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
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1bb199d7d7f5f1d0911950666420d4322c48dc3f36d2a0daa16f3be72e58edf4
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size 438204248
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