Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Linco/setfit-trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Linco/setfit-trainer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Linco/setfit-trainer") 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 Linco/setfit-trainer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Linco/setfit-trainer") model = AutoModel.from_pretrained("Linco/setfit-trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Linco/setfit-trainer: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Linco/setfit-trainer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Linco/setfit-trainer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Linco/setfit-trainer/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- d7b7665fe8411eb8531ac72dcc0775eaa18b44ba4a5f997566d75b9b3203f586
- Size of remote file:
- 438 MB
- SHA256:
- e18d86b49bf787c1f045fb76feb73d5629e82a8edbf46f3f8dfb3360deff2306
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