Phoranker

🚀 PhoRanker: A Cross-encoder Model for Vietnamese Text Ranking

PhoRanker is a cross-encoder model designed for Vietnamese text ranking, offering high performance on relevant datasets.

🚀 Quick Start

This section provides a quick guide on how to install and use PhoRanker.

✨ Features

• Cross-encoder: Ideal for text classification and reranking tasks.
• Vietnamese Support: Specifically trained for Vietnamese text.
• High Performance: Demonstrates excellent results on the MS MMarco Passage Reranking - Vi - Dev dataset.

📦 Installation

• Install VnCoreNLP for word segmentation:
  • pip install py_vncorenlp
• Install sentence-transformers (recommended) - Usage:
  • pip install sentence-transformers
• Install transformers (optional) - Usage:
  • pip install transformers

💻 Usage Examples

Basic Usage

Before using the model, you need to perform pre - processing on the text.

import py_vncorenlp
py_vncorenlp.download_model(save_dir='/absolute/path/to/vncorenlp')
rdrsegmenter = py_vncorenlp.VnCoreNLP(annotators=["wseg"], save_dir='/absolute/path/to/vncorenlp')

query = "Trường UIT là gì?"
sentences = [
    "Trường Đại học Công nghệ Thông tin có tên tiếng Anh là University of Information Technology (viết tắt là UIT) là thành viên của Đại học Quốc Gia TP.HCM.",
    "Trường Đại học Kinh tế – Luật (tiếng Anh: University of Economics and Law – UEL) là trường đại học đào tạo và nghiên cứu khối ngành kinh tế, kinh doanh và luật hàng đầu Việt Nam.",
    "Quĩ uỷ thác đầu tư (tiếng Anh: Unit Investment Trusts; viết tắt: UIT) là một công ty đầu tư mua hoặc nắm giữ một danh mục đầu tư cố định"
]

tokenized_query = rdrsegmenter.word_segment(query)
tokenized_sentences = [rdrsegmenter.word_segment(sent) for sent in sentences]

tokenized_pairs = [[tokenized_query, sent] for sent in tokenized_sentences]

MODEL_ID = 'itdainb/PhoRanker'
MAX_LENGTH = 256

Usage with sentence-transformers

from sentence_transformers import CrossEncoder
model = CrossEncoder(MODEL_ID, max_length=MAX_LENGTH)

# For fp16 usage
model.model.half()

scores = model.predict(tokenized_pairs)

# 0.982, 0.2444, 0.9253
print(scores)

Usage with transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# For fp16 usage
model.half()

features = tokenizer(tokenized_pairs, padding=True, truncation="longest_first", return_tensors="pt", max_length=MAX_LENGTH)

model.eval()
with torch.no_grad():
    model_predictions = model(**features, return_dict=True)

    logits = model_predictions.logits
    logits = torch.nn.Sigmoid()(logits)
    scores = [logit[0] for logit in logits]

# 0.9819, 0.2444, 0.9253
print(scores)

📚 Documentation

Performance

In the following table, we provide various pre - trained Cross - Encoders together with their performance on the MS MMarco Passage Reranking - Vi - Dev dataset.

Note: Runtime was computed on a A100 GPU with fp16.

🔧 Technical Details

PhoRanker is a cross - encoder model for Vietnamese text ranking. It is trained on the unicamp-dl/mmarco dataset, achieving high performance in text classification and reranking tasks.

📄 License

This project is licensed under the Apache - 2.0 license.

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Citation

Please cite as

@misc{PhoRanker,
  title={PhoRanker: A Cross-encoder Model for Vietnamese Text Ranking},
  author={Dai Nguyen Ba ({ORCID:0009-0008-8559-3154})},
  year={2024},
  publisher={Huggingface},
  journal={huggingface repository},
  howpublished={\url{https://huggingface.co/itdainb/PhoRanker}},
}