Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use RomanLeo2003/tatar_sentence_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RomanLeo2003/tatar_sentence_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RomanLeo2003/tatar_sentence_encoder") 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 RomanLeo2003/tatar_sentence_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RomanLeo2003/tatar_sentence_encoder") model = AutoModel.from_pretrained("RomanLeo2003/tatar_sentence_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c26ed2d90bdf6588253da6dbed06cd01b9fb2ebfc12a9533bf796a5d17022b4a
- Size of remote file:
- 329 MB
- SHA256:
- 7b646a08c2445fca6868cb703259a206040f04cef5be9809d682270f60f9b39b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.