transformers/docs/source/en/model_doc/led.md

LED

Overview

The LED model was proposed in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.

The abstract from the paper is the following:

Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset.

Usage tips

• [LEDForConditionalGeneration] is an extension of [BartForConditionalGeneration] exchanging the traditional self-attention layer with Longformer's chunked self-attention layer. [LEDTokenizer] is an alias of [BartTokenizer].
• LED works very well on long-range sequence-to-sequence tasks where the input_ids largely exceed a length of 1024 tokens.
• LED pads the input_ids to be a multiple of config.attention_window if required. Therefore a small speed-up is gained, when [LEDTokenizer] is used with the pad_to_multiple_of argument.
• LED makes use of global attention by means of the global_attention_mask (see [LongformerModel]). For summarization, it is advised to put global attention only on the first <s> token. For question answering, it is advised to put global attention on all tokens of the question.
• To fine-tune LED on all 16384, gradient checkpointing can be enabled in case training leads to out-of-memory (OOM) errors. This can be done by executing model.gradient_checkpointing_enable(). Moreover, the use_cache=False flag can be used to disable the caching mechanism to save memory.
• 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.

Resources

• A notebook showing how to evaluate LED.
• A notebook showing how to fine-tune LED.
• Text classification task guide
• Question answering task guide
• Translation task guide
• Summarization task guide

LEDConfig

autodoc LEDConfig

LEDTokenizer

autodoc LEDTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary

LEDTokenizerFast

autodoc LEDTokenizerFast

LED specific outputs

autodoc models.led.modeling_led.LEDEncoderBaseModelOutput

autodoc models.led.modeling_led.LEDSeq2SeqModelOutput

autodoc models.led.modeling_led.LEDSeq2SeqLMOutput

autodoc models.led.modeling_led.LEDSeq2SeqSequenceClassifierOutput

autodoc models.led.modeling_led.LEDSeq2SeqQuestionAnsweringModelOutput

autodoc models.led.modeling_tf_led.TFLEDEncoderBaseModelOutput

autodoc models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput

autodoc models.led.modeling_tf_led.TFLEDSeq2SeqLMOutput

LEDModel

autodoc LEDModel - forward

LEDForConditionalGeneration

autodoc LEDForConditionalGeneration - forward

LEDForSequenceClassification

autodoc LEDForSequenceClassification - forward

LEDForQuestionAnswering

autodoc LEDForQuestionAnswering - forward

TFLEDModel

autodoc TFLEDModel - call

TFLEDForConditionalGeneration

autodoc TFLEDForConditionalGeneration - call