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README.md
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app_file: app.py
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pinned: false
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license: mit
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---
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app_file: app.py
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pinned: false
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license: mit
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short_description: 'SEMA: Analyze sentence similarity, sentiment, and more'
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---
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# π§ͺ SEMA: Semantic Evaluation & Matching Analyzer
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SEMA is an interactive Gradio-powered NLP tool for analyzing semantic similarity, paraphrasing, sentiment, and linguistic features between two sentences.
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## π Features
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- β
Sentence similarity scoring (BERT/SBERT models)
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- π Visual similarity charts (Bar, Gauge, Heatmap)
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- π Paraphrase detection and generation
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- π¬ Sentiment analysis (transformers-based)
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- π§ Named Entity Recognition (NER)
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- π§Ύ POS tagging with visual distribution (Pie chart)
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- π Text statistics: word counts, overlap, character counts
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## π§ Models Used
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- Sentence Transformers:
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- `all-MiniLM-L6-v2`
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- `paraphrase-MiniLM-L3-v2`
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- `paraphrase-multilingual-MiniLM-L12-v2`
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- `distilbert-base-nli-mean-tokens`
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- Paraphraser: `ramsrigouthamg/t5_paraphraser`
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- Sentiment: Hugging Face sentiment-analysis pipeline
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- NER/POS: spaCy (`en_core_web_trf`)
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## π οΈ Setup
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```bash
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git clone https://github.com/adityasync/SEMA.git
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cd SEMA
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pip install -r requirements.txt
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python app.py
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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