Instructions to use DhanasriArul/Model2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use DhanasriArul/Model2vec with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("DhanasriArul/Model2vec") - sentence-transformers
How to use DhanasriArul/Model2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DhanasriArul/Model2vec") 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
File size: 962 Bytes
d45f802 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | This directory includes a few sample datasets to get you started.
* `california_housing_data*.csv` is California housing data from the 1990 US
Census; more information is available at:
https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
* `mnist_*.csv` is a small sample of the
[MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
described at: http://yann.lecun.com/exdb/mnist/
* `anscombe.json` contains a copy of
[Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
was originally described in
Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the
[vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
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