Instructions to use smallbenchnlp/bert-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smallbenchnlp/bert-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="smallbenchnlp/bert-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("smallbenchnlp/bert-small") model = AutoModelForMaskedLM.from_pretrained("smallbenchnlp/bert-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b94fded3f199aa48498e6ad33ef35e1c7418faba273d974be84025fe7a7f394
- Size of remote file:
- 54.3 MB
- SHA256:
- 6efc8f66c02ff1bba96f27063001ef40fdf99876ae82c4f3b1c70d2da615d498
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