Model Description
A BERT-based model trained to classify text as either Cantonese or Traditional Chinese.
Intended Use
- Primary Application: Language classification for Cantonese and Traditional Chinese texts.
- Users: NLP researchers, developers working with Chinese language data.
Training Data
Utilizes the "raptorkwok/cantonese-traditional-chinese-parallel-corpus" from Hugging Face Datasets.
Training Procedure
- Base Model:
bert-base-chinese
- Epochs: 3
- Learning Rate: 2e-5
How to Use
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ming030890/chinese-langid")
model = AutoModelForSequenceClassification.from_pretrained("ming030890/chinese-langid")
text = "係唔係廣東話?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
# 0 for Cantonese, 1 for Traditional Chinese
prediction = outputs.logits.argmax(-1).item()
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