Instructions to use DipanAI/TesAKantaiBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DipanAI/TesAKantaiBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DipanAI/TesAKantaiBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("DipanAI/TesAKantaiBERT") model = AutoModelForMaskedLM.from_pretrained("DipanAI/TesAKantaiBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6fe6eab3ed4ba1e154db3c7d88d7575593836c9357ac3c4165deb8678fe8e27
- Size of remote file:
- 334 MB
- SHA256:
- 532bc3b6952055a9280e83e0b72b7ce77bb9ab097307c6ace7dd1b927feb051b
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