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RichardErkhov/
matheusrdgsf_-_phi-sentiment-analysis-model-gguf

Name: matheusrdgsf_-_phi-sentiment-analysis-model-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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phi-sentiment-analysis-model - GGUF

Model creator: https://huggingface.co/matheusrdgsf/
Original model: https://huggingface.co/matheusrdgsf/phi-sentiment-analysis-model/

Name	Quant method	Size
phi-sentiment-analysis-model.Q2_K.gguf	Q2_K	0.54GB
phi-sentiment-analysis-model.Q3_K_S.gguf	Q3_K_S	0.61GB
phi-sentiment-analysis-model.Q3_K.gguf	Q3_K	0.69GB
phi-sentiment-analysis-model.Q3_K_M.gguf	Q3_K_M	0.69GB
phi-sentiment-analysis-model.Q3_K_L.gguf	Q3_K_L	0.75GB
phi-sentiment-analysis-model.IQ4_XS.gguf	IQ4_XS	0.74GB
phi-sentiment-analysis-model.Q4_0.gguf	Q4_0	0.77GB
phi-sentiment-analysis-model.IQ4_NL.gguf	IQ4_NL	0.78GB
phi-sentiment-analysis-model.Q4_K_S.gguf	Q4_K_S	0.78GB
phi-sentiment-analysis-model.Q4_K.gguf	Q4_K	0.83GB
phi-sentiment-analysis-model.Q4_K_M.gguf	Q4_K_M	0.83GB
phi-sentiment-analysis-model.Q4_1.gguf	Q4_1	0.85GB
phi-sentiment-analysis-model.Q5_0.gguf	Q5_0	0.92GB
phi-sentiment-analysis-model.Q5_K_S.gguf	Q5_K_S	0.92GB
phi-sentiment-analysis-model.Q5_K.gguf	Q5_K	0.96GB
phi-sentiment-analysis-model.Q5_K_M.gguf	Q5_K_M	0.96GB
phi-sentiment-analysis-model.Q5_1.gguf	Q5_1	1.0GB
phi-sentiment-analysis-model.Q6_K.gguf	Q6_K	1.09GB
phi-sentiment-analysis-model.Q8_0.gguf	Q8_0	1.41GB

Original model description:

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:

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

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'

RichardErkhov/matheusrdgsf_-_phi-sentiment-analysis-model-gguf