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---
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
[Add more information here](https://huggingface.co./templates/model-card-example)
## Example Usage
```python3
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 🤖💤](https://datadreamer.dev). The synthetic dataset card and model card can be found [here](datadreamer.json). The training arguments can be found [here](training_args.json). |