Text Classification
Transformers
Safetensors
code
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HuggingFaceTB/stack-edu-classifier-php with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/stack-edu-classifier-php with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceTB/stack-edu-classifier-php")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/stack-edu-classifier-php") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/stack-edu-classifier-php") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7107a0bba81893d2940e5866ece611b33b87c8f760e613b27f42b32e0922bc4e
- Size of remote file:
- 497 MB
- SHA256:
- c8e90160c4130d165a3b54f3023d5ef7dd072d8436a64ac2294046a30e5dbcdd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.