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:
- 52225e1855778358b4a6f58b490dc216e360ebc1d675e735e902efe97bcba84a
- Size of remote file:
- 1.88 GB
- SHA256:
- 02c5ba1e6ccbbe7bf7a32dc094bb38415ece9b5e43c688c5b5f635e0a5410392
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