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:
- 02a7cb8cc53738e8dbcf99ba2825b7a65d10b36decaad6df101ee3f43b31e6d4
- Size of remote file:
- 3.31 kB
- SHA256:
- c617849dfe252c6712269f0d0bbb08f023787781df96e6c94be1dd244d267dd8
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