cross-encoder/stsb-roberta-base

🤗 Hugging Face sourcetext-rankingapache-2.0125M params499 MBsafetensors✓ 14 checksumsupdated today

Submit in one command

Run it next to your model folder. It makes the torrent, checks your files against Hugging Face, and submits it. You just start seeding and paste your key from your account. It only reads your files and never changes them. Read the script first if you like.

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo cross-encoder/stsb-roberta-base ./model-folder

Needs a seeder →

Cross-Encoder for Semantic Textual Similarity

This model was trained using SentenceTransformers Cross-Encoder class.

Training Data

This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.

Usage and Performance

Pre-trained models can be used like this:

from sentence_transformers import CrossEncoder

model = CrossEncoder('cross-encoder/stsb-roberta-base')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])

The model will predict scores for the pairs ('Sentence 1', 'Sentence 2') and ('Sentence 3', 'Sentence 4').

You can use this model also without sentence_transformers and by just using Transformers AutoModel class