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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac70e23ffa81ecd7af81284cc34d564ff80339d85cad5d895b91fa0eca1730e1
- Size of remote file:
- 438 MB
- SHA256:
- bcc05e8dffede4ee5c1be61e5b8e6b727125c854ef4a591a28c75a06566079c3
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