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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anjandash/JavaBERT-mini") model = AutoModelForSequenceClassification.from_pretrained("anjandash/JavaBERT-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anjandash/JavaBERT-mini: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/anjandash/JavaBERT-mini/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anjandash/JavaBERT-mini/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anjandash/JavaBERT-mini/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 4f74f8e325815bdee60c6a562236b9d9d1a71a2a0f43ff8dc736c478cc1b2fb2
- Size of remote file:
- 541 MB
- SHA256:
- d0c371486c3dbeba52fa5dfdf0339d683e07ad48f522bb5591e7b05b29b168e4
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