Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/roberta-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/roberta-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/roberta-classification-10ksamples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/roberta-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/roberta-classification-10ksamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3709d643789c529cb3e08a08b44524ff1b58ce19772f2cb20930ae8cee5ed762
- Size of remote file:
- 4.03 kB
- SHA256:
- 78f31e768ed9b844ef038fb256d6871d0bd8f9a316cce9032d2421f0235277ed
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