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") - 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -117,7 +117,6 @@ model_name: M2V_base_multilingual
|
|
| 117 |
tags:
|
| 118 |
- embeddings
|
| 119 |
- static-embeddings
|
| 120 |
-
pipeline_tag: feature-extraction
|
| 121 |
---
|
| 122 |
|
| 123 |
# M2V_base_multilingual Model Card
|
|
|
|
| 117 |
tags:
|
| 118 |
- embeddings
|
| 119 |
- static-embeddings
|
|
|
|
| 120 |
---
|
| 121 |
|
| 122 |
# M2V_base_multilingual Model Card
|