Text Classification
Transformers
Safetensors
code
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HuggingFaceTB/stack-edu-classifier-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/stack-edu-classifier-java with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceTB/stack-edu-classifier-java")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/stack-edu-classifier-java") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/stack-edu-classifier-java", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a51375164121ad467da675d639c26d25a9f7c96d77e5df98c78b80e0baee319
- Size of remote file:
- 497 MB
- SHA256:
- 96bd3a42101ee2e8c13b2dd00669302b03a27ce55a9629947407c24734d82c5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.