Instructions to use minishlab/M2V_base_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/M2V_base_output with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/M2V_base_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_base_output with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/M2V_base_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 tokenizer.json from minishlab/M2V_base_output: direct link, hf CLI and curl.
- Browser
- Download file 684 kB
-
https://huggingface.co/minishlab/M2V_base_output/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://minishlab/M2V_base_output@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/minishlab/M2V_base_output/resolve/refs%2Fpr%2F1/tokenizer.json
684 kB
- Xet hash:
- 54aba82d6b286ed73d15468953767ae9b15a7075cd24e8806a9a595ab1272330
- Size of remote file:
- 684 kB
- SHA256:
- e67e803f624fb4d67dea1c730d06e1067e1b14d830e2c2202569e3ef0f70bb50
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