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--- |
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library_name: transformers |
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language: |
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- en |
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widget: |
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- text: "Your task is to classify sentences' sentiment as 'positive' or 'negative'. Your answer should be one word, either 'positive' or 'negative'. |
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Sentence: I love this movie! |
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Answer: " |
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- text: "Your task is to classify sentences' sentiment as 'positive' or 'negative'. Your answer should be one word, either 'positive' or 'negative'. |
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Sentence: I hate this movie! |
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Answer: " |
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pipeline_tag: text-generation |
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tags: |
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- nlp |
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--- |
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# Model Card for Phi 1.5B Microsoft Trained Sentiment Analysis Model |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model performs sentiment analysis on sentences, classifying them as either 'positive' or 'negative'. It is trained on the IMDB dataset and has been fine-tuned for this task. |
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## Model Details |
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### Model Description |
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Phi 1.5B Microsoft trained with the IMDB Dataset. |
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### Prompt Used in Training |
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Your task is to classify sentences' sentiment as 'positive' or 'negative'. Your answer should be one word, either 'positive' or 'negative'. |
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Sentence: {text} |
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Answer: |
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## Inference Example using Hugging Face Inference API |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="matheusrdgsf/phi-sentiment-analysis-model") |
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result = classifier("I love this movie") |
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print(result[0]['label']) # Output: 'POSITIVE' |
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