Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:100
loss:TripletLoss
text-embeddings-inference
Instructions to use DariaaaS/e5-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DariaaaS/e5-fine-tuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DariaaaS/e5-fine-tuned") sentences = [ "What is the average household income in the city known as \"Danzig\"?", "the most bad aliases refer to MAX(COUNT(bad_alias));", "Greeneville is the city;", "average household income refers to avg_income_per_household; city known as \"Danzig\" refers to bad_alias = 'Danzig';" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from DariaaaS/e5-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/DariaaaS/e5-fine-tuned/resolve/main/modules.json
- Command line
-
hf download hf://DariaaaS/e5-fine-tuned/modules.json
-
curl -L -o modules.json https://huggingface.co/DariaaaS/e5-fine-tuned/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" | |
| } | |
| ] |