Instructions to use DRamos/experimental_topic_classification_GPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DRamos/experimental_topic_classification_GPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DRamos/experimental_topic_classification_GPU")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DRamos/experimental_topic_classification_GPU") model = AutoModelForSequenceClassification.from_pretrained("DRamos/experimental_topic_classification_GPU") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34e018fbba0661b846a1c67838641bae2313b51fc89c5a654784313f92c54410
- Size of remote file:
- 1.88 GB
- SHA256:
- 8918d5ce6e1e2e45b41dce22a6720d2606256466c7c8a34193223215309ed31b
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