.4723 | 0.5019 | 0.9313   |
| 0.321         | 6.0   | 726  | 0.3382          | 0.5091    | 0.4780 | 0.4931 | 0.9285   |
| 0.321         | 7.0   | 847  | 0.3528          | 0.5489    | 0.5177 | 0.5328 | 0.9315   |
| 0.321         | 8.0   | 968  | 0.3623          | 0.5446    | 0.5191 | 0.5316 | 0.9306   |
| 0.0997        | 9.0   | 1089 | 0.3706          | 0.5225    | 0.5262 | 0.5244 | 0.9283   |
| 0.0997        | 10.0  | 1210 | 0.3788          | 0.5395    | 0.5234 | 0.5313 | 0.9307   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1