text2sql
Code-Llama-2-13B-instruct-text2sql-GGUFArctic-Text2SQL-R1-7BArctic-Text2SQL-R1-7B-i1-GGUFArctic-Text2SQL-R1-7B-GGUFXeAI_-_LLaMa_3.2_3B_Instruct_Text2SQL_Legacy-ggufhuyhoangt2201_-_llama3.2-1b-text2SQL-finetuned-multitableJidouka2.1-ggufNESPED-GEN-Llama-3.2-text2SQL-v0-GGUFNESPED-GEN-Llama-3.2-text2SQL-indentacao-GGUF
Datasets
All datasets matching “text2sql”synthetic-text2sqlEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/synthetic-text2sql.text2sql-eval-results
Text2SQL Evaluation Toolkit — Pre-computed Results
Pre-computed inference and evaluation results produced by the
IBM/text2sql-eval-toolkit
across six text-to-SQL benchmarks.
These artefacts power the toolkit's evaluation dashboard and analysis scripts
without requiring you to re-run multi-hour inference pipelines.
Quick start
Install the toolkit and fetch all results (~3.8 GB):
pip install text2sql-eval-toolkit
text2sql-eval-toolkit results fetch
Fetch a single… See the full description on the dataset page: https://huggingface.co/datasets/text2sql-eval-toolkit/text2sql-eval-results.synthetic-text2sql-qrels
Dataset Card for "synthetic-text2sql-qrels"
More Information needed
text2sql-dataset
Dataset
We built this dataset from several sources combining examples from:
Wikisql
Bird
Spider
Synthetic SQL samples
This dataset has been cleaned and filtered by:
Removing DDL/DML examples (INSERT, UPDATE, DELETE, etc.)
De-duplicating examples based on hashing semantics of SQL and queries
Filtering only SELECT-style analytical queries
synthetic-text2sql-queries-corpusEmploying the CoIR evaluation framework's dataset version, utilize the code below for assessment:
import coir
from coir.data_loader import get_tasks
from coir.evaluation import COIR
from coir.models import YourCustomDEModel
model_name = "intfloat/e5-base-v2"
# Load the model
model = YourCustomDEModel(model_name=model_name)
# Get tasks
#all task ["codetrans-dl","stackoverflow-qa","apps","codefeedback-mt","codefeedback-st","codetrans-contest","synthetic-
# text2sql","cosqa","codesearchnet"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/synthetic-text2sql-queries-corpus.RBAC-Text2SQL-Benchmark
RBAC-Text2SQL Benchmark
Role-conditioned Text-to-SQL instances for evaluating whether LLMs generate SQL that
respects Role-Based Access Control (RBAC) constraints. Each instance pairs a natural
language question with a role policy; the model must either produce a correct SQL query
that touches only authorized resources, or refuse with Sorry, I cannot answer.
Code, evaluation harness, and reproduction instructions:
https://github.com/2020dfff/RBAC-Text2SQL-Benchmark… See the full description on the dataset page: https://huggingface.co/datasets/sharkiefff/RBAC-Text2SQL-Benchmark.