---
base_model: NlpHUST/ner-vietnamese-electra-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_ner-token_classification_v1.0
  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. -->

# my_awesome_ner-token_classification_v1.0

This model is a fine-tuned version of [NlpHUST/ner-vietnamese-electra-base](https://huggingface.co/NlpHUST/ner-vietnamese-electra-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0322
- Precision: 0.4590
- Recall: 0.5400
- F1: 0.4963
- Accuracy: 0.7805

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8701        | 1.9929  | 562  | 0.8431          | 0.4537    | 0.4154 | 0.4337 | 0.7907   |
| 0.5651        | 3.9858  | 1124 | 0.7613          | 0.4524    | 0.4899 | 0.4704 | 0.7898   |
| 0.4312        | 5.9787  | 1686 | 0.8134          | 0.4654    | 0.5182 | 0.4904 | 0.7902   |
| 0.3305        | 7.9716  | 2248 | 0.8743          | 0.4417    | 0.5336 | 0.4833 | 0.7762   |
| 0.255         | 9.9645  | 2810 | 0.9331          | 0.4217    | 0.5375 | 0.4726 | 0.7694   |
| 0.2071        | 11.9574 | 3372 | 0.9707          | 0.4527    | 0.5435 | 0.4940 | 0.7795   |
| 0.1984        | 13.9504 | 3934 | 0.9967          | 0.4663    | 0.5336 | 0.4977 | 0.7834   |
| 0.1702        | 15.9433 | 4496 | 1.0322          | 0.4590    | 0.5400 | 0.4963 | 0.7805   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1