Instructions to use anjandash/JavaBERT-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anjandash/JavaBERT-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anjandash/JavaBERT-mini")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anjandash/JavaBERT-mini") model = AutoModelForSequenceClassification.from_pretrained("anjandash/JavaBERT-mini") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75afc579b31d253352dcc752c8163119da44f80f59e16cd086882bb5a840923d
- Size of remote file:
- 541 MB
- SHA256:
- 7de810b3f312aecc96faaa27da7b5425ea9d6418a37703829905d079efb83af8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.