Instructions to use calvegh/experimental_topic_classification_GPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use calvegh/experimental_topic_classification_GPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="calvegh/experimental_topic_classification_GPU", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("calvegh/experimental_topic_classification_GPU") model = AutoModelForSequenceClassification.from_pretrained("calvegh/experimental_topic_classification_GPU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88135a43a1bf87f5f65b8a4fc6d5e3f5b733a6852b94a723d686d1cccfcedb9d
- Size of remote file:
- 1.88 GB
- SHA256:
- 1bc4c7de499b2d22dcd19ad4904e2383a65bd6a8d0e9a008315ef63b8fb5f117
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