NllbMoeModel

— Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
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_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weig


NllbMoeModel

hted average in the cross-attention heads.
encoder_last_hidden_state (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 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, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the optional 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_heads, sequence_length, sequence_length).
Attentions weights of the


NllbMoeModel

 encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
modules.
  The NllbMoeModel forward method, overrides the __call__ special method. 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. The NllbMoeModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Modul


NllbMoeModel

e
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, NllbMoeModel
>>> tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
>>> model = SwitchTransformersModel.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
>>> 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
>>> # preprocess: Prepend decoder_input_ids with start token which is pad token for NllbMoeModel
>>> decoder_input_ids = model._shift_right(decoder_input_ids)
>>> # forward pass
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidde


NllbMoeForConditionalGeneration

class transformers.NllbMoeForConditionalGeneration  < source > ( config: NllbMoeConfig  )   Parameters   config (NllbMoeConfig) —
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.    The NllbMoe Model with a language modeling head. Can be used for summarization.
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.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optio


NllbMoeForConditionalGeneration

nal[torch.Tensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None output_router_logits: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.Seq2SeqMoEOutput or tuple(torch.FloatT


NllbMoeForConditionalGeneration

ensor)  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 AutoTokenizer. See PreTrainedTokenizer.encode() and
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.
What are attention masks?   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 AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
NllbMoe uses the eos_token_id as the starting t


NllbMoeForConditionalGeneration

oken 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.LongTensor 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.   head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   cross_attn_head_mask (torch.Tensor of shape (decode


NllbMoeForConditionalGeneration

r_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, e


NllbMoeForConditionalGeneration

mbed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential 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). inputs_embeds (torch.FloatTensor of shape
(batch_size, sequence_length, hidden_size), optional): Optionally, instead of passing input_ids you
can choose to directly pass an embedded representation. This is useful if you want more control over how to
convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.   decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) —
Optionally, instead o


NllbMoeForConditionalGeneration

f passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). This is useful if you want more control over how to convert
decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.   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.


NllbMoeForConditionalGeneration

output_router_logits (bool, optional) —
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored
(masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].   Returns
transformers.modeling_outputs.Seq2SeqMoEOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqMoEOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (NllbMoeConfig) a


NllbMoeForConditionalGeneration

nd inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, r


NllbMoeForConditionalGeneration

eturned 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, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states 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_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer


NllbMoeForConditionalGeneration

) of shape (batch_size, sequence_length, num_experts).
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
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_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (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 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 o


NllbMoeForConditionalGeneration

utput of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, 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_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the encoder model, useful to compute the auxiliary loss and z_


NllbMoeForConditionalGeneration

loss for Mixture of Experts
models.
  The NllbMoeForConditionalGeneration forward method, overrides the __call__ special method. 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.  Translation example:   Copied >>> from transformers import AutoTokenizer, NllbMoeForConditionalGeneration
>>> model = NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")
>>> text_to_translate = "Life is like a box of chocolates"
>>> model_inputs = tokenizer(text_to_translate, return_tensors="pt")
>>> # translate to French
>>> gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("eng_Latn"))
>>> print(tokenizer.batch_decode(gen_tokens, skip_special_t


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 explicit


Overview

ly 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.
Tips:
ProphetNet is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than
the left. The model architecture is based on the original Transformer, but replaces the “standard” self-attention mechanism in the decoder by a a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.
The Authors’ code can be found here.


ProphetNetConfig

class transformers.ProphetNetConfig  < source > ( activation_dropout: typing.Optional[float] = 0.1 activation_function: typing.Union[str, typing.Callable, NoneType] = 'gelu' vocab_size: typing.Optional[int] = 30522 hidden_size: typing.Optional[int] = 1024 encoder_ffn_dim: typing.Optional[int] = 4096 num_encoder_layers: typing.Optional[int] = 12 num_encoder_attention_heads: typing.Optional[int] = 16 decoder_ffn_dim: typing.Optional[int] = 4096 num_decoder_layers: typing.Optional[int] = 12 num_decoder_attention_heads: typing.Optional[int] = 16 attention_dropout: typing.Optional[float] = 0.1 dropout: typing.Optional[float] = 0.1 max_position_embeddings: typing.Optional[int] = 512 init_std: typing.Optional[float] = 0.02 is_encoder_decoder: typing.Optional[bool] = True add_cross_attention: typing.Optional[bool] = True decoder_start_token_id: typing.Optional[int] = 0 ngr


ProphetNetConfig

am: typing.Optional[int] = 2 num_buckets: typing.Optional[int] = 32 relative_max_distance: typing.Optional[int] = 128 disable_ngram_loss: typing.Optional[bool] = False eps: typing.Optional[float] = 0.0 use_cache: typing.Optional[bool] = True pad_token_id: typing.Optional[int] = 0 bos_token_id: typing.Optional[int] = 1 eos_token_id: typing.Optional[int] = 2 **kwargs  )   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 repres


ProphetNetConfig

ented 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 decode


ProphetNetConfig

r.   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) —


ProphetNetConfig

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](see https://arxiv.org/abs/1910.10683) 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](see https://arxiv.org/abs/1910.10683) for more details.   disable_ngram_loss (bool, optional, defaults to False) —
Whether be tr


ProphetNetConfig

ained 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).    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. Instantiating a
configuration with the defaults will yield a similar configuration to that of the ProphetNet
microsoft/prophetnet-large-uncased architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more informa


ProphetNetTokenizer

class transformers.ProphetNetTokenizer  < source > ( vocab_file: str do_lower_case: typing.Optional[bool] = True do_basic_tokenize: typing.Optional[bool] = True never_split: typing.Optional[typing.Iterable] = None unk_token: typing.Optional[str] = '[UNK]' sep_token: typing.Optional[str] = '[SEP]' x_sep_token: typing.Optional[str] = '[X_SEP]' pad_token: typing.Optional[str] = '[PAD]' mask_token: typing.Optional[str] = '[MASK]' tokenize_chinese_chars: typing.Optional[bool] = True strip_accents: typing.Optional[bool] = None **kwargs  )   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=Tr


ProphetNetTokenizer

ue   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.   mask_token (str, optional, defaults to "[MASK]") —
The token used for masking values. This is the token used when training thi


ProphetNetTokenizer

s 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).    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.  build_inputs_with_special_tokens  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.   token_ids_1 (List[int], optional) —
Optional seco


ProphetNetTokenizer

nd list of IDs for sequence pairs.   Returns
List[int]

List of input IDs with the appropriate special tokens.
  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]  convert_tokens_to_string  < source > ( tokens: str  )    Converts a sequence of tokens (string) in a single string.  create_token_type_ids_from_sequences  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None  ) → List[int]  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[int]

List of token type IDs according to the given sequence(s).
  Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ProphetNet  sequence pa


ProphetNetTokenizer

ir mask has the following format:   Copied 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).  get_special_tokens_mask  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: typing.Optional[bool] = False  ) → List[int]  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
List[int]

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


ProphetNet specific outputs

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


ProphetNet specific outputs

loatTensor 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) t


ProphetNet specific outputs

o 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), o


ProphetNet specific outputs

ptional, 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) —
Tup


ProphetNet specific outputs

le 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, return


ProphetNet specific outputs

ed 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.    Base class for sequence-to-sequence language models outputs.
 class transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput  < source > ( last_hidden_state: FloatTensor last_hidden_state_ngram: typing.Optional[torch.FloatTensor] = None past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_ngram_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_ngram_attentions: t


ProphetