Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/roberta-base-wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/roberta-base-wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-wnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-wnli") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5df673f5fe5626fbb0a12464a97f2ab5dcfdc8301837a4d5f4154015eb9f8d8
- Size of remote file:
- 3.31 kB
- SHA256:
- 8318d461f1896811663cb4a134a4e21ff39cfafedfbfbd7e030184ec207f4324
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