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