---
base_model: FacebookAI/roberta-base
datasets:
- SynthSTEL/styledistance_training_triplets
tags:
- datadreamer
- datadreamer-0.35.0
- synthetic
- sentence-transformers
- feature-extraction
- sentence-similarity
library_name: sentence-transformers
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?"
pipeline_tag: sentence-similarity
---
# 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).