Model Card for Phi 1.5B Microsoft Trained Sentiment Analysis Model
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
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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