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
Update README.md
Browse files
README.md
CHANGED
|
@@ -16,7 +16,7 @@ pipeline_tag: text-classification
|
|
| 16 |
Source : https://github.com/jcfossati/ForSureLLM
|
| 17 |
|
| 18 |
Basic analysis of english and french language for yes/no detection.
|
| 19 |
-
When knowing the question and waiting a yes/no answer, asking a frontier LLM is overkill using
|
| 20 |
|
| 21 |
## Stats
|
| 22 |
|
|
|
|
| 16 |
Source : https://github.com/jcfossati/ForSureLLM
|
| 17 |
|
| 18 |
Basic analysis of english and french language for yes/no detection.
|
| 19 |
+
When knowing the question and waiting a yes/no answer, asking a frontier LLM is overkill using too much resources, and latency is high for a limited action.
|
| 20 |
|
| 21 |
## Stats
|
| 22 |
|