---
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: 0.1966
- Precision: 0.6438
- Recall: 0.6483
- F1: 0.6460
- Accuracy: 0.9478

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4628        | 1.0   | 551  | 0.2481          | 0.5940    | 0.5448 | 0.5683 | 0.9326   |
| 0.2106        | 2.0   | 1102 | 0.1833          | 0.6486    | 0.6621 | 0.6553 | 0.9433   |
| 0.1434        | 3.0   | 1653 | 0.1966          | 0.6438    | 0.6483 | 0.6460 | 0.9478   |


### Framework versions

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