Instructions to use cardiffnlp/roberta-base-topic-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cardiffnlp/roberta-base-topic-multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cardiffnlp/roberta-base-topic-multi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/roberta-base-topic-multi") model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/roberta-base-topic-multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download best_run_hyperparameters.json from cardiffnlp/roberta-base-topic-multi: direct link, hf CLI and curl.
- Browser
- Download file 98 Bytes
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https://huggingface.co/cardiffnlp/roberta-base-topic-multi/resolve/main/best_run_hyperparameters.json
- Command line
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hf download hf://cardiffnlp/roberta-base-topic-multi/best_run_hyperparameters.json
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curl -L -o best_run_hyperparameters.json https://huggingface.co/cardiffnlp/roberta-base-topic-multi/resolve/main/best_run_hyperparameters.json
98 Bytes
| {"learning_rate": 2.9106359131330674e-05, "num_train_epochs": 5, "per_device_train_batch_size": 8} |