Instructions to use Ransaka/sinhala-bert-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ransaka/sinhala-bert-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Ransaka/sinhala-bert-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Ransaka/sinhala-bert-small") model = AutoModelForMaskedLM.from_pretrained("Ransaka/sinhala-bert-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
152b58a
-
Update README.md
6dae603 -
Model save
2b099c8 -
Training in progress, step 140000
a7841d8 -
Training in progress, step 130000
99f1afc -
Training in progress, step 120000
19d6f10 -
Training in progress, step 110000
d754933 -
Training in progress, step 100000
b00301b -
Training in progress, step 90000
3a895cc -
Training in progress, step 80000
242dacd -
Training in progress, step 70000
91e8d77 -
Training in progress, step 60000
05eeae1 -
Training in progress, step 50000
7103f43 -
Training in progress, step 40000
b0cb165 -
Training in progress, step 30000
34bee37 -
Training in progress, step 20000
23ee3bf -
Training in progress, step 10000
ac0a78e -
Model save
622a6b7
-
Model save
579c35a -
Model save
cc3dca9 -
initial commit
3105733