Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/roberta-base-qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/roberta-base-qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-qnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-qnli") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a4b34a769f5e22aeb034b122fa7dfe142d1468406407bfb4340f4c2797bfa14
- Size of remote file:
- 499 MB
- SHA256:
- 3054bdb55deaddfb316ad94a01ac8a89b272e104c2f81d6c7ecf02a3b2cc9fbb
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