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:
- 4a271cecd260cd314b3401b55200d9a993e4ec3a4cf0fa4d36abe3d2ccd7b9a0
- Size of remote file:
- 1.35 GB
- SHA256:
- b3bfaaceb314f95e84004a4b15534d8737349674c7326242ffc29d886da21ae4
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