Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:300000
loss:CoSENTLoss
text-embeddings-inference
Instructions to use s2593817/sft-sql-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use s2593817/sft-sql-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("s2593817/sft-sql-embedding") sentences = [ "SELECT DISTINCT count(alias3.col1) , alias1.col2 FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col2 = alias2.col2 JOIN table3 AS alias3 ON alias1.col1 = alias3.col1 WHERE alias2.col3 = str AND alias3.year = num GROUP BY alias1.col2", "SELECT col1 , avg(col2) FROM table1 WHERE col3 LIKE str GROUP BY col1", "SELECT col1 , col2 FROM table1 WHERE col3 LIKE str GROUP BY col1 ORDER BY count(*) DESC LIMIT num", "SELECT col1 , avg(col2) FROM table1 GROUP BY col1 ORDER BY avg(col2)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 1,656 Bytes
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"tokenize_chinese_chars": true,
"tokenizer_class": "MPNetTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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