Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use JoBeer/all-mpnet-base-v2-eclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JoBeer/all-mpnet-base-v2-eclass with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JoBeer/all-mpnet-base-v2-eclass") 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 JoBeer/all-mpnet-base-v2-eclass with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JoBeer/all-mpnet-base-v2-eclass") model = AutoModel.from_pretrained("JoBeer/all-mpnet-base-v2-eclass") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Update dataset YAML metadata for model
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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# all-mpnet-base-v2-eclass
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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datasets: JoBeer/eclassTrainST
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pipeline_tag: sentence-similarity
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# all-mpnet-base-v2-eclass
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