Instructions to use mascIT/bert-tiny-ita-lemma-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mascIT/bert-tiny-ita-lemma-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mascIT/bert-tiny-ita-lemma-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mascIT/bert-tiny-ita-lemma-classification") model = AutoModelForSequenceClassification.from_pretrained("mascIT/bert-tiny-ita-lemma-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d90478d0462d07974d959fcb10214b7a1db24bcc61b72d57f8b254804f961bcf
- Size of remote file:
- 12.2 MB
- SHA256:
- b09ff027db5168d7881ea8fdc13d0e37532d0cfd4090195afc4c9b738b7bdcfc
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