Text Classification
Transformers
Safetensors
code
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HuggingFaceTB/stack-edu-classifier-shell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/stack-edu-classifier-shell with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceTB/stack-edu-classifier-shell")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/stack-edu-classifier-shell") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/stack-edu-classifier-shell", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f39dc41147c5e5354e7d756b53f0784670b531055d67d5daae0abf023dfbdddc
- Size of remote file:
- 497 MB
- SHA256:
- 9b43a0ca367839c85086b603b2adf843f9a623e3d21e409b7467e1f20e7be0c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.