---
base_model: NlpHUST/ner-vietnamese-electra-base
tags:
- generated_from_trainer
model-index:
- name: ner-education-hcmut
  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-education-hcmut

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: 0.1569
- Location: {'precision': 0.8888888888888888, 'recall': 0.6153846153846154, 'f1': 0.7272727272727274, 'number': 13}
- Miscellaneous: {'precision': 0.6255506607929515, 'recall': 0.7029702970297029, 'f1': 0.662004662004662, 'number': 202}
- Organization: {'precision': 0.723404255319149, 'recall': 0.7906976744186046, 'f1': 0.7555555555555555, 'number': 43}
- Person: {'precision': 0.3157894736842105, 'recall': 0.3157894736842105, 'f1': 0.3157894736842105, 'number': 19}
- Overall Precision: 0.6291
- Overall Recall: 0.6859
- Overall F1: 0.6563
- Overall Accuracy: 0.9587

## 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: 5e-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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Location                                                                                                 | Miscellaneous                                                                                            | Organization                                                                                            | Person                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log        | 1.0   | 220  | 0.1317          | {'precision': 0.6666666666666666, 'recall': 0.46153846153846156, 'f1': 0.5454545454545455, 'number': 13} | {'precision': 0.5213675213675214, 'recall': 0.6039603960396039, 'f1': 0.5596330275229358, 'number': 202} | {'precision': 0.38028169014084506, 'recall': 0.627906976744186, 'f1': 0.4736842105263158, 'number': 43} | {'precision': 0.23809523809523808, 'recall': 0.2631578947368421, 'f1': 0.25, 'number': 19}              | 0.4776            | 0.5776         | 0.5229     | 0.9504           |
| No log        | 2.0   | 440  | 0.1513          | {'precision': 0.6, 'recall': 0.46153846153846156, 'f1': 0.5217391304347826, 'number': 13}                | {'precision': 0.6008403361344538, 'recall': 0.7079207920792079, 'f1': 0.65, 'number': 202}               | {'precision': 0.6538461538461539, 'recall': 0.7906976744186046, 'f1': 0.7157894736842104, 'number': 43} | {'precision': 0.3333333333333333, 'recall': 0.3157894736842105, 'f1': 0.3243243243243243, 'number': 19} | 0.5943            | 0.6823         | 0.6353     | 0.9564           |
| 0.1164        | 3.0   | 660  | 0.1569          | {'precision': 0.8888888888888888, 'recall': 0.6153846153846154, 'f1': 0.7272727272727274, 'number': 13}  | {'precision': 0.6255506607929515, 'recall': 0.7029702970297029, 'f1': 0.662004662004662, 'number': 202}  | {'precision': 0.723404255319149, 'recall': 0.7906976744186046, 'f1': 0.7555555555555555, 'number': 43}  | {'precision': 0.3157894736842105, 'recall': 0.3157894736842105, 'f1': 0.3157894736842105, 'number': 19} | 0.6291            | 0.6859         | 0.6563     | 0.9587           |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1