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:
- e6eee40c75a2cd737c253feb9557673fa756120192da107cfd5811f0a33d7bde
- Size of remote file:
- 499 MB
- SHA256:
- 28b0a8684e9d906adafde22f6e26d4f676f3cb86a61014c6ec5a6b15127d280c
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