Instructions to use activebus/BERT-PT_laptop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use activebus/BERT-PT_laptop with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="activebus/BERT-PT_laptop")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_laptop") model = AutoModelForMaskedLM.from_pretrained("activebus/BERT-PT_laptop") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f75f87c6ca8ebc508e908b961ac81c418e3f33f0f4047bc25ed218f87b07af7
- Size of remote file:
- 438 MB
- SHA256:
- f54a26f6d9164ce311d5df139cd947611166228065f5a120db22fe747022ffb8
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