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:
- 4e9d4e061e51e811dea048cc5689f88a361b1f092c7049534a1f2d92238e80a7
- Size of remote file:
- 1.88 GB
- SHA256:
- 64b3ff6094e45d816d37551485488122937c67fb70941ea51d656807578bf9a4
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