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
| 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](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021. | |
| For further details on the model and its result, see our paper and our implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all). | |