Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/msmarco-databricks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rithwik-db/msmarco-databricks with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/msmarco-databricks") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use rithwik-db/msmarco-databricks with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/msmarco-databricks") model = AutoModel.from_pretrained("rithwik-db/msmarco-databricks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5719253d27550890671b63ea131fbeeed1c42e7747891644e4b0b7953cf56b8
- Size of remote file:
- 265 MB
- SHA256:
- e75ef600ebc1b4fd5917e17255cad28ead93f70948599f7164de3a3c786e50a8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.