Sentence Similarity
sentence-transformers
PyTorch
t5
feature-extraction
mitre_ttps
security
adversarial-threat-annotation
Instructions to use QCRI/monot5_AllDataSplit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use QCRI/monot5_AllDataSplit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("QCRI/monot5_AllDataSplit") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad87ff026c40b5d16cf59c5445d62bbfa8a705cac09a9d39ba5a74bbe509f457
- Size of remote file:
- 3.58 kB
- SHA256:
- 3bf7f99cf852bc92002539201014b91c557f334911f535fad26c8eacc1e46149
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