Instructions to use luffycodes/parallel-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luffycodes/parallel-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="luffycodes/parallel-roberta-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("luffycodes/parallel-roberta-large") model = AutoModelForMaskedLM.from_pretrained("luffycodes/parallel-roberta-large") - Notebooks
- Google Colab
- Kaggle
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---
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## Model weights for Parallel Roberta-Large model ##
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To use this model, you need to use the following [modeling_roberta.py](https://github.com/luffycodes/Parallel-Transformers-Pytorch/blob/main/paf_modeling_roberta.py) file.
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- en
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---
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## Model weights for Parallel Roberta-Large model ##
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To use this model, you need to use the following [modeling_roberta.py](https://github.com/luffycodes/Parallel-Transformers-Pytorch/blob/main/paf_modeling_roberta.py) file.
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If you use this work, please cite:
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Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design
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https://arxiv.org/abs/2305.13297
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```
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@misc{sonkar2023investigating,
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title={Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design},
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author={Shashank Sonkar and Richard G. Baraniuk},
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year={2023},
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eprint={2305.13297},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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