Instructions to use Reproducibility/naacl22_causalDistilBERT_instance_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Reproducibility/naacl22_causalDistilBERT_instance_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Reproducibility/naacl22_causalDistilBERT_instance_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Reproducibility/naacl22_causalDistilBERT_instance_1") model = AutoModelForMaskedLM.from_pretrained("Reproducibility/naacl22_causalDistilBERT_instance_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Reproducibility/naacl22_causalDistilBERT_instance_1: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Reproducibility/naacl22_causalDistilBERT_instance_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Reproducibility/naacl22_causalDistilBERT_instance_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Reproducibility/naacl22_causalDistilBERT_instance_1/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 2b11d89c179f2c6ffa75eed44165f627e6f1dfc47b1f2567b6d5a4b988d56d9a
- Size of remote file:
- 268 MB
- SHA256:
- 6a9308fcdf67738aeb562f8ecc48da743be6ea267b78bb71643e05e68b0bcf64
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.