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 modules.json from DariaaaS/e5-args-1: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/DariaaaS/e5-args-1/resolve/main/modules.json
- Command line
-
hf download hf://DariaaaS/e5-args-1/modules.json
-
curl -L -o modules.json https://huggingface.co/DariaaaS/e5-args-1/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" | |
| } | |
| ] |