Instructions to use claudios/JavaBERT-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claudios/JavaBERT-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="claudios/JavaBERT-uncased")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("claudios/JavaBERT-uncased") model = AutoModel.from_pretrained("claudios/JavaBERT-uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48d3d7ba74bd8cc4d796976ab2d14d944b58c077ed82df0f893d71b9e7ea3e6f
- Size of remote file:
- 438 MB
- SHA256:
- 4e645a00b9bff686e1215a9172efcd9de6e9492c30f4f8d16ed752647ddb8231
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