# ProphetNet¶

DISCLAIMER: If you see something strange, file a Github Issue and assign @patrickvonplaten

## Overview¶

The ProphetNet model was proposed in ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou on 13 Jan, 2020.

ProphetNet is an encoder-decoder model and can predict n-future tokens for “ngram” language modeling instead of just the next token.

The abstract from the paper is the following:

In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.

The Authors’ code can be found here.

## ProphetNetConfig¶

class transformers.ProphetNetConfig(activation_dropout=0.1, activation_function='gelu', vocab_size=30522, hidden_size=1024, encoder_ffn_dim=4096, num_encoder_layers=12, num_encoder_attention_heads=16, decoder_ffn_dim=4096, num_decoder_layers=12, num_decoder_attention_heads=16, attention_dropout=0.1, dropout=0.1, max_position_embeddings=512, init_std=0.02, is_encoder_decoder=True, add_cross_attention=True, decoder_start_token_id=0, ngram=2, num_buckets=32, relative_max_distance=128, disable_ngram_loss=False, eps=0.0, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, **kwargs)[source]

This is the configuration class to store the configuration of a ProphetNetModel. It is used to instantiate a ProphetNet model according to the specified arguments, defining the model architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Parameters
• activation_dropout (float, optional, defaults to 0.1) – The dropout ratio for activations inside the fully connected layer.

• activation_function (str or function, optional, defaults to "gelu") – The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "silu" and "gelu_new" are supported.

• vocab_size (int, optional, defaults to 30522) – Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling ProphetNetModel.

• hidden_size (int, optional, defaults to 1024) – Dimensionality of the layers and the pooler layer.

• encoder_ffn_dim (int, optional, defaults to 4096) – Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.

• num_encoder_layers (int, optional, defaults to 12) – Number of encoder layers.

• num_encoder_attention_heads (int, optional, defaults to 16) – Number of attention heads for each attention layer in the Transformer encoder.

• decoder_ffn_dim (int, optional, defaults to 4096) – Dimensionality of the intermediate (often named feed-forward) layer in decoder.

• num_decoder_layers (int, optional, defaults to 12) – Number of decoder layers.

• num_decoder_attention_heads (int, optional, defaults to 16) – Number of attention heads for each attention layer in the Transformer decoder.

• attention_dropout (float, optional, defaults to 0.1) – The dropout ratio for the attention probabilities.

• dropout (float, optional, defaults to 0.1) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

• max_position_embeddings (int, optional, defaults to 512) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

• init_std (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

• add_cross_attention (bool, optional, defaults to True) – Whether cross-attention layers should be added to the model.

• is_encoder_decoder (bool, optional, defaults to True) – Whether this is an encoder/decoder model.

• pad_token_id (int, optional, defaults to 1) – Padding token id.

• bos_token_id (int, optional, defaults to 0) – Beginning of stream token id.

• eos_token_id (int, optional, defaults to 2) – End of stream token id.

• ngram (int, optional, defaults to 2) – Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first token.

• num_buckets (int, optional, defaults to 32) – The number of buckets to use for each attention layer. This is for relative position calculation. See the T5 paper for more details.

• relative_max_distance (int, optional, defaults to 128) – Relative distances greater than this number will be put into the last same bucket. This is for relative position calculation. See the T5 paper for more details.

• disable_ngram_loss (bool, optional, defaults to False) – Whether be trained predicting only the next first token.

• eps (float, optional, defaults to 0.0) – Controls the epsilon parameter value for label smoothing in the loss calculation. If set to 0, no label smoothing is performed.

• use_cache (bool, optional, defaults to True) – Whether or not the model should return the last key/values attentions (not used by all models).

## ProphetNetTokenizer¶

class transformers.ProphetNetTokenizer(vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', x_sep_token='[X_SEP]', pad_token='[PAD]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, **kwargs)[source]

Construct a ProphetNetTokenizer. Based on WordPiece.

This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

Parameters
• vocab_file (str) – File containing the vocabulary.

• do_lower_case (bool, optional, defaults to True) – Whether or not to lowercase the input when tokenizing.

• do_basic_tokenize (bool, optional, defaults to True) – Whether or not to do basic tokenization before WordPiece.

