🚀 Vietnamese Named Entity Recognition (NER) Model
This is a Vietnamese Named Entity Recognition (NER) model. It is a fine - tuned version of [NlpHUST/electra - base - vn](https://huggingface.co/NlpHUST/electra - base - vn), which can effectively identify named entities in Vietnamese text.
🚀 Quick Start
This model is a fine - tuned version of [NlpHUST/electra - base - vn](https://huggingface.co/NlpHUST/electra - base - vn) on an VLSP 2018 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0580
- Location Precision: 0.9353
- Location Recall: 0.9377
- Location F1: 0.9365
- Location Number: 2360
- Miscellaneous Precision: 0.5660
- Miscellaneous Recall: 0.6897
- Miscellaneous F1: 0.6218
- Miscellaneous Number: 174
- Organization Precision: 0.8610
- Organization Recall: 0.9068
- Organization F1: 0.8833
- Organization Number: 1878
- Person Precision: 0.9692
- Person Recall: 0.9637
- Person F1: 0.9664
- Person Number: 2121
- Overall Precision: 0.9122
- Overall Recall: 0.9307
- Overall F1: 0.9214
- Overall Accuracy: 0.9907
✨ Features
- Fine - tuned Model: Based on [NlpHUST/electra - base - vn](https://huggingface.co/NlpHUST/electra - base - vn), it is fine - tuned on the VLSP 2018 dataset.
- Multiple Entity Types: Can identify various named entities such as locations, organizations, persons, and miscellaneous entities.
- High Performance: Achieves high precision, recall, and F1 scores on the evaluation set.
💻 Usage Examples
Basic Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("NlpHUST/ner - vietnamese - electra - base")
model = AutoModelForTokenClassification.from_pretrained("NlpHUST/ner - vietnamese - electra - base")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Liên quan vụ việc CSGT bị tố đánh dân, trúng một cháu nhỏ đang ngủ, đang lan truyền trên mạng xã hội, Đại tá Nguyễn Văn Tảo, Phó Giám đốc Công an tỉnh Tiền Giang vừa có cuộc họp cùng Chỉ huy Công an huyện Châu Thành và một số đơn vị nghiệp vụ cấp tỉnh để chỉ đạo làm rõ thông tin."
ner_results = nlp(example)
print(ner_results)
📚 Documentation
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
🔧 Technical Details
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e - 05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- num_epochs: 10.0
Framework versions
- Transformers 4.20.1
- Pytorch 1.8.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
Contact information
For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).
📄 License
No license information provided in the original document.
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