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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- rouge |
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tags: |
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- nanoT5 |
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datasets: |
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- allenai/c4 |
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--- |
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[Google's T5-v1.1-base](https://huggingface.co/google/t5-v1_1-base) pre-trained for 24 hours (80k steps / 256 batch size) on a single GPU in [nanoT5](https://github.com/PiotrNawrot/nanoT5) library for efficient pre-training. |
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For more details about the model refer to the original [paper](https://arxiv.org/pdf/2002.05202.pdf) and original [model weights](https://huggingface.co/google/t5-v1_1-base). |
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It can be further fine-tuned on SuperNatural-Instructions dataset to achieve comparable performance to the same model pre-trained on 150x more data through "a combination of model and data parallelism [...] on slices of Cloud TPU Pods", each with 1024 TPUs. |
