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 1_Pooling/config.json from DariaaaS/e5-args-1: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/DariaaaS/e5-args-1/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://DariaaaS/e5-args-1/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/DariaaaS/e5-args-1/resolve/main/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |