metadata
base_model: FacebookAI/roberta-base
datasets:
- SynthSTEL/styledistance_training_triplets
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- datadreamer
- datadreamer-0.35.0
- synthetic
- sentence-transformers
- feature-extraction
- sentence-similarity
widget:
- example_title: Example 1
source_sentence: >-
Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from
future competitions.
sentences:
- >-
We're raising funds 2 improve our school's storage facilities and add
new playground equipment!
- >-
Did you hear about the Wales wing? He'll hate to withdraw due to
injuries from future competitions.
- example_title: Example 2
source_sentence: >-
You planned the DesignMeets Decades of Design event; you executed it
perfectly.
sentences:
- We'll find it hard to prove the thief didn't face a real threat!
- >-
You orchestrated the DesignMeets Decades of Design gathering; you
actualized it flawlessly.
- example_title: Example 3
source_sentence: >-
Did the William Barr maintain a commitment to allow Robert Mueller to
finish the inquiry?
sentences:
- >-
Will the artist be compiling a music album, or will there be a different
focus in the future?
- >-
Did William Barr maintain commitment to allow Robert Mueller to finish
inquiry?
Model Card
Example Usage
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer('SynthSTEL/styledistance') # Load model
input = model.encode("Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from future competitions.")
others = model.encode(["We're raising funds 2 improve our school's storage facilities and add new playground equipment!", "Did you hear about the Wales wing? He'll hate to withdraw due to injuries from future competitions."])
print(cos_sim(input, others))
This model was trained with a synthetic dataset with DataDreamer 🤖💤. The synthetic dataset card and model card can be found here. The training arguments can be found here.