Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Ahmed235/roberta_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ahmed235/roberta_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ahmed235/roberta_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ahmed235/roberta_classification") model = AutoModelForSequenceClassification.from_pretrained("Ahmed235/roberta_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29b869cfb8875bd65bc9caeb580d44915cf4acd181cd6400b7b6e74c3846341d
- Size of remote file:
- 4.66 kB
- SHA256:
- 13293c667f7b7b384543cb57c9d9b8ddfe6b4f8e3d8b5923c1c4be7a1e893d43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.