Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/roberta-base-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/roberta-base-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-rte") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-rte") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b29259170b0db41366f0ab0401d8f0a0e0d62fb2d2f123fa77b24c5e53be65de
- Size of remote file:
- 499 MB
- SHA256:
- fda9956cf8e7b9c35d6d11664d0aec739c44ea207465c65df28ba0ec16a81e96
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