The collator for all types of dataset. Remember to add the corresponding collation code after adding a new type of task.

用于封装Seq2SeqModel使其可以做生成任务

tokenizer for image features, event and task type this is NOT inherent from transformers Tokenizer

Multi-headed attention from 'Attention Is All You Need' paper

Head for sentence-level classification tasks.

This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by either offsetting based on padding_idx or

Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a :class:EncoderLayer. Args: conf

This module produces sinusoidal positional embeddings of any length.

Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`DecoderLayer`. Args: config: BartConfig

Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a :class:`EncoderLayer`. Args: co

主要修改是，position的是循环的 This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by either offsetting bas

主要修改是，position的是循环的, 和上面的区别是tag所在的位置不同 This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by ei

给定一个Seq2SeqDecoder，decode出句子