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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from Reproducibility/naacl22_causalDistilBERT_instance_3: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Reproducibility/naacl22_causalDistilBERT_instance_3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Reproducibility/naacl22_causalDistilBERT_instance_3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Reproducibility/naacl22_causalDistilBERT_instance_3/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 94c980a8a6855b526ff6a97b9ff8d8b06e41d4ffc494d8f6d25b41e2d984ca71
- Size of remote file:
- 268 MB
- SHA256:
- 462cdde451e992ffec551f97d9404c4853c385f1acb7bbf11979dd49bc580e58
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