Text Classification
Transformers
ONNX
Safetensors
English
modernbert
rag
governance
hallucination-detection
classification
fitz-gov
pyrrho
text-embeddings-inference
Instructions to use yafitzdev/pyrrho-v1-nano-g2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yafitzdev/pyrrho-v1-nano-g2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yafitzdev/pyrrho-v1-nano-g2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yafitzdev/pyrrho-v1-nano-g2") model = AutoModelForSequenceClassification.from_pretrained("yafitzdev/pyrrho-v1-nano-g2") - Notebooks
- Google Colab
- Kaggle
Clarify encoder and MoE output contracts
Browse files
README.md
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The encoder does not output generated answers, explanations, citations, source spans, retrieval results, or experimental multitask-head fields.
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## Intended Use
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}
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```
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The encoder does not output generated answers, explanations, citations, source spans, retrieval results, taxonomy/category tags, route IDs, scalar diagnostics, or experimental multitask-head fields. Taxonomy/category fields that appear in evaluation reports are benchmark metadata used for breakdowns; route, taxonomy, and scalar heads are part of the experimental MoE track, not the published nano encoder output contract.
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## Intended Use
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