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vietnamese-ner

This model is a fine-tuned version of 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

Model description

More information needed

How to use

You can use this model with Transformers pipeline for NER.

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)

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: 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).