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:
- ddcde0fb6b8d4189d2426f66c47baf9766479b82316e23f1d0848627508eb224
- Size of remote file:
- 438 MB
- SHA256:
- 588d41e09049472f821680303d3641c4a7abec37c4cc10d5c9a7b34ccb4e9357
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