• never_split (Iterable, optional) – Collection of tokens which will never be split during tokenization. Only has an effect when do_basic_tokenize=True

• unk_token (str, optional, defaults to "[UNK]") – The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

• sep_token (str, optional, defaults to "[SEP]") – The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.

• x_sep_token (str, optional, defaults to "[X_SEP]") – Special second separator token, which can be generated by ProphetNetForConditionalGeneration. It is used to separate bullet-point like sentences in summarization, e.g..

• pad_token (str, optional, defaults to "[PAD]") – The token used for padding, for example when batching sequences of different lengths.

• cls_token (str, optional, defaults to "[CLS]") – The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.

• mask_token (str, optional, defaults to "[MASK]") – The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.

• tokenize_chinese_chars (bool, optional, defaults to True) –

Whether or not to tokenize Chinese characters.

This should likely be deactivated for Japanese (see this issue).

• strip_accents – (bool, optional): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for lowercase (as in the original BERT).

build_inputs_with_special_tokens(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT sequence has the following format:

• single sequence: [CLS] X [SEP]

• pair of sequences: [CLS] A [SEP] B [SEP]

Parameters
• token_ids_0 (List[int]) – List of IDs to which the special tokens will be added.

• token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

Returns

List of input IDs with the appropriate special tokens.

Return type

List[int]

convert_tokens_to_string(tokens)[source]

Converts a sequence of tokens (string) in a single string.

create_token_type_ids_from_sequences(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ProphetNet sequence pair mask has the following format:

0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |


If token_ids_1 is None, this method only returns the first portion of the mask (0s).

Parameters
• token_ids_0 (List[int]) – List of IDs.

• token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

Returns

List of token type IDs according to the given sequence(s).

Return type

List[int]

get_special_tokens_mask(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) → List[int][source]

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

Parameters
• token_ids_0 (List[int]) – List of IDs.

• token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

• already_has_special_tokens (bool, optional, defaults to False) – Whether or not the token list is already formatted with special tokens for the model.

Returns

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Return type

List[int]

get_vocab()[source]

Returns the vocabulary as a dictionary of token to index.

tokenizer.get_vocab()[token] is equivalent to tokenizer.convert_tokens_to_ids(token) when token is in the vocab.

Returns

The vocabulary.

Return type

Dict[str, int]

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

Parameters
• save_directory (str) – The directory in which to save the vocabulary.

• filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.

Returns

Paths to the files saved.

Return type

Tuple(str)

property vocab_size

Size of the base vocabulary (without the added tokens).

Type

int

## ProphetNet specific outputs¶

class transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput(loss: Optional[torch.FloatTensor] = None, logits: torch.FloatTensor = None, logits_ngram: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, decoder_ngram_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, decoder_ngram_attentions: Optional[Tuple[torch.FloatTensor]] = None, cross_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_last_hidden_state: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]

Base class for sequence-to-sequence language models outputs.

Parameters
• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Language modeling loss.

• logits (torch.FloatTensor of shape (batch_size, decoder_sequence_length, config.vocab_size)) – Prediction scores of the main stream language modeling head (scores for each vocabulary token before SoftMax).

• logits_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) – Prediction scores of the predict stream language modeling head (scores for each vocabulary token before SoftMax).

• past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) –

List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

• decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, decoder_sequence_length, hidden_size).

Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.

• decoder_ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, ngram * decoder_sequence_length, hidden_size).

Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.

• decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• decoder_ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).

Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the

• encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, encoder_sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

• encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, encoder_sequence_length, hidden_size).

Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

• encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length). Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

class transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput(last_hidden_state: torch.FloatTensor, last_hidden_state_ngram: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, decoder_ngram_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, decoder_ngram_attentions: Optional[Tuple[torch.FloatTensor]] = None, cross_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_last_hidden_state: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]

Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential decoding.

Parameters
• last_hidden_state (torch.FloatTensor of shape (batch_size, decoder_sequence_length, hidden_size)) –

Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.

If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

• last_hidden_state_ngram (torch.FloatTensor of shape (batch_size,ngram * decoder_sequence_length, config.vocab_size)) – Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.

• past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) –

List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

• decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, decoder_sequence_length, hidden_size).

Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.

• decoder_ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, ngram * decoder_sequence_length, hidden_size).

Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.

• decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• decoder_ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the

• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).

Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the

• encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, encoder_sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

• encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, encoder_sequence_length, hidden_size).

Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

• encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length).

Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

class transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput(last_hidden_state: torch.FloatTensor, last_hidden_state_ngram: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, hidden_states: Optional[Tuple[torch.FloatTensor]] = None, hidden_states_ngram: Optional[Tuple[torch.FloatTensor]] = None, attentions: Optional[Tuple[torch.FloatTensor]] = None, ngram_attentions: Optional[Tuple[torch.FloatTensor]] = None, cross_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]

Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).

Parameters
• last_hidden_state (torch.FloatTensor of shape (batch_size, decoder_sequence_length, hidden_size)) –

Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.

If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

• last_hidden_state_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) – Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.

• past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) –

List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, decoder_sequence_length, hidden_size).

Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.

• ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, ngram * decoder_sequence_length, hidden_size).

Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.

• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the

• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).

Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the

class transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput(loss: Optional[torch.FloatTensor] = None, logits: torch.FloatTensor = None, logits_ngram: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, hidden_states: Optional[Tuple[torch.FloatTensor]] = None, hidden_states_ngram: Optional[Tuple[torch.FloatTensor]] = None, attentions: Optional[Tuple[torch.FloatTensor]] = None, ngram_attentions: Optional[Tuple[torch.FloatTensor]] = None, cross_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]

Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).

Parameters
• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Language modeling loss.

• logits (torch.FloatTensor of shape (batch_size, decoder_sequence_length, config.vocab_size)) – Prediction scores of the main stream language modeling head (scores for each vocabulary token before SoftMax).

• logits_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) – Prediction scores of the predict stream language modeling head (scores for each vocabulary token before SoftMax).

• past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) –

List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, decoder_sequence_length, hidden_size).

Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.

• ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) –

Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, ngram * decoder_sequence_length, hidden_size).

Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.

• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the

• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) –

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).

Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the

## ProphetNetModel¶

class transformers.ProphetNetModel(config)[source]

The bare ProphetNet Model outputting raw hidden-states without any specific head on top. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

Original ProphetNet code can be found at <https://github.com/microsoft/ProphetNet> . Checkpoints were converted from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the file convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py.

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and behavior.

Parameters

config (ProphetNetConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs: Optional[Tuple] = None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The ProphetNetModel forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

Indices can be obtained using ProphetNetTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) –

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

• 1 for tokens that are not masked,

• 0 for tokens that are masked.

• decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) –

Indices of decoder input sequence tokens in the vocabulary.

Indices can be obtained using PreTrainedTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

ProphetNet uses the eos_token_id as the starting token for decoder_input_ids generation. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).

• decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length), optional) –

Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.

If you want to change padding behavior, you should read modeling_bart._prepare_decoder_inputs() and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

• encoder_outputs (tuple(tuple(torch.FloatTensor), optional) – Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.

• past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.

If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

• use_cache (bool, optional) – If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

• output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

• output_hidden_states (bool, optional) – Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

• return_dict (bool, optional) – Whether or not to return a ModelOutput instead of a plain tuple.

Returns

A ProphetNetSeq2SeqModelOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (ProphenetConfig) and inputs.

• last_hidden_state (torch.FloatTensor of shape (batch_size, decoder_sequence_length, hidden_size)) – Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.

If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

• last_hidden_state_ngram (torch.FloatTensor of shape (batch_size,ngram * decoder_sequence_length, config.vocab_size)) – Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.

• past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

• decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, decoder_sequence_length, hidden_size).

Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.

• decoder_ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, ngram * decoder_sequence_length, hidden_size).

Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.

• decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• decoder_ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the

• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).

Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the

• encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, encoder_sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

• encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, encoder_sequence_length, hidden_size).

Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

• encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length).

Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from transformers import ProphetNetTokenizer, ProphetNetModel

>>> tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
>>> model = ProphetNetModel.from_pretrained('microsoft/prophetnet-large-uncased')

>>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

>>> last_hidden_states = outputs.last_hidden_state  # main stream hidden states
>>> last_hidden_states_ngram = outputs.last_hidden_state_ngram  # predict hidden states


Return type

ProphetNetSeq2SeqModelOutput or tuple(torch.FloatTensor)

## ProphetNetEncoder¶

class transformers.ProphetNetEncoder(config: transformers.models.prophetnet.configuration_prophetnet.ProphetNetConfig, word_embeddings: torch.nn.modules.sparse.Embedding = None)[source]

The standalone encoder part of the ProphetNetModel. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

Original ProphetNet code can be found at <https://github.com/microsoft/ProphetNet> . Checkpoints were converted from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the file convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py.

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and behavior.

Parameters

config (ProphetNetConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

word_embeddings (torch.nn.Embeddings of shape (config.vocab_size, config.hidden_size), optional):

The word embedding parameters. This can be used to initialize ProphetNetEncoder with pre-defined word embeddings instead of randomely initialized word embeddings.

forward(input_ids=None, attention_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The ProphetNetEncoder forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

Indices can be obtained using ProphetNetTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) –

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

• 1 for tokens that are not masked,

• 0 for tokens that are masked.

• output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

• output_hidden_states (bool, optional) – Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

• return_dict (bool, optional) – Whether or not to return a ModelOutput instead of a plain tuple.

Returns

A BaseModelOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (ProphenetConfig) and inputs.

• last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.

• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the model at the output of each layer plus the initial embedding outputs.

• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from transformers import ProphetNetTokenizer, ProphetNetEncoder
>>> import torch

>>> tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
>>> model = ProphetNetEncoder.from_pretrained('patrickvonplaten/prophetnet-large-uncased-standalone')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state


Return type

BaseModelOutput or tuple(torch.FloatTensor)

## ProphetNetDecoder¶

class transformers.ProphetNetDecoder(config: transformers.models.prophetnet.configuration_prophetnet.ProphetNetConfig, word_embeddings: torch.nn.modules.sparse.Embedding = None)[source]

The standalone decoder part of the ProphetNetModel. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

Original ProphetNet code can be found at <https://github.com/microsoft/ProphetNet> . Checkpoints were converted from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the file convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py.

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and behavior.

Parameters

config (ProphetNetConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

word_embeddings (torch.nn.Embeddings of shape (config.vocab_size, config.hidden_size), optional):

The word embedding parameters. This can be used to initialize ProphetNetEncoder with pre-defined word embeddings instead of randomely initialized word embeddings.

forward(input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The ProphetNetDecoder forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

Indices can be obtained using ProphetNetTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) –

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

• 1 for tokens that are not masked,

• 0 for tokens that are masked.

• output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

• output_hidden_states (bool, optional) – Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

• return_dict (bool, optional) – Whether or not to return a ModelOutput instead of a plain tuple.

• encoder_hidden_states (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.

• encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) – Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:

• past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.

If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

• use_cache (bool, optional) –

If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

• 1 for tokens that are not masked,

• 0 for tokens that are masked.

Returns

A ProphetNetDecoderModelOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (ProphenetConfig) and inputs.

• last_hidden_state (torch.FloatTensor of shape (batch_size, decoder_sequence_length, hidden_size)) – Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.

If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

• last_hidden_state_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) – Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.

• past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, decoder_sequence_length, hidden_size).

Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.

• ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, ngram * decoder_sequence_length, hidden_size).

Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.

• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the

• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).

Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the

Example:

>>> from transformers import ProphetNetTokenizer, ProphetNetDecoder
>>> import torch

>>> tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state


Return type

ProphetNetDecoderModelOutput or tuple(torch.FloatTensor)

## ProphetNetForConditionalGeneration¶

class transformers.ProphetNetForConditionalGeneration(config: transformers.models.prophetnet.configuration_prophetnet.ProphetNetConfig)[source]

The ProphetNet Model with a language modeling head. Can be used for sequence generation tasks. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

Original ProphetNet code can be found at <https://github.com/microsoft/ProphetNet> . Checkpoints were converted from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the file convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py.

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and behavior.

Parameters

config (ProphetNetConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs=None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The ProphetNetForConditionalGeneration forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

Indices can be obtained using ProphetNetTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) –

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

• 1 for tokens that are not masked,

• 0 for tokens that are masked.

• decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) –

Indices of decoder input sequence tokens in the vocabulary.

Indices can be obtained using PreTrainedTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

ProphetNet uses the eos_token_id as the starting token for decoder_input_ids generation. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).

• decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length), optional) –

Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.

If you want to change padding behavior, you should read modeling_bart._prepare_decoder_inputs() and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

• encoder_outputs (tuple(tuple(torch.FloatTensor), optional) – Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.

• past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.

