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-args-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DariaaaS/e5-args-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DariaaaS/e5-args-1") sentences = [ "How many athletes from region 151 have won a medal?", "athletes refer to person_id; region 151 refers to region_id = 151; won a medal refers to medal_id <> 4;", "Rio de Janeiro refers to city_name = 'Rio de Janeiro';", "the highest number of participants refers to MAX(COUNT(person_id)); the lowest number of participants refers to MIN(COUNT(person_id)); Which summer Olympic refers to games_name where season = 'Summer';" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from DariaaaS/e5-args-1: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/DariaaaS/e5-args-1/resolve/main/tokenizer.json
- Command line
-
hf download hf://DariaaaS/e5-args-1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/DariaaaS/e5-args-1/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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