Text Classification
ONNX
sentence-transformers
French
English
yes-no
intent-classification
distillation
edge
quantization
Eval Results (legacy)
Instructions to use jcfossati/ForSureLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jcfossati/ForSureLLM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jcfossati/ForSureLLM") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
JC commited on
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README.md
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---
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language:
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- fr
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- en
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tags:
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- text-classification
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- yes-no
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- onnx
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- distillation
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license: gpl-3.0
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pipeline_tag: text-classification
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---
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# ForSureLLM — yes/no/unknown classifier
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Source : https://github.com/jcfossati/ForSureLLM
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Basic analysis of english and french language for yes/no detection.
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When knowing the question and waiting a yes/no answer, asking a frontier LLM is overkill using to much resources, and latency is high for a limited action.
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## Stats
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Distilled via KL-divergence on soft labels.
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MiniLM-L12-v2 multilingual backbone, fine-tuned + int8 quantized.
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- Accuracy: 91.4%
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- ECE: 0.007 (calibrated)
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- Latency: 2.5ms CPU
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- Size: 113 MB
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## Usage
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- Interaction between an application and the user featuring free-form text input.
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- Chatbot asking user and needed a yes/no answer
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