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Phi2 Ner V1 by pahautelman

 »  All LLMs  »  pahautelman  »  Phi2 Ner V1   URL Share it on

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

Phi2 Ner V1 Benchmarks

LLME Score: 0.14997
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Phi2 Ner V1 Parameters and Internals

Model Type 
text-generation, named entity recognition
Use Cases 
Areas:
research, commercial applications
Primary Use Cases:
named entity recognition
Additional Notes 
The model is trained to recognize the named entity categories such as person, organization, etc.
Supported Languages 
en (fluent)
Training Details 
Data Sources:
conll2012_ontonotesv5
Data Volume:
87,265 examples
Methodology:
Fine-tuning using one quarter of the ConLL 2012 OntoNotes v5 dataset
Context Length:
1024
Input Output 
Input Format:
Prompt with text input
Accepted Modalities:
text
Output Format:
Named entities labeled in text
Performance Tips:
Correctly label entities and use structured prompts for accurate outputs.
LLM NamePhi2 Ner V1
Repository 🤗https://huggingface.co/pahautelman/phi2-ner-v1 
Model Size1.6b
Required VRAM1.9 GB
Updated2025-06-09
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
Phi2 Ner V1 (pahautelman/phi2-ner-v1)

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

Rank the Phi2 Ner 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 v20241124
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