---
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-fine-tune-bert-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. -->

# ner-fine-tune-bert-ner

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3662
- Precision: 0.2383
- Recall: 0.2818
- F1: 0.2582
- Accuracy: 0.9406

## 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: 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 122  | 0.2295          | 0.1255    | 0.0716 | 0.0912 | 0.9514   |
| No log        | 2.0   | 244  | 0.2152          | 0.2022    | 0.1270 | 0.1560 | 0.9514   |
| No log        | 3.0   | 366  | 0.2044          | 0.1696    | 0.1547 | 0.1618 | 0.9497   |
| No log        | 4.0   | 488  | 0.2269          | 0.1980    | 0.1363 | 0.1614 | 0.9536   |
| 0.2142        | 5.0   | 610  | 0.2335          | 0.1931    | 0.1547 | 0.1718 | 0.9521   |
| 0.2142        | 6.0   | 732  | 0.2516          | 0.1959    | 0.1778 | 0.1864 | 0.9491   |
| 0.2142        | 7.0   | 854  | 0.2446          | 0.2565    | 0.2517 | 0.2541 | 0.9542   |
| 0.2142        | 8.0   | 976  | 0.2527          | 0.2273    | 0.2656 | 0.2449 | 0.9481   |
| 0.0658        | 9.0   | 1098 | 0.2724          | 0.2459    | 0.2055 | 0.2239 | 0.9526   |
| 0.0658        | 10.0  | 1220 | 0.2620          | 0.2895    | 0.2748 | 0.2820 | 0.9549   |
| 0.0658        | 11.0  | 1342 | 0.2846          | 0.2102    | 0.2748 | 0.2382 | 0.9416   |
| 0.0658        | 12.0  | 1464 | 0.2943          | 0.2292    | 0.2610 | 0.2441 | 0.9450   |
| 0.0273        | 13.0  | 1586 | 0.3154          | 0.2064    | 0.2679 | 0.2332 | 0.9381   |
| 0.0273        | 14.0  | 1708 | 0.3097          | 0.2254    | 0.2217 | 0.2235 | 0.9464   |
| 0.0273        | 15.0  | 1830 | 0.3313          | 0.2375    | 0.2517 | 0.2444 | 0.9426   |
| 0.0273        | 16.0  | 1952 | 0.3256          | 0.2098    | 0.2864 | 0.2422 | 0.9361   |
| 0.0155        | 17.0  | 2074 | 0.3333          | 0.2162    | 0.2656 | 0.2383 | 0.9393   |
| 0.0155        | 18.0  | 2196 | 0.3073          | 0.2446    | 0.2864 | 0.2638 | 0.9449   |
| 0.0155        | 19.0  | 2318 | 0.3241          | 0.2418    | 0.2725 | 0.2562 | 0.9437   |
| 0.0155        | 20.0  | 2440 | 0.3348          | 0.2338    | 0.2587 | 0.2456 | 0.9446   |
| 0.0091        | 21.0  | 2562 | 0.3595          | 0.234     | 0.2702 | 0.2508 | 0.9402   |
| 0.0091        | 22.0  | 2684 | 0.3658          | 0.2263    | 0.2818 | 0.2510 | 0.9387   |
| 0.0091        | 23.0  | 2806 | 0.3495          | 0.2391    | 0.2794 | 0.2577 | 0.9419   |
| 0.0091        | 24.0  | 2928 | 0.3545          | 0.2398    | 0.2841 | 0.2600 | 0.9409   |
| 0.0066        | 25.0  | 3050 | 0.3557          | 0.2309    | 0.2864 | 0.2557 | 0.9402   |
| 0.0066        | 26.0  | 3172 | 0.3498          | 0.2449    | 0.2748 | 0.2590 | 0.9432   |
| 0.0066        | 27.0  | 3294 | 0.3586          | 0.2375    | 0.2841 | 0.2587 | 0.9416   |
| 0.0066        | 28.0  | 3416 | 0.3676          | 0.2389    | 0.2725 | 0.2546 | 0.9417   |
| 0.005         | 29.0  | 3538 | 0.3663          | 0.2412    | 0.2864 | 0.2619 | 0.9404   |
| 0.005         | 30.0  | 3660 | 0.3662          | 0.2383    | 0.2818 | 0.2582 | 0.9406   |


### Framework versions

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