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:
# pip install -U transformers accelerate # 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
Download spm.model from jamescalam/deberta-v3-base-qa: direct link, hf CLI and curl.
- Browser
- Download file 2.46 MB
-
https://huggingface.co/jamescalam/deberta-v3-base-qa/resolve/main/spm.model
- Command line
-
hf download hf://jamescalam/deberta-v3-base-qa/spm.model
-
curl -L -o spm.model https://huggingface.co/jamescalam/deberta-v3-base-qa/resolve/main/spm.model
2.46 MB
- Xet hash:
- ceef70ee5c068f2e8ec2ea787d1566d2f5846b612f0e0a3879edffa246dc91cf
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
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