Sentence Similarity
sentence-transformers
Safetensors
Polish
English
German
eurobert
- embeddings
plwordnet
semantic-relations
semantic-search
custom_code
Instructions to use radlab/semantic-euro-bert-encoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use radlab/semantic-euro-bert-encoder-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("radlab/semantic-euro-bert-encoder-v1", trust_remote_code=True) 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
Download DETAILED_README.md from radlab/semantic-euro-bert-encoder-v1: direct link, hf CLI and curl.
- Browser
- Download file 12.4 MB
-
https://huggingface.co/radlab/semantic-euro-bert-encoder-v1/resolve/main/DETAILED_README.md
- Command line
-
hf download hf://radlab/semantic-euro-bert-encoder-v1/DETAILED_README.md
-
curl -L -o DETAILED_README.md https://huggingface.co/radlab/semantic-euro-bert-encoder-v1/resolve/main/DETAILED_README.md
12.4 MB
- Xet hash:
- b8b98cf234cb9f23c8bbab0a39e2e154d852852f8779172a2238226e416d760f
- Size of remote file:
- 12.4 MB
- SHA256:
- 7fbcb367d7684e4d48e9b9fab958b06934e7960c4b1848d81395de10d166ec3c
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