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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/scincl") model = AutoModel.from_pretrained("malteos/scincl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from malteos/scincl: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/malteos/scincl/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://malteos/scincl/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/malteos/scincl/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- d68154c8f672fb24979163c96d50f0b98ddc3a465ca01988389043251134e44d
- Size of remote file:
- 440 MB
- SHA256:
- 34bf9b9761e253927a6533218fbf41b7ebe06d4100e61da83f877af56e113299
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