Instructions to use Reproducibility/naacl22_causalDistilBERT_instance_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Reproducibility/naacl22_causalDistilBERT_instance_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Reproducibility/naacl22_causalDistilBERT_instance_3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Reproducibility/naacl22_causalDistilBERT_instance_3") model = AutoModelForMaskedLM.from_pretrained("Reproducibility/naacl22_causalDistilBERT_instance_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6e07cede58c5e7b0f67c44002a5786d2c8aa63bd7f814dcb4d14c68c2263627
- Size of remote file:
- 268 MB
- SHA256:
- 4a67e51eed503945c314a2b2252e8e0b0e92c5d4fb00b918af252d5456f164d9
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