Sentence Similarity
sentence-transformers
Safetensors
English
roberta
datadreamer
datadreamer-0.35.0
Synthetic
feature-extraction
text-embeddings-inference
Instructions to use StyleDistance/styledistance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use StyleDistance/styledistance with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("StyleDistance/styledistance") sentences = [ "Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from future competitions.", "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." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Inference
- Notebooks
- Google Colab
- Kaggle
|
Download README.md from StyleDistance/styledistance: direct link, hf CLI and curl.
- Browser
- Download file 2.25 kB
-
https://huggingface.co/StyleDistance/styledistance/resolve/a2ef7ffb93f4af8d9727f65b602e260ba5d13607/README.md
- Command line
-
hf download hf://StyleDistance/styledistance@a2ef7ffb93f4af8d9727f65b602e260ba5d13607/README.md
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curl -L -o README.md https://huggingface.co/StyleDistance/styledistance/resolve/a2ef7ffb93f4af8d9727f65b602e260ba5d13607/README.md
2.25 kB
| 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). |