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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1261c1125324ea0982ddf12e988accbf4e3c406d95dcbce461288461056de5ac
- Size of remote file:
- 3.58 kB
- SHA256:
- 19b234e1d7b4b347469faef473e6acf9d8cf6efd652f6812f3e9a1fc931f24f3
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