README.md

---
language:
- en
- ar
- zh
- nl
- fr
- ru
- es
- tr
tags:
- sentiment-analysis
- text-classification
- aspect-based-sentiment-analysis
- deberta
- pyabsa
- efficient
- lightweight
- production-ready
- no-llm
license: mit
pipeline_tag: text-classification
widget:
- text: >-
    The user interface is brilliant, but the documentation is a total mess.
    [SEP] user interface [SEP]
- text: >-
    The user interface is brilliant, but the documentation is a total mess.
    [SEP] documentation [SEP]
---

# State-of-the-Art Multilingual Sentiment Analysis 

## Multilingual -> English, Chinese, Arabic, Dutch, French, Russian, Spanish, Turkish, etc.

Tired of the high costs, slow latency, and massive computational footprint of Large Language Models? This is the sentiment analysis model you've been waiting for.

**`deberta-v3-large-absa-v1.1`** delivers **state-of-the-art accuracy** for fine-grained sentiment analysis with the speed, efficiency, and simplicity of a classic encoder model. It represents a paradigm shift in production-ready AI: maximum performance with minimum operational burden.

### Why This Model?.
- **🎯 Wide Usage:** This model reaches **One million downloads** already! (Maybe) the most downloaded open-source ABSA model ever.
- **🏆 SOTA Performance:** Built on the powerful `DeBERTa-v3` architecture and fine-tuned with advanced, context-aware methods from PyABSA, this model achieves top-tier accuracy on complex sentiment tasks.
- **⚡ LLM-Free Efficiency:** No need for A100s or massive GPU clusters. This model runs inference at a fraction of the computational cost, enabling real-time performance on standard CPUs or modest GPUs.
- **💰 Lower Costs:** Slash your hosting and API call expenses. The small footprint and high efficiency translate directly to significant savings, whether you're a startup or an enterprise.
- **🚀 Production-Ready:** Lightweight, fast, and reliable. This model is built to be deployed at scale for applications that demand immediate and accurate sentiment feedback.

### Ideal Use Cases

This model excels where speed, cost, and precision are critical:

- **Real-time Social Media Monitoring:** Analyze brand sentiment towards specific product features as it happens.
- **Intelligent Customer Support:** Automatically route tickets based on the sentiment towards different aspects of a complaint.
- **Product Review Analysis:** Aggregate fine-grained feedback on thousands of reviews to identify precise strengths and weaknesses.
- **Market Intelligence:** Understand nuanced public opinion on key industry topics.

## How to Use

Getting started is incredibly simple. You can use the Hugging Face `pipeline` for a zero-effort implementation.


from transformers import pipeline

# Load the classifier pipeline - it's that easy.
```python
classifier = pipeline("text-classification", model="yangheng/deberta-v3-large-absa-v1.1")
sentence = "The food was exceptional, although the service was a bit slow."
```
# Analyze sentiment for the 'food' aspect
```python
result_food = classifier(sentence, text_pair="food")
result_food ->
{
  'Negative': 0.989
  'Neutral': 0.008
  'Positive': 0.003
}
```
# Analyze sentiment for the 'service' aspect from the same sentence
```python
result_service = classifier("这部手机的性能差劲", text_pair="性能")
result_service = classifier("这台汽车的引擎推力强劲", text_pair="引擎")
```
## The Technology Behind the Performance

### Base Model

It starts with `microsoft/deberta-v3-large`, a highly optimized encoder known for its disentangled attention mechanism, which improves efficiency and performance over original BERT/RoBERTa models.

### Fine-Tuning Architecture

It employs the FAST-LCF-BERT backbone trained from the PyABSA framework. This introduces a Local Context Focus (LCF) layer that dynamically guides the model to concentrate on the words and phrases most relevant to the given aspect, dramatically improving contextual understanding and accuracy.

### Training Data

This model was trained on a robust, aggregated corpus of over 30,000 unique samples (augmented to ~180,000 examples) from canonical ABSA datasets, including SemEval-2014, SemEval-2016, MAMS, and more. The standard test sets were excluded to ensure fair and reliable benchmarking.

## Citation

If you use this model in your research or application, please cite the foundational work on the PyABSA framework.

### BibTeX Citation

```bibtex
@inproceedings{DBLP:conf/cikm/0008ZL23,
  author       = {Heng Yang and Chen Zhang and Ke Li},
  title        = {PyABSA: {A} Modularized Framework for Reproducible Aspect-based Sentiment Analysis},
  booktitle    = {Proceedings of the 32nd {ACM} International Conference on Information and Knowledge Management, {CIKM} 2023},
  pages        = {5117--5122},
  publisher    = {{ACM}},
  year         = {2023},
  doi          = {10.1145/3583780.3614752}
}

@article{YangZMT21,
  author       = {Heng Yang and Biqing Zeng and Mayi Xu and Tianxing Wang},
  title        = {Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable Sentiment Dependency Learning},
  journal      = {CoRR},
  volume       = {abs/2110.08604},
  year         = {2021},
  url          = {https://arxiv.org/abs/2110.08604},
}