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  pipeline_tag: text2text-generation
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  tags:
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  - t5x
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- - encode-decoder
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  ---
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  Pile-T5 XXL is an Encoder-Decoder model trained on [the Pile](https://pile.eleuther.ai/) using the [T5x](https://github.com/google-research/t5x) library. The model was trained for 2 million steps or roughly 2 trillion tokens using MLM-objective similar to the original T5 model.
 
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  ### Model Details
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@@ -133,16 +134,17 @@ checkpoints that can be used for finetuning with the T5x library, refer to [here
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  ### Evaluations
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- TBD
 
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  ### BibTeX
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  ```
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- @article{2024t5v2,
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  author = {Lintang Sutawika and Aran Komatsuzaki and Colin Raffel},
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- title = {Pile T5, an update of T5},
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  year = {2024},
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- url = {}
 
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  }
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  ```
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-
 
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  pipeline_tag: text2text-generation
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  tags:
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  - t5x
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+ - encoder-decoder
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  ---
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  Pile-T5 XXL is an Encoder-Decoder model trained on [the Pile](https://pile.eleuther.ai/) using the [T5x](https://github.com/google-research/t5x) library. The model was trained for 2 million steps or roughly 2 trillion tokens using MLM-objective similar to the original T5 model.
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+ The HF version of Pile-T5 XXL borrows UMT5's model implementation as it uses scalable model implementation from T5x and uses `LlamaTokenizer`.
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  ### Model Details
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  ### Evaluations
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+ Pile-T5 XXL was evaluated on SuperGLUE, CodeXGLUE. A Flan-finetuned version was evaluated on Flan Held In tasks, MMLU and BBH.
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+ Results can be seen in the [blogpost](https://blog.eleuther.ai/pile-t5/)
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  ### BibTeX
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  ```
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+ @misc{2024PileT5,
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  author = {Lintang Sutawika and Aran Komatsuzaki and Colin Raffel},
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+ title = {Pile-T5},
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  year = {2024},
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+ url = {https://blog.eleuther.ai/pile-t5/},
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+ note = {Blog post},
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  }
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  ```