Text Classification
Transformers
Safetensors
code
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HuggingFaceTB/stack-edu-classifier-markdown with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/stack-edu-classifier-markdown with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceTB/stack-edu-classifier-markdown")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/stack-edu-classifier-markdown") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/stack-edu-classifier-markdown") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c5e10ff8353f8b53ba6a7ed92fdd5fd5c861e773f1c2bb88946e99b50188139
- Size of remote file:
- 5.37 kB
- SHA256:
- 7b953c46933ae3b6f24a3d95e90a5272506621d785372cea90b2a928b3c8fed1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.