Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Adarsh921/multi_qa_mpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Adarsh921/multi_qa_mpnet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Adarsh921/multi_qa_mpnet") 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 Adarsh921/multi_qa_mpnet with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Adarsh921/multi_qa_mpnet") model = AutoModel.from_pretrained("Adarsh921/multi_qa_mpnet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf48294088313fb947c853f6036836466a4513b3677e29241a394c72bce61718
- Size of remote file:
- 438 MB
- SHA256:
- 834c5ac9ce33b0de9bb041ca65f0edadc5418725ac54cedbd6ffc88beb828808
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