Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use danfeg/IF-E5-L-M_Finetuned-COMB-3000 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-COMB-3000 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/IF-E5-L-M_Finetuned-COMB-3000") 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 model.safetensors from danfeg/IF-E5-L-M_Finetuned-COMB-3000: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/danfeg/IF-E5-L-M_Finetuned-COMB-3000/resolve/main/model.safetensors
- Command line
-
hf download hf://danfeg/IF-E5-L-M_Finetuned-COMB-3000/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/danfeg/IF-E5-L-M_Finetuned-COMB-3000/resolve/main/model.safetensors
2.24 GB
- Xet hash:
- 089f1c0490ed0ed570c1fedf4b5c621f922b737892031bb41cd55e335fd4bef6
- Size of remote file:
- 2.24 GB
- SHA256:
- a3abe20a04e5e01aef12bb3392a9abcfedd41c48ce7d038ba09633ee983c4de0
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