Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BlueAvenir/Testiter4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BlueAvenir/Testiter4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BlueAvenir/Testiter4") 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/Testiter4 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/Testiter4") model = AutoModel.from_pretrained("BlueAvenir/Testiter4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cc61fdaf83b353f2c92c3be1b9760b897f0999a45299b25f35cf0b1fb544d17
- Size of remote file:
- 6.99 kB
- SHA256:
- 026cc8bc3f97688bbde6ca64f8bee994f68f02cc974eba833c05d701d07741c2
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