Text Classification
Transformers
Safetensors
English
qwen3
text-to-sql
text2sql
nl2sql
sql
sql-generation
template-matching
template-selection
constrained-decoding
database
nli
paraphrase
reranker
cross-encoder
text-embeddings-inference
Instructions to use smitxxiv/Qwen3-Re4B-SQL-TeCoD-TMM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smitxxiv/Qwen3-Re4B-SQL-TeCoD-TMM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smitxxiv/Qwen3-Re4B-SQL-TeCoD-TMM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smitxxiv/Qwen3-Re4B-SQL-TeCoD-TMM") model = AutoModelForSequenceClassification.from_pretrained("smitxxiv/Qwen3-Re4B-SQL-TeCoD-TMM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15675fe95d6f37ba5b16365e92a5350f5a9f80f87a0754258dd2f60df14589eb
- Size of remote file:
- 11.4 MB
- SHA256:
- dc68ef9483e17b8453514159d4e669d963d343267a9407d51bfe1a5c3d81e7a3
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