Text Classification
Transformers
PyTorch
Safetensors
English
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jantrienes/roberta-large-question-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jantrienes/roberta-large-question-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jantrienes/roberta-large-question-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jantrienes/roberta-large-question-classifier") model = AutoModelForSequenceClassification.from_pretrained("jantrienes/roberta-large-question-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d981201e8757aaa717190b58f89973fa86a8e3a4d512e3791af9eee203f07bd7
- Size of remote file:
- 1.42 GB
- SHA256:
- 632e4998b864201d52dc9bb19b649075ce06ceab00b63399c5ab76abf1a8007b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.