---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-ner-2
  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-ner-2

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: 0.7358
- Precision: 0.1646
- Recall: 0.4605
- F1: 0.2425
- Accuracy: 0.8784

## 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   | 149  | 0.5651          | 0.1347    | 0.4192 | 0.2038 | 0.8686   |
| No log        | 2.0   | 298  | 0.5818          | 0.1440    | 0.4227 | 0.2148 | 0.8785   |
| No log        | 3.0   | 447  | 0.6011          | 0.1432    | 0.3986 | 0.2107 | 0.8808   |
| 0.0328        | 4.0   | 596  | 0.5546          | 0.1613    | 0.3986 | 0.2297 | 0.8955   |
| 0.0328        | 5.0   | 745  | 0.7685          | 0.1371    | 0.4467 | 0.2098 | 0.8600   |
| 0.0328        | 6.0   | 894  | 0.7755          | 0.1486    | 0.4570 | 0.2243 | 0.8686   |
| 0.0102        | 7.0   | 1043 | 0.6831          | 0.1669    | 0.4570 | 0.2445 | 0.8834   |
| 0.0102        | 8.0   | 1192 | 0.7698          | 0.1524    | 0.4639 | 0.2294 | 0.8715   |
| 0.0102        | 9.0   | 1341 | 0.7303          | 0.1681    | 0.4708 | 0.2477 | 0.8791   |
| 0.0102        | 10.0  | 1490 | 0.7358          | 0.1646    | 0.4605 | 0.2425 | 0.8784   |


### Framework versions

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