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
Download pytorch_model.bin from QCRI/monot5_AllDataSplit: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/QCRI/monot5_AllDataSplit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://QCRI/monot5_AllDataSplit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/QCRI/monot5_AllDataSplit/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- 3e72350e7be1736affce186bc1e75b504d61ec3136dee4602acf433781da8f52
- Size of remote file:
- 892 MB
- SHA256:
- ad9b111c8019eb0eae101ab25bc6f94304b3e1af9e118602b47c8d8ae4367b3c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.