Text Classification
Transformers
Safetensors
code
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HuggingFaceTB/stack-edu-classifier-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/stack-edu-classifier-sql with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceTB/stack-edu-classifier-sql")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/stack-edu-classifier-sql") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/stack-edu-classifier-sql") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24e5838536a2f2e573ef00b3a407918f5614190b91e284454f90646a1d72bd87
- Size of remote file:
- 5.3 kB
- SHA256:
- 92fd8cae19a8366b8ceffb78915d5c4c7e0150cc8c2b468350f9dcc5f8be91f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.