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Phi Sentiment Analysis Model by matheusrdgsf

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Phi Sentiment Analysis Model Benchmarks

LLME Score: 0.19885
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Phi Sentiment Analysis Model Parameters and Internals

Model Type 
text-classification
Use Cases 
Areas:
sentiment analysis
Applications:
movie reviews, customer feedback, social media monitoring
Primary Use Cases:
text sentiment classification
Supported Languages 
en (High)
Training Details 
Data Sources:
IMDB Dataset
Methodology:
Fine-tuning for sentiment analysis
Input Output 
Input Format:
Sentence: {text}
Accepted Modalities:
text
Output Format:
One word: 'positive' or 'negative'
LLM NamePhi Sentiment Analysis Model
Repository 🤗https://huggingface.co/matheusrdgsf/phi-sentiment-analysis-model 
Model Size1.4b
Required VRAM2.8 GB
Updated2024-11-21
Maintainermatheusrdgsf
Model Typephi
Model Files  2.8 GB
Supported Languagesen
Model ArchitecturePhiForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.37.0
Tokenizer ClassCodeGenTokenizer
Padding Token<|endoftext|>
Vocabulary Size51200
Torch Data Typefloat16
Phi Sentiment Analysis Model (matheusrdgsf/phi-sentiment-analysis-model)

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Note: green Score (e.g. "73.2") means that the model is better than matheusrdgsf/phi-sentiment-analysis-model.

Rank the Phi Sentiment Analysis Model Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241110
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