π§ Model
bart-large-mnli-yahoo-answers
by joeddav
bart-large-mnli-yahoo-answers is an open-source AI model by joeddav
π Updated 12/31/2025
Technical Specifications
Parameters0.41
ArchitectureBartForSequenceClassification
View Config (3 entries)
{
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"tokenizer_config": {}
}
πΎ
Est. VRAM Required
~3 GB
Estimation Formula
VRAM = params Γ 0.6 + 2 GB
Based on FP16 precision.
β οΈ Does not account for KV cache or parallel overhead.
π Estimate only. Actual requirements may vary.
π Daily sync (11:00 Beijing)
Based on open-source metadata snapshot. Last synced: Dec 31, 2025
π§ Architecture Explorer
Neural network architecture
1 Input Layer
2 Hidden Layers
3 Attention
4 Output Layer
Parameters 0.41B
Technical Specifications
Parameters0.41
ArchitectureBartForSequenceClassification
0View Config (3 entries)
{
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"tokenizer_config": {}
}
π Limitations & Considerations
- β’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
- β’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
- β’ FNI scores are relative rankings and may change as new models are added.
- β License Unknown: Verify licensing terms before commercial use.
- β’ Source: Huggingface
π Related Resources
π Related Papers
No related papers linked yet. Check the model's official documentation for research papers.
π Training Datasets
Training data information not available. Refer to the original model card for details.
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