Instructions to use Reproducibility/naacl22_causalDistilBERT_instance_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Reproducibility/naacl22_causalDistilBERT_instance_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Reproducibility/naacl22_causalDistilBERT_instance_2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Reproducibility/naacl22_causalDistilBERT_instance_2") model = AutoModelForMaskedLM.from_pretrained("Reproducibility/naacl22_causalDistilBERT_instance_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6047074a98e730c8dd80f297b455c2b3d2dbebe206e04b35ad8e4dda4dbe55eb
- Size of remote file:
- 268 MB
- SHA256:
- cbf04c80c86e37b1818d071ca7e47bf8191ffa20ed904bb82832e3739f184268
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