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 tokenizer.json from DariaaaS/e5-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/DariaaaS/e5-fine-tuned/resolve/main/tokenizer.json
- Command line
-
hf download hf://DariaaaS/e5-fine-tuned/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/DariaaaS/e5-fine-tuned/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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