Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
semantic-search
automotive-parts
synonyms
Instructions to use InstalilyAI/synonym-transformer-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use InstalilyAI/synonym-transformer-LoRA with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("InstalilyAI/synonym-transformer-LoRA") 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
Liam Wilbur
Add LoRA fine-tuned transformer with reduced degradation but more balanced synonym performance
6cd99c7 Download modules.json from InstalilyAI/synonym-transformer-LoRA: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/InstalilyAI/synonym-transformer-LoRA/resolve/main/modules.json
- Command line
-
hf download hf://InstalilyAI/synonym-transformer-LoRA/modules.json
-
curl -L -o modules.json https://huggingface.co/InstalilyAI/synonym-transformer-LoRA/resolve/main/modules.json
349 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |