Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use JeremiahZ/roberta-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/roberta-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/roberta-base-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/roberta-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/roberta-base-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2223007a6cdd0a7d4079944bdc45e0432c239db6e799a408aad5f844c5664cf3
- Size of remote file:
- 3.31 kB
- SHA256:
- 1f55682bda2646faee2ba7a4fd6bac0576e20f731bc97e057c6c64c2706b471a
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