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") - 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -7,8 +7,8 @@ license: mit
|
|
| 7 |
model_name: M2V_base_output
|
| 8 |
tags:
|
| 9 |
- embeddings
|
| 10 |
-
- sentence-transformers
|
| 11 |
- static-embeddings
|
|
|
|
| 12 |
---
|
| 13 |
|
| 14 |
# M2V_base_output Model Card
|
|
@@ -65,4 +65,4 @@ Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) i
|
|
| 65 |
|
| 66 |
## Model Authors
|
| 67 |
|
| 68 |
-
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of Stephan Tulkens and Thomas van Dongen.
|
|
|
|
| 7 |
model_name: M2V_base_output
|
| 8 |
tags:
|
| 9 |
- embeddings
|
|
|
|
| 10 |
- static-embeddings
|
| 11 |
+
pipeline_tag: feature-extraction
|
| 12 |
---
|
| 13 |
|
| 14 |
# M2V_base_output Model Card
|
|
|
|
| 65 |
|
| 66 |
## Model Authors
|
| 67 |
|
| 68 |
+
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of Stephan Tulkens and Thomas van Dongen.
|