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phi-sentiment-analysis-model
matheusrdgsf/phi-sentiment-analysis-model
phi-sentiment-analysis-model is a text generation model from matheusrdgsf. Use it when you need the model to write or continue text. It is set up for transformers.
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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2.8 GB on disk
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At a glance
- Task
- Text Generation
- Library
- transformers
- Model type
- phi
- Access
- Public
- Created
- Apr 24, 2024
- Updated
- Apr 25, 2024
- SHA
- b1a74dc0
Try a prompt
Model card
From the Hugging Face model README
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
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'
- Task
- Text Generation
- Library
- transformers
- Type
- phi
- Languages
- en
- Created
- Apr 24, 2024
- Updated
- Apr 25, 2024