Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Linco/test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Linco/test-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Linco/test-model") 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/test-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Linco/test-model") model = AutoModel.from_pretrained("Linco/test-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e0514f15498fe93f117b4fa74eecc5927378a287a6fb7ad03aea1685bcd2a48
- Size of remote file:
- 6.99 kB
- SHA256:
- 5bf8faca32b18af272f3deb5d407af25ab96d6800f992af31898261f589307b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.