Sentence Similarity
sentence-transformers
PyTorch
Transformers
deberta-v2
feature-extraction
text-embeddings-inference
Instructions to use jamescalam/deberta-v3-base-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jamescalam/deberta-v3-base-qa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jamescalam/deberta-v3-base-qa") 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 jamescalam/deberta-v3-base-qa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jamescalam/deberta-v3-base-qa") model = AutoModel.from_pretrained("jamescalam/deberta-v3-base-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88b2c46aee63e64f95389970af8761d7c955fc6f67f6ca590ff963c63326a0f0
- Size of remote file:
- 735 MB
- SHA256:
- e4a436c381717c3be04d858734c1bebd952555877e5db2ea2aa25950920f7ec6
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