metadata

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

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'