Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers.js
bert
feature-extraction
mteb
arctic
snowflake-arctic-embed
Eval Results (legacy)
text-embeddings-inference
Instructions to use Snowflake/snowflake-arctic-embed-xs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Snowflake/snowflake-arctic-embed-xs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Snowflake/snowflake-arctic-embed-xs") 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.js
How to use Snowflake/snowflake-arctic-embed-xs with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'Snowflake/snowflake-arctic-embed-xs'); - Inference
- Notebooks
- Google Colab
- Kaggle
Don't create pooling layer with Sentence Transformers
#8
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Remove "Some weights were not initialized" warning
Details
We can introduce some model arguments in the configuration, which will be used in the eventual model creation. This solved the warning that users have been experiencing:
Some weights of BertModel were not initialized from the model and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']
- Tom Aarsen
spacemanidol changed pull request status to open
spacemanidol changed pull request status to merged