Instructions to use mpapucci/bert-topic-classification-tag-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpapucci/bert-topic-classification-tag-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mpapucci/bert-topic-classification-tag-it")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mpapucci/bert-topic-classification-tag-it") model = AutoModelForSequenceClassification.from_pretrained("mpapucci/bert-topic-classification-tag-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47313f30f36ca9dbdaf70219a8f02f911ec0875c92a822c096ccd86e5a3f250b
- Size of remote file:
- 440 MB
- SHA256:
- 0c21e266022b2c32ac617e1738882ffe070f038466132c2960dfd205e461ca2e
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