Instructions to use akahana/tinybert-javanese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akahana/tinybert-javanese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="akahana/tinybert-javanese")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("akahana/tinybert-javanese") model = AutoModelForMaskedLM.from_pretrained("akahana/tinybert-javanese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d094e11559886fb9d4ac43292e5a668312745a15f8bd626d45782e66ed913b5
- Size of remote file:
- 5.18 kB
- SHA256:
- 02c2557b01f0a900e6d0f1d01238cd2efba7c0987aefc917f1b8b86445004309
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