If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

• use_cache (bool, optional) – If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

• output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

• output_hidden_states (bool, optional) – Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

• return_dict (bool, optional) – Whether or not to return a ModelOutput instead of a plain tuple.

• labels (torch.LongTensor of shape (batch_size,), optional) – Labels for computing the sequence classification/regression loss. Indices should be in [-100, 0, ..., config.vocab_size - 1]. All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]

Returns

A ProphetNetSeq2SeqLMOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (ProphenetConfig) and inputs.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Language modeling loss.

• logits (torch.FloatTensor of shape (batch_size, decoder_sequence_length, config.vocab_size)) – Prediction scores of the main stream language modeling head (scores for each vocabulary token before SoftMax).

• logits_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) – Prediction scores of the predict stream language modeling head (scores for each vocabulary token before SoftMax).

• past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

• decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, decoder_sequence_length, hidden_size).

Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.

• decoder_ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, ngram * decoder_sequence_length, hidden_size).

Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.

• decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• decoder_ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).

Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the

• encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, encoder_sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

• encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, encoder_sequence_length, hidden_size).

Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

• encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length). Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from transformers import ProphetNetTokenizer, ProphetNetForConditionalGeneration

>>> tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
>>> model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased')

>>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)

>>> logits_next_token = outputs.logits  # logits to predict next token as usual
>>> logits_ngram_next_tokens = outputs.logits_ngram  # logits to predict 2nd, 3rd, ... next tokens


Return type

ProphetNetSeq2SeqLMOutput or tuple(torch.FloatTensor)

## ProphetNetForCausalLM¶

class transformers.ProphetNetForCausalLM(config)[source]

The standalone decoder part of the ProphetNetModel with a lm head on top. The model can be used for causal language modeling. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

Original ProphetNet code can be found at <https://github.com/microsoft/ProphetNet> . Checkpoints were converted from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the file convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py.

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and behavior.

Parameters

config (ProphetNetConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

forward(input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The ProphetNetForCausalLM forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

Indices can be obtained using ProphetNetTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) –

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

• 1 for tokens that are not masked,

• 0 for tokens that are masked.

• output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

• output_hidden_states (bool, optional) – Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

• return_dict (bool, optional) – Whether or not to return a ModelOutput instead of a plain tuple.

• encoder_hidden_states (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.

• encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) – Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:

• past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up decoding.

If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

• use_cache (bool, optional) –

If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

• 1 for tokens that are not masked,

• 0 for tokens that are masked.

• labels (torch.LongTensor of shape (batch_size, sequence_length), optional) – Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels n [0, ..., config.vocab_size]

Returns

A ProphetNetDecoderLMOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (ProphenetConfig) and inputs.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Language modeling loss.

• logits (torch.FloatTensor of shape (batch_size, decoder_sequence_length, config.vocab_size)) – Prediction scores of the main stream language modeling head (scores for each vocabulary token before SoftMax).

• logits_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) – Prediction scores of the predict stream language modeling head (scores for each vocabulary token before SoftMax).

• past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, decoder_sequence_length, hidden_size).

Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.

• ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, ngram * decoder_sequence_length, hidden_size).

Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.

• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

• ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).

Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the

• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).

Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the

Example:

>>> from transformers import ProphetNetTokenizer, ProphetNetForCausalLM
>>> import torch

>>> tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
>>> model = ProphetNetForCausalLM.from_pretrained('microsoft/prophetnet-large-uncased')
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> logits = outputs.logits

>>> # Model can also be used with EncoderDecoder framework
>>> from transformers import BertTokenizer, EncoderDecoderModel, ProphetNetTokenizer
>>> import torch

>>> tokenizer_enc = BertTokenizer.from_pretrained('bert-large-uncased')
>>> tokenizer_dec = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-large-uncased", "microsoft/prophetnet-large-uncased")

>>> ARTICLE = (
... "the us state department said wednesday it had received no "
... "formal word from bolivia that it was expelling the us ambassador there "
... "but said the charges made against him are  baseless ."
... )
>>> input_ids = tokenizer_enc(ARTICLE, return_tensors="pt").input_ids
>>> labels = tokenizer_dec("us rejects charges against its ambassador in bolivia", return_tensors="pt").input_ids
>>> outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-1], labels=labels[:, 1:])

>>> loss = outputs.loss


Return type

ProphetNetDecoderLMOutput or tuple(torch.FloatTensor)