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--- |
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title: "ESPA AI" |
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emoji: "🤖" |
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colorFrom: "blue" |
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colorTo: "green" |
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sdk: "transformers" |
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sdk_version: "4.21.1" |
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app_file: "app.py" |
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license: "mit" |
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tags: |
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- "text-classification" |
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- "distilbert" |
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- "NLP" |
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- "sentiment-analysis" |
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short_description: "A DistilBERT-based model fine-tuned on IMDb for text classification." |
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--- |
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# Model Card for ESPA AI |
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ESPA AI is a text classification model fine-tuned on the IMDb dataset using DistilBERT. It is designed to classify movie reviews as either positive or negative. |
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## Model Details |
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### Model Description |
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This model uses the DistilBERT architecture, a smaller, faster version of BERT, to perform sentiment analysis on text data. It has been fine-tuned on the IMDb dataset for binary classification (positive or negative reviews). |
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- **Developed by:** DilipKY |
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- **Funded by:** [Optional Information] |
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- **Model type:** Transformer-based model (DistilBERT) |
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- **Language(s):** English |
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- **License:** MIT License |
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- **Finetuned from model:** distilbert-base-uncased |
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### Model Sources |
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- **Repository:** [DilipKY/espa-ai](https://huggingface.co/DilipKY/espa-ai) |
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- **Paper:** [DistilBERT: A smaller, faster, cheaper version of BERT](https://arxiv.org/abs/1910.01108) |
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## Uses |
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### Direct Use |
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This model can be used to classify text data into positive or negative categories. It is useful for sentiment analysis in applications like customer feedback analysis, review classification, etc. |
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```python |
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from transformers import pipeline |
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# Load pre-trained model from Hugging Face |
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classifier = pipeline("text-classification", model="DilipKY/espa-ai") |
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# Test on a sample review |
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sample_text = "This movie was amazing! The plot was so engaging and the acting was superb." |
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result = classifier(sample_text) |
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print(result) |
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