Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use danfeg/IF-E5-L-M_Finetuned-FR-2481 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/IF-E5-L-M_Finetuned-FR-2481 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/IF-E5-L-M_Finetuned-FR-2481") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from danfeg/IF-E5-L-M_Finetuned-FR-2481: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/danfeg/IF-E5-L-M_Finetuned-FR-2481/resolve/main/tokenizer.json
- Command line
-
hf download hf://danfeg/IF-E5-L-M_Finetuned-FR-2481/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/danfeg/IF-E5-L-M_Finetuned-FR-2481/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5f077be07abb91ed75b6146001ac8d3218def78d7e8d75e493f696b022c227c5
- Size of remote file:
- 17.1 MB
- SHA256:
- f1cc44ad7faaeec47241864835473fd5403f2da94673f3f764a77ebcb0a803ec
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