Instructions to use minishlab/M2V_multilingual_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/M2V_multilingual_output with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/M2V_multilingual_output") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use minishlab/M2V_multilingual_output with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/M2V_multilingual_output") 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 config.json from minishlab/M2V_multilingual_output: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/minishlab/M2V_multilingual_output/resolve/main/config.json
- Command line
-
hf download hf://minishlab/M2V_multilingual_output/config.json
-
curl -L -o config.json https://huggingface.co/minishlab/M2V_multilingual_output/resolve/main/config.json
190 Bytes
| {"model_type": "model2vec", "architectures": ["StaticModel"],"tokenizer_name": "sentence-transformers/LaBSE", "apply_pca": 256, "apply_zipf": true, "normalize": false, "seq_length": 1000000} |