Instructions to use dbourget/pb-small-53K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbourget/pb-small-53K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbourget/pb-small-53K")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbourget/pb-small-53K") model = AutoModelForMaskedLM.from_pretrained("dbourget/pb-small-53K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b32106a83f81cd965a6f35aab2e5ba6c33a9ba7f6a0eeaad59cd05c6327c7143
- Size of remote file:
- 438 MB
- SHA256:
- 46ec6ef0af35c382183bee164c6041ce761adee0c23364f21939cb8cfbcb7b78
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