Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:98646
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use adugeen/facts-reranker-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adugeen/facts-reranker-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adugeen/facts-reranker-e5-base") sentences = [ "Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context\nDialogue Context: bot_0: Hey, how are things today?", "Followup phrase: i am painting a mural in bedroom of Sunset on beach with black backgound.", "Followup phrase: I have a kitten.", "Followup phrase: I live in Georgia." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from adugeen/facts-reranker-e5-base: direct link, hf CLI and curl.
- Browser
- Download file 6.29 kB
-
https://huggingface.co/adugeen/facts-reranker-e5-base/resolve/main/training_args.bin
- Command line
-
hf download hf://adugeen/facts-reranker-e5-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/adugeen/facts-reranker-e5-base/resolve/main/training_args.bin
6.29 kB
- Xet hash:
- 8e95182cdc80cdc9559dc843d28a6b9a085bedb69c51f72092e1c28b206403d0
- Size of remote file:
- 6.29 kB
- SHA256:
- 5384afe07551799d6b6a2eb845c3242a4f862fa7b35e6062940ffd617d66485c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.