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---
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language:
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- en
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- ar
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- zh
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- nl
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- fr
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- ru
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- es
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- tr
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tags:
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- multilingual-sentiment-analysis
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- sentiment-analysis
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- aspect-based-sentiment-analysis
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- deberta
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- pyabsa
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- efficient
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- lightweight
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- production-ready
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- no-llm
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license: mit
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pipeline_tag: text-classification
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widget:
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- text: >-
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The user interface is brilliant, but the documentation is a total mess.
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[SEP] user interface [SEP]
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- text: >-
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The user interface is brilliant, but the documentation is a total mess.
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[SEP] documentation [SEP]
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---
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# State-of-the-Art Multilingual Sentiment Analysis
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## Multilingual -> English, Chinese, Arabic, Dutch, French, Russian, Spanish, Turkish, etc.
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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.
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**`deberta-v3-base-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.
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### Why This Model?
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- **🎯 Wide Usage:** This model reaches **one million downloads** already! (Maybe) the most downloaded open-source ABSA model ever.
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- **🏆 SOTA Performance:** Built on the powerful `DeBERTa-v3` architecture and fine-tuned with advanced, context-aware methods from [PyABSA](https://github.com/yangheng95/PyABSA), this model achieves top-tier accuracy on complex sentiment tasks.
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- **⚡ 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.
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- **💰 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.
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- **🚀 Production-Ready:** Lightweight, fast, and reliable. This model is built to be deployed at scale for applications that demand immediate and accurate sentiment feedback.
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### Ideal Use Cases
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This model excels where speed, cost, and precision are critical:
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- **Real-time Social Media Monitoring:** Analyze brand sentiment towards specific product features as it happens.
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- **Intelligent Customer Support:** Automatically route tickets based on the sentiment towards different aspects of a complaint.
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- **Product Review Analysis:** Aggregate fine-grained feedback on thousands of reviews to identify precise strengths and weaknesses.
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- **Market Intelligence:** Understand nuanced public opinion on key industry topics.
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## How to Use
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Getting started is incredibly simple. You can use the Hugging Face `pipeline` for a zero-effort implementation.
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from transformers import pipeline
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### Load the classifier pipeline - it's that easy.
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```python
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classifier = pipeline("text-classification", model="yangheng/deberta-v3-base-absa-v1.1")
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sentence = "The food was exceptional, although the service was a bit slow."
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```
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### Analyze sentiment for the 'food' aspect
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```python
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result_food = classifier(sentence, text_pair="food")
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result_food ->
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{
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'Negative': 0.989
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'Neutral': 0.008
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'Positive': 0.003
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}
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```
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### Analyze sentiment for the Chinese texts.
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```python
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result_service = classifier("这部手机的性能差劲", text_pair="性能")
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result_service = classifier("这台汽车的引擎推力强劲", text_pair="引擎")
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```
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## Using PyABSA for End-to-End Analysis
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For a more powerful, end-to-end solution that handles both aspect term extraction and sentiment classification in a single call, you can use the PyABSA library. This is the very framework used to train and optimize this model.
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First, install PyABSA:
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```bash
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pip install pyabsa
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```
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Then, you can perform inference like this. The model will automatically find the aspects in the text and classify their sentiment.
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```python3
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from pyabsa import AspectTermExtraction as ATEPC, available_checkpoints
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# Load the model directly from Hugging Face Hub
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aspect_extractor = ATEPC.AspectExtractor(
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'multilingual', # Can be replaced with a specific checkpoint name or a local file path
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auto_device=True, # Use GPU/CPU or Auto
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cal_perplexity=True # Calculate text perplexity
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)
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texts = [
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"这家餐厅的牛排很好吃，但是服务很慢。",
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"The battery life is terrible but the camera is excellent."
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]
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# Perform end-to-end aspect-based sentiment analysis
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result = aspect_extractor.predict(
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texts,
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print_result=True, # Console Printing
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save_result=False, # Save results into a json file
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ignore_error=True, # Exception handling for error cases
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pred_sentiment=True # Predict sentiment for extracted aspects
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)
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# The output automatically identifies aspects and their corresponding sentiments:
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# {
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# "text": "The user interface is brilliant, but the documentation is a total mess.",
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# "aspect": ["user interface", "documentation"],
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# "position": [[4, 19], [41, 54]],
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# "sentiment": ["Positive", "Negative"],
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# "probability": [[1e-05, 0.0001, 0.9998], [0.9998, 0.0001, 1e-05]],
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# "confidence": [0.9997, 0.9997]
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# }
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```
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Find more solutions for ABSA tasks in PyASBA.
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## The Technology Behind the Performance
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### Base Model
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It starts with `microsoft/deberta-v3-base`, a highly optimized encoder known for its disentangled attention mechanism, which improves efficiency and performance over original BERT/RoBERTa models.
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### Fine-Tuning Architecture
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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.
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### Training Data
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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.
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## Citation
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If you use this model in your research or application, please cite the foundational work on the PyABSA framework.
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### BibTeX Citation
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```bibtex
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@inproceedings{YangCL23PyABSA,
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author = {Heng Yang and Chen Zhang and Ke Li},
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title = {PyABSA: {A} Modularized Framework for Reproducible Aspect-based Sentiment Analysis},
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booktitle = {Proceedings of the 32nd {ACM} International Conference on Information and Knowledge Management, {CIKM} 2023},
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pages = {5117--5122},
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publisher = {{ACM}},
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year = {2023},
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doi = {10.1145/3583780.3614752}
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}
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@inproceedings{YangL24LCF/LCA,
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author = {Heng Yang and
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Ke Li},
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editor = {Yvette Graham and
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Matthew Purver},
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title = {Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation},
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booktitle = {Findings of the Association for Computational Linguistics: {EACL}
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2024, St. Julian's, Malta, March 17-22, 2024},
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pages = {182--195},
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publisher = {Association for Computational Linguistics},
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year = {2024},
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url = {https://aclanthology.org/2024.findings-eacl.13},
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timestamp = {Tue, 23 Jul 2024 08:21:59 +0200},
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biburl = {https://dblp.org/rec/conf/eacl/YangL24.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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``` |
