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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 262578b62a6fd5892e033fa7444dfbd528e44cf9e62d119dde268881ba1253d6
- Size of remote file:
- 5.3 kB
- SHA256:
- 43a20d0543893c9be8a3ba5d78d838a92e8a552e6b62c17019dfe6448a774612
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.