Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BlueAvenir/vaillant_relation_depth_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BlueAvenir/vaillant_relation_depth_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BlueAvenir/vaillant_relation_depth_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 BlueAvenir/vaillant_relation_depth_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/vaillant_relation_depth_model") model = AutoModel.from_pretrained("BlueAvenir/vaillant_relation_depth_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 518173abc1a21ea9499369794e42813d94a1017098bc3a677a0d94bed0e8762e
- Size of remote file:
- 6.96 kB
- SHA256:
- 3f3135767157951ec59b7552e6e40c22b37130f0d897ec85338d8b75eba0754a
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