Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BlueAvenir/dummy_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BlueAvenir/dummy_10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BlueAvenir/dummy_10") 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/dummy_10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/dummy_10") model = AutoModel.from_pretrained("BlueAvenir/dummy_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40cf2c72427a61353451ad19ed0bb24c07950f5a51c12a26b81efabb2535e789
- Size of remote file:
- 1.11 GB
- SHA256:
- 38247d911eb9d010ad799a1f2e0f68700893ed630496c2e822c5cbe0b6108b4a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.