---
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: 0.0985
- Location: {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6}
- Miscellaneous: {'precision': 0.6069651741293532, 'recall': 0.7176470588235294, 'f1': 0.6576819407008085, 'number': 170}
- Organization: {'precision': 0.4166666666666667, 'recall': 0.5769230769230769, 'f1': 0.48387096774193544, 'number': 26}
- Person: {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10}
- Overall Precision: 0.5863
- Overall Recall: 0.6887
- Overall F1: 0.6334
- Overall Accuracy: 0.9702

## 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   | 269  | 0.1088          | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6}               | {'precision': 0.46311475409836067, 'recall': 0.6647058823529411, 'f1': 0.5458937198067633, 'number': 170} | {'precision': 0.3333333333333333, 'recall': 0.46153846153846156, 'f1': 0.3870967741935484, 'number': 26}  | {'precision': 0.6666666666666666, 'recall': 0.6, 'f1': 0.631578947368421, 'number': 10} | 0.4573            | 0.6321         | 0.5307     | 0.9631           |
| 0.1453        | 2.0   | 538  | 0.0948          | {'precision': 1.0, 'recall': 0.5, 'f1': 0.6666666666666666, 'number': 6} | {'precision': 0.5525114155251142, 'recall': 0.711764705882353, 'f1': 0.622107969151671, 'number': 170}    | {'precision': 0.42424242424242425, 'recall': 0.5384615384615384, 'f1': 0.47457627118644075, 'number': 26} | {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10}              | 0.5475            | 0.6792         | 0.6063     | 0.9654           |
| 0.1453        | 3.0   | 807  | 0.0985          | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6}               | {'precision': 0.6069651741293532, 'recall': 0.7176470588235294, 'f1': 0.6576819407008085, 'number': 170}  | {'precision': 0.4166666666666667, 'recall': 0.5769230769230769, 'f1': 0.48387096774193544, 'number': 26}  | {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10}              | 0.5863            | 0.6887         | 0.6334     | 0.9702           |


### Framework versions

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