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:
- 3f9a18d49d9090cff58653b85616589ef2ebc760327b915f6d7fffd82f9b8d57
- Size of remote file:
- 1.11 GB
- SHA256:
- 9b8bfa8803e12190c63fbfda39dbad2ce44aaf687d0e1c34983ae61d2e0236ba
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