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:
- c518142c8182c9dd0a93a3e61cc8e55f0f9d9d06f72698332c6d2f46180b28e9
- Size of remote file:
- 1.11 GB
- SHA256:
- 0a5245b61a51a46bd4ebed9daf8280586ad206934a0c18ab3024a872a5d156e5
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