README.md

metadata

language:
  - en
  - zh
tags:
  - token-classification
  - aspect-based-sentiment-analysis
  - sentiment-analysis
  - sequence-labeling
  - transformers
  - deberta
license: mit
pipeline_tag: token-classification
widget:
  - text: The user interface is brilliant, but the documentation is a total mess.
  - text: 这家餐厅的牛排很好吃，但是服务很慢。

Multilingual End-to-End Aspect-based Sentiment Analysis

This model performs end-to-end Aspect-Based Sentiment Analysis (ABSA) by jointly extracting aspect terms and their sentiments via a single token-classification head. Labels are merged as IOB-with-sentiment, e.g. B-ASP-Positive, I-ASP-Negative, or O for non-aspect tokens.

What it does

• Detects aspect terms as spans in text
• Assigns a sentiment for each detected aspect (Positive/Negative/Neutral)
• Returns character-level offsets (start, end) in the original input

How to use (Transformers pipeline)

from transformers import pipeline

nlp = pipeline(
    "token-classification",
    model="yangheng/deberta-v3-base-end2end-absa",  # replace with your repo id
    aggregation_strategy="simple",  # aggregates sub-tokens into word-level entities
)

text = "The user interface is brilliant, but the documentation is a total mess."
preds = nlp(text)
print(preds)
# Example entity structure:
# [{
#   'entity_group': 'B-ASP-Positive',
#   'word': 'user interface',
#   'start': 4,
#   'end': 19,
#   'score': 0.98
# }, {
#   'entity_group': 'B-ASP-Negative',
#   'word': 'documentation',
#   'start': 41,
#   'end': 54,
#   'score': 0.99
# }]

Convert entities to aspect-level results

def postprocess_entities(entities):
    aspects = []
    for ent in entities:
        label = ent["entity_group"]  # e.g. B-ASP-Positive or I-ASP-Positive
        parts = label.split("-")
        # Expected formats: O, B-ASP-<SENT>, I-ASP-<SENT>
        if label == "O":
            continue
        prefix, _, sentiment = parts[0], parts[1], parts[2]
        aspects.append({
            "aspect": ent["word"],
            "sentiment": sentiment,
            "start": int(ent["start"]),
            "end": int(ent["end"]),
            "score": float(ent.get("score", 0.0)),
        })
    return aspects

aspects = postprocess_entities(preds)
print(aspects)

Enhanced Sentiment classification

The aspect sentiment analysis performance can be improved by the joint aspect term extraction and aspect sentiment classification. Find the example here

FastAPI serving (optional)

You can deploy a simple REST service using FastAPI:

python serve_singlehead_api.py --model yangheng/deberta-v3-base-end2end-absa --host 0.0.0.0 --port 8000

Predict:

curl -X POST http://localhost:8000/predict \
  -H "Content-Type: application/json" \
  -d '{"text":"The user interface is brilliant, but the documentation is a total mess."}'

Notes:

• start/end are character offsets in the original string.
• aggregation_strategy='simple' merges sub-tokens into word-level spans. Set to none|first|average|max as needed.

Model details

• Base model: microsoft/deberta-v3-base
• Task: Token classification with merged labels: O, B-ASP-{Positive|Negative|Neutral}, I-ASP-{Positive|Negative|Neutral}
• Training: Fine-tuned with Hugging Face Transformers. The model config includes id2label/label2id for native pipeline compatibility and Hub Inference API.

Limitations

• Long texts are truncated to the maximum sequence length of the model (typically 512). Adjust during training/inference if required.
• Sentiments limited to Positive/Negative/Neutral unless retrained with extended schema.

License

MIT