Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
sts
embeddings
symmetric-augmentation
fine-tuned
text-embeddings-inference
Instructions to use blueprint-ai/SymSTS-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use blueprint-ai/SymSTS-MiniLM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("blueprint-ai/SymSTS-MiniLM") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -587,7 +587,7 @@ for t in TASKS:
|
|
| 587 |
b = base.get(t, 0)
|
| 588 |
o = ours.get(t, 0)
|
| 589 |
print(f"{t:<16} {b:>8.4f} {o:>8.4f} {o-b:>+8.4f}")
|
| 590 |
-
```
|
| 591 |
|
| 592 |
###10. Limitations
|
| 593 |
Trained on a small curated dataset (~16,000 augmented pairs). May not generalize well
|
|
|
|
| 587 |
b = base.get(t, 0)
|
| 588 |
o = ours.get(t, 0)
|
| 589 |
print(f"{t:<16} {b:>8.4f} {o:>8.4f} {o-b:>+8.4f}")
|
| 590 |
+
```
|
| 591 |
|
| 592 |
###10. Limitations
|
| 593 |
Trained on a small curated dataset (~16,000 augmented pairs). May not generalize well
|