Instructions to use shawon95/BengaliFakeReviewDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shawon95/BengaliFakeReviewDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shawon95/BengaliFakeReviewDetection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shawon95/BengaliFakeReviewDetection") model = AutoModelForSequenceClassification.from_pretrained("shawon95/BengaliFakeReviewDetection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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The paper **"Bengali Fake Reviews: A Benchmark Dataset and Detection System"** accepted in [Neuroomputing](https://www.sciencedirect.com/journal/neurocomputing), a journal published by Elsevier.
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## Repository Structure
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The repository has two folders:
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Code: All the codes for deep learning models, transformers, ensemble model and text conversion pipeline are available.
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Dataset: Contains two excel files (a) fake.xlsx (b) non-fake xlsx
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Each file contains two columns: **Review** (collected raw reviews), **Label** (annotations).
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## Dataset Statistics
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- Annotated by 4 native Bangla speakers with more than 90% trustworthiness score.
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The paper **"Bengali Fake Reviews: A Benchmark Dataset and Detection System"** accepted in [Neuroomputing](https://www.sciencedirect.com/journal/neurocomputing), a journal published by Elsevier.
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## Dataset Statistics
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- Annotated by 4 native Bangla speakers with more than 90% trustworthiness score.
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