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:
- 78677a24173955da17dbdb1725b83a41048fa4a5251737a04163a05c1db53677
- Size of remote file:
- 3.96 kB
- SHA256:
- 6223c0ca066858d1ad06836f513e20a3672aef98662b7d9f02f07849b640d28e
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