This model is a fine-tuned version of yangheng/deberta-v3-base-end2end-absa for Aspect-Based Sentiment Analysis (ABSA).
It performs end-to-end ABSA by jointly extracting aspect terms and their sentiments using a single token-classification head. Labels follow an IOB-with-sentiment format, for example B-ASP-Positive, I-ASP-Negative, or O for non-aspect tokens.

Developed by: Sajida-dev
Model type: Token Classification (Aspect-Based Sentiment Analysis)
Language(s): English
License: MIT
Finetuned from: yangheng/deberta-v3-base-end2end-absa
Library: Transformers

Model Sources

Repository: https://huggingface.co/sajida-dev/fine-tune-deberta-v3-base-end2end-absa-model
Base Paper: DeBERTa: Decoding-enhanced BERT with Disentangled Attention (https://arxiv.org/abs/2006.03654)
Demo: Available via Hugging Face pipeline("token-classification")

Uses

Direct Use

Extract aspect terms from text (e.g., "battery life", "screen")
Assign sentiment polarity (Positive, Negative, Neutral) to each aspect
Useful for product reviews, customer feedback, and opinion mining

Downstream Use

Integration into customer service analytics
Market research sentiment dashboards
Fine-tuning for domain-specific ABSA tasks (restaurants, healthcare, etc.)

Out-of-Scope Use

General sentiment classification without aspect extraction
Non-English text
Misuse for biased or harmful profiling

Bias, Risks, and Limitations

Bias

Model performance depends on the dataset used for fine-tuning. If training data is domain-specific, generalization to other domains may be limited.

Limitations

Works best on English text
May misclassify nuanced sentiments such as sarcasm or irony
Aspect boundary detection may fail for complex multi-word expressions

Recommendations

Users should validate outputs on their own domain-specific data and consider further fine-tuning if needed.

How to Get Started with the Model

python

from transformers import pipeline

nlp = pipeline(
    "token-classification",
    model="sajida-dev/fine-tune-deberta-v3-base-end2end-absa-model"
)

text = "The battery life is amazing but the screen is dull."
results = nlp(text)
print(results)

Then, you can perform inference like this. The model will automatically find the aspects in the text and classify their sentiment.

python3

{
   "text": "The user interface is brilliant, but the documentation is a total mess.",
   "aspect": ["user interface", "documentation"],
   "position": [[4, 19], [41, 54]],
   "sentiment": ["Positive", "Negative"],
   "probability": [[1e-05, 0.0001, 0.9998], [0.9998, 0.0001, 1e-05]],
   "confidence": [0.9997, 0.9997]
}

Training Details

Training Data

Fine-tuned on ABSA datasets (for example, product review datasets; details to be added).
Preprocessing includes tokenization with the DeBERTa tokenizer and IOB tagging with sentiment labels.

Training Procedure

Optimizer: AdamW
Loss: Cross-entropy for token classification
Mixed precision: fp16
Early stopping: Enabled (patience = 3)

Training Hyperparameters

Hyperparameter	Value
Learning rate	2e-5
Warmup ratio	0.1
Number of epochs	5
Train batch size (per device)	16
Eval batch size (per device)	32
Gradient accumulation steps	2
Weight decay	0.01
Label smoothing factor	0.05
Evaluation strategy	Per epoch
Save strategy	Per epoch
Metric for best model	F1
Random seed	42

Evaluation

Testing Data

Held-out ABSA test split from product review datasets.

Factors & Metrics

Accuracy
Precision
Recall
F1-score

Results (Final Evaluation on Test Set)

Metric	Value
Accuracy	0.9660
Precision	0.5905
Recall	0.4684
F1-score	0.5224
Eval loss	0.1200

Environmental Impact

Item	Details
Hardware	GPU
Training time	~3.5 hours
Training epochs	5
Carbon footprint	Not estimated

Technical Specifications

Architecture: DeBERTa-v3 base with token classification head
Objective: Joint aspect extraction and sentiment classification
Compute Framework: Hugging Face Transformers with PyTorch

Citation

BibTeX:

  @misc{sajida2025absa,
    author       = {Sajida-dev},
    title        = {Fine-tuned DeBERTa-v3 Base End-to-End ABSA Model},
    year         = {2025},
    publisher    = {Hugging Face},
    howpublished = {https://huggingface.co/sajida-dev/fine-tune-deberta-v3-base-end2end-absa-model}
  }