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Phi2 Ner Sft V1 by pahautelman

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  4-bit   Autotrain   Autotrain compatible   Bitsandbytes   Conversational   Custom code   Dataset:conll2012 ontonotesv5   En   Endpoints compatible   Phi   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/pahautelman/phi2-ner-sft-v1 

Phi2 Ner Sft V1 Benchmarks

LLME Score: 0.16632
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi2 Ner Sft V1 (pahautelman/phi2-ner-sft-v1)

Phi2 Ner Sft V1 Parameters and Internals

Model Type 
text generation, named entity recognition
Use Cases 
Areas:
research, commercial applications
Primary Use Cases:
named entity recognition
Supported Languages 
languages_supported (en), proficiency_level ()
Training Details 
Data Sources:
conll2012_ontonotesv5
Data Volume:
21,817 examples
Methodology:
fine-tuned using SFT AutoTrain trainer
Input Output 
Input Format:
tokenized text string
Accepted Modalities:
text
Output Format:
tokenized text string with entities labeled
LLM NamePhi2 Ner Sft V1
Repository 🤗https://huggingface.co/pahautelman/phi2-ner-sft-v1 
Model Size1.6b
Required VRAM1.9 GB
Updated2025-02-05
Maintainerpahautelman
Model Typephi
Model Files  1.9 GB
Supported Languagesen
Model ArchitecturePhiForCausalLM
Licensemit
Context Length2048
Model Max Length2048
Transformers Version4.37.2
Tokenizer ClassCodeGenTokenizer
Padding Token<|endoftext|>
Vocabulary Size50295
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than pahautelman/phi2-ner-sft-v1.

Rank the Phi2 Ner Sft V1 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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Release v20241227
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