Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
mpnet
feature-extraction
Trained with AutoTrain
text-embeddings-inference
Instructions to use sidbrahim/autotrain-NarrativesAnalogues-MPNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sidbrahim/autotrain-NarrativesAnalogues-MPNET with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sidbrahim/autotrain-NarrativesAnalogues-MPNET") sentences = [ "search_query: i love autotrain", "search_query: huggingface auto train", "search_query: hugging face auto train", "search_query: i love autotrain" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download checkpoint-60/optimizer.pt from sidbrahim/autotrain-NarrativesAnalogues-MPNET: direct link, hf CLI and curl.
- Browser
- Download file 871 MB
-
https://huggingface.co/sidbrahim/autotrain-NarrativesAnalogues-MPNET/resolve/main/checkpoint-60/optimizer.pt
- Command line
-
hf download hf://sidbrahim/autotrain-NarrativesAnalogues-MPNET/checkpoint-60/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/sidbrahim/autotrain-NarrativesAnalogues-MPNET/resolve/main/checkpoint-60/optimizer.pt
871 MB
- Xet hash:
- b0eb32955986c788d31095163739d37f8259301137b20d4b5ac3e842c645b80e
- Size of remote file:
- 871 MB
- SHA256:
- c16f30c6b66701897b7ebff95a9041b390f9a2ddccab6a2f404390aacb42adaa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.