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

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2391
- Precision: 0.9245
- Recall: 0.9186
- F1: 0.9216
- Accuracy: 0.9168

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- 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        | 0.37  | 100  | 0.5115          | 0.8204    | 0.8719 | 0.8454 | 0.8200   |
| No log        | 0.75  | 200  | 0.3808          | 0.8684    | 0.8766 | 0.8725 | 0.8600   |
| No log        | 1.12  | 300  | 0.3315          | 0.8900    | 0.8865 | 0.8882 | 0.8799   |
| No log        | 1.49  | 400  | 0.3069          | 0.9036    | 0.8917 | 0.8976 | 0.8921   |
| 0.5306        | 1.87  | 500  | 0.2908          | 0.9066    | 0.8978 | 0.9022 | 0.8980   |
| 0.5306        | 2.24  | 600  | 0.2783          | 0.9114    | 0.9061 | 0.9087 | 0.9048   |
| 0.5306        | 2.61  | 700  | 0.2729          | 0.9151    | 0.9123 | 0.9137 | 0.9096   |
| 0.5306        | 2.99  | 800  | 0.2628          | 0.9157    | 0.9086 | 0.9121 | 0.9077   |
| 0.5306        | 3.36  | 900  | 0.2600          | 0.9207    | 0.9123 | 0.9165 | 0.9107   |
| 0.3037        | 3.73  | 1000 | 0.2539          | 0.9188    | 0.9134 | 0.9161 | 0.9110   |
| 0.3037        | 4.1   | 1100 | 0.2488          | 0.9229    | 0.9178 | 0.9203 | 0.9148   |
| 0.3037        | 4.48  | 1200 | 0.2449          | 0.9225    | 0.9170 | 0.9198 | 0.9146   |
| 0.3037        | 4.85  | 1300 | 0.2466          | 0.9230    | 0.9177 | 0.9203 | 0.9155   |
| 0.3037        | 5.22  | 1400 | 0.2415          | 0.9229    | 0.9188 | 0.9208 | 0.9161   |
| 0.2668        | 5.6   | 1500 | 0.2413          | 0.9237    | 0.9189 | 0.9213 | 0.9164   |
| 0.2668        | 5.97  | 1600 | 0.2391          | 0.9245    | 0.9186 | 0.9216 | 0.9168   |
| 0.2668        | 6.34  | 1700 | 0.2399          | 0.9245    | 0.9178 | 0.9211 | 0.9162   |
| 0.2668        | 6.72  | 1800 | 0.2369          | 0.9239    | 0.9181 | 0.9210 | 0.9164   |
| 0.2668        | 7.09  | 1900 | 0.2390          | 0.9239    | 0.9183 | 0.9211 | 0.9164   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2