Instructions to use malteos/scincl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use malteos/scincl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("malteos/scincl") 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] - Transformers
How to use malteos/scincl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="malteos/scincl")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/scincl") model = AutoModel.from_pretrained("malteos/scincl") - Inference
- Notebooks
- Google Colab
- Kaggle
Add Sentence Transformers integration
#2
by tomaarsen HF Staff - opened
Hello @malteos
Pull Request overview
- Integrate with Sentence Transformers
Details
This allows your model to be easily used in third party applications that integrate with Sentence Transformers, like LangChain, LlamaIndex, Haystack, SetFit, BERTopic, etc. The usage is also simplified, as ST abstracts away the tokenization, pooling, and similarity calculations.
- Tom Aarsen
tomaarsen changed pull request status to open
Thanks @tomaarsen
malteos changed pull request status to merged