---
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).