Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/roberta-base-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/roberta-base-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-mrpc") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-mrpc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7daa83021b9e0bceab7a70265975b3872ddd8a88024c7018d82d5896a5d819a9
- Size of remote file:
- 499 MB
- SHA256:
- 0bf4e02ea594646e1c4447b743621ccdfa0f9e166725215ac9998aa5e2a06a8f
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