Instructions to use Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa") model = AutoModelForPreTraining.from_pretrained("Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
license: apache-2.0
tags:
- fill-mask
datasets:
- wikipedia
- bookcorpus
80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.
For further details on the model and its result, see our paper and our implementation available here.