---
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'