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Update documentation on seq2seq models with absolute positional embeddings, to be in line with Tips section for BERT and GPT2 (#20068)

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Co-authored-by: jordiclive <jordiclive19@imperial.ac.uk>
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Jordan Clive
2022-11-04 15:32:44 +00:00
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docs/source/en/model_doc/led.mdx
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@@ -50,6 +50,8 @@ Tips:
flag can be used to disable the caching mechanism to save memory.
- A notebook showing how to evaluate LED, can be accessed [here](https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing).
- A notebook showing how to fine-tune LED, can be accessed [here](https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing).
- LED is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left.
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).