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
File size: 349 Bytes
4e9816d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.models.Transformer"
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{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.models.Pooling"
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{
"idx": 2,
"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.models.Normalize"
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