Instructions to use n2vec/Bertikal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use n2vec/Bertikal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="n2vec/Bertikal")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("n2vec/Bertikal") model = AutoModelForMaskedLM.from_pretrained("n2vec/Bertikal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a78eefc4d1d0b67e8e5263ced70866e911f809b21ff548990ec4c5f6793bbd49
- Size of remote file:
- 961 Bytes
- SHA256:
- be6b924f0038901aafe542eb66617dd0e1548e71c1b355e0ec090a0b80b30423
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