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Bert Small by prajjwal1

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Bert Small is an open-source language model by prajjwal1. Features: LLM, VRAM: 0.1GB, Context: 0.5K, License: mit, LLM Explorer Score: 0.04.

  Arxiv:1908.08962   Arxiv:2110.01518   Bert   En   Endpoints compatible   Mnli   Nli   Pre-training   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/prajjwal1/bert-small 

Bert Small Benchmarks

LLME Score: 0.04012
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bert Small (prajjwal1/bert-small)
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Bert Small Parameters and Internals

Model Type 
Text Transformer, NLI
Use Cases 
Areas:
Natural Language Inference
Applications:
Research, Commercial NLP applications
Primary Use Cases:
Supposed to be trained on downstream tasks such as NLI
Limitations:
Model size is relatively small, hence may not perform as well as its larger counterparts on tasks that require significant model capacity.
Considerations:
Developers should focus on tasks that can leverage the compact size of the model for efficiency.
Additional Notes 
Original implementation and additional information can be found in the specified GitHub repository.
Training Details 
Data Sources:
Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics
Methodology:
Condensed and compact pre-training followed by downstream task training
Model Architecture:
L=4, H=512
LLM NameBert Small
Repository 🤗https://huggingface.co/prajjwal1/bert-small 
Required VRAM0.1 GB
Updated2026-03-29
Maintainerprajjwal1
Model Files  0.1 GB
Supported Languagesen
Model ArchitectureAutoModel
Licensemit
Context Length512
Model Max Length512
Vocabulary Size30522

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Note: green Score (e.g. "73.2") means that the model is better than prajjwal1/bert-small.

Rank the Bert Small 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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