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Spanbert Large Finetuned Squadv2 by mrm8488

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Spanbert Large Finetuned Squadv2 is an open-source language model by mrm8488. Features: LLM, VRAM: 0.7GB, Context: 0.5K, Fine-Tuned, LLM Explorer Score: 0.

  Arxiv:1907.10529   Bert   En   Endpoints compatible   Finetuned   Jax   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv2 

Spanbert Large Finetuned Squadv2 Benchmarks

LLME Score: 0.001
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Spanbert Large Finetuned Squadv2 (mrm8488/spanbert-large-finetuned-squadv2)
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Spanbert Large Finetuned Squadv2 Parameters and Internals

Model Type 
Q&A
Use Cases 
Areas:
Research, Commercial applications
Applications:
Question answering, Coreference resolution, Relation extraction
Primary Use Cases:
Answering questions with possible unanswerable detection (SQuAD 2.0)
Additional Notes 
Note that numbers marked with * are evaluated on development sets because those models weren't submitted to the official SQuAD leaderboard.
Training Details 
Data Sources:
SQuAD 2.0
Data Volume:
130k for training, 12.3k for evaluation
Methodology:
Fine-tuning on SQuAD 2.0
Context Length:
512
Model Architecture:
SpanBERT large-cased
Input Output 
Input Format:
Context and question pairs
Accepted Modalities:
text
Output Format:
Answer and score
Performance Tips:
Use with transformers pipelines for fast usage.
LLM NameSpanbert Large Finetuned Squadv2
Repository 🤗https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv2 
Required VRAM0.7 GB
Updated2026-01-02
Maintainermrm8488
Model Typebert
Model Files  0.7 GB
Supported Languagesen
Model ArchitectureAutoModel
Context Length512
Model Max Length512
Vocabulary Size28996

Best Alternatives to Spanbert Large Finetuned Squadv2

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Context / RAM
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Opengpt2 Pytorch Forward1K / 6 GB41
Finsent Transformer0.5K / 0.4 GB01
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Bert Chinese L 12 H 768 A 120.5K / 0.4 GB11
Simbert Chinese Tiny0.5K / 0 GB50
Bert Tiny0.5K / 0 GB784113139
Note: green Score (e.g. "73.2") means that the model is better than mrm8488/spanbert-large-finetuned-squadv2.

Rank the Spanbert Large Finetuned Squadv2 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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