Instructions to use Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa") model = AutoModelForMaskedLM.from_pretrained("Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# 85% Sparse DistilBERT-Base (uncased) Prune OFA
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This model is a result from our paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021.
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license: apache-2.0
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# 85% Sparse DistilBERT-Base (uncased) Prune OFA
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This model is a result from our paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021.
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