Instructions to use emre/java-RoBERTa-Tara-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emre/java-RoBERTa-Tara-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="emre/java-RoBERTa-Tara-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("emre/java-RoBERTa-Tara-small") model = AutoModelForMaskedLM.from_pretrained("emre/java-RoBERTa-Tara-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (f8c0ae63553bb9aa967e819b7f795e0fd689dc1c) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>4362c8d
-
Update README.md
855a899 -
Update README.md
dfe5518 -
Update README.md
ff6befc -
Upload optimizer.pt with git-lfs
e5a912f -
Update README.md
f3cd44a -
Upload pytorch_model.bin with git-lfs
7d2b371 -
Update README.md
3e5f340 -
Create README.md
8ba5e0b -
Upload config.json
f513685 -
Upload training_args.bin with git-lfs
659da80 -
Upload rng_state.pth with git-lfs
b4b5733 -
Upload scheduler.pt with git-lfs
b435d6d -
Upload trainer_state.json
6342b27 -
add tokenizer
e354968 -
initial commit
156d694