Instructions to use JLake310/roberta-large-topic-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JLake310/roberta-large-topic-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JLake310/roberta-large-topic-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JLake310/roberta-large-topic-classification") model = AutoModelForSequenceClassification.from_pretrained("JLake310/roberta-large-topic-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26f8d677e84bbd7835279898b4d56a2f1196e5080b850693a9d3854bdc83958d
- Size of remote file:
- 1.35 GB
- SHA256:
- ea1bb5b1adad337551299c284ff1ba70d5475b3e2acbd391959ba19d26ee902c
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