Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
mpnet
feature-extraction
negation
text-embeddings-inference
Instructions to use tum-nlp/NegMPNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tum-nlp/NegMPNet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tum-nlp/NegMPNet") 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 tum-nlp/NegMPNet with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tum-nlp/NegMPNet") model = AutoModel.from_pretrained("tum-nlp/NegMPNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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size 437971872
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