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

# bert-NER

This model is a fine-tuned version of [dslim/bert-large-NER](https://huggingface.co/dslim/bert-large-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3706
- Precision: 0.5564
- Recall: 0.6510
- F1: 0.6
- Accuracy: 0.9117

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4407        | 1.0   | 746  | 0.3447          | 0.4835    | 0.5572 | 0.5177 | 0.8881   |
| 0.245         | 2.0   | 1492 | 0.3399          | 0.5439    | 0.5630 | 0.5533 | 0.9014   |
| 0.0865        | 3.0   | 2238 | 0.3706          | 0.5564    | 0.6510 | 0.6    | 0.9117   |


### Framework versions

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