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ZombitX64/MultiSent-E5-Pro

sourceHugging Facecc-by-nc-nd-4.0updated 1y agoView on Hugging Face

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Model Card

🎯 MultiSent-E5-Pro: Advanced Thai Sentiment Classifier

<strong>🇹🇭 State-of-the-art Thai sentiment analysis with multilingual capabilities</strong>

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📋 Quick Overview

MultiSent-E5-Pro is a fine-tuned sentiment analysis model based on intfloat/multilingual-e5-large, specially optimized for Thai with support for multilingual contexts. The model classifies text into four categories: Positive, Negative, Neutral, and Question.

🎯 Key Features

—Handles Thai-specific expressions, colloquialisms, and sarcasm effectively
—Performs well on real-world social media & review data
—Multilingual support for Southeast and East Asian languages

🏆 Benchmark Summary

Rank	Model	Accuracy	F1-Macro	Notes
🥇 1st	MultiSent-E5-Pro	84.61%	84.61%	Best overall
2nd	MultiSent-E5	80.62%	80.62%	Baseline model
3rd	sentiment-103	57.40%	49.87%	Moderate baseline

📊 Detailed Metrics (2,183 samples)

Metric	Score
Accuracy	84.61%
F1-Macro	84.61%
F1-Weighted	84.75%
Avg Confidence	98.53%
Low Confidence Rate (<60%)	0.96%

Per-Class Performance

Class	Precision	Recall	F1	Notes
Negative	91.0%	84.6%	87.7%	Excellent
Positive	83.0%	94.3%	88.3%	Excellent
Neutral	71.9%	81.6%	76.4%	Moderate
Question	94.4%	79.0%	86.0%	Good

⚡ Quick Start

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = "ZombitX64/MultiSent-E5-Pro"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForSequenceClassification.from_pretrained(model)

text = "ผลิตภัณฑ์นี้ดีมาก ใช้งานง่าย"
inputs = tokenizer(text, return_tensors="pt", truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted = torch.argmax(probs, dim=-1)

labels = ["Question", "Negative", "Neutral", "Positive"]
print(f"Sentiment: {labels[predicted.item()]} (Confidence: {probs[0][predicted].item():.2%})")

🌟 Use Cases

Application	Suitability
Product Reviews	🟢 Excellent
Social Media	🟢 Excellent
Customer Support	🟢 Excellent
Content Moderation	🟡 Good
Research Analysis	🟡 Good

⚠ Known Limitations

—Sarcasm Misclassification (especially in Chinese)
—Mixed Sentiments lean toward Neutral
—Low recall for Question class due to limited data
—Bias toward Positive due to class imbalance
—Overconfidence in some multilingual predictions

🛠 Technical Info

Config	Value
Base Model	multilingual-e5-large
Params	\~1.02B
Classes	4
Max Length	512
Training Time	\~27 min

Data Summary:

—Training: 2,456 samples
—Validation: 273 samples
—Evaluation: 2,183 samples

📄 Citation

bibtex

@misc{MultiSent-E5-Pro-2024,
  title={MultiSent-E5-Pro: Advanced Thai Sentiment Analysis},
  author={ZombitX64, Janutsaha K., Saengwichain C.},
  year={2024},
  url={https://huggingface.co/ZombitX64/MultiSent-E5-Pro},
  note={Hugging Face Model Card}
}

bibtex

@article{wang2024multilingual,
  title={Multilingual E5 Text Embeddings: A Technical Report},
  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
  journal={arXiv preprint arXiv:2402.05672},
  year={2024}
}

👨‍💼 Authors

Role	Name
Lead Dev	ZombitX64
Data Scientist	Krittanut Janutsaha
Engineer	Chanyut Saengwichain

😊 Feedback & Contributions

—💬 Open Discussion
—🐛 Report Issue
—🌟 Star the repo if useful!

<div align="center"> Last Updated: Dec 2024 | Version: 1.1 | Docs: v2.0 </div>