Instructions to use veronica320/MPE_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use veronica320/MPE_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="veronica320/MPE_roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("veronica320/MPE_roberta") model = AutoModelForSequenceClassification.from_pretrained("veronica320/MPE_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7df5e809f07330d2769b05242689ba8b5a1a4ab826912caf9245377339e7d1b
- Size of remote file:
- 499 MB
- SHA256:
- 5311a64e67d4e6599dde16865413e988e8a94d41dc181dfaaa63697de75fa5c3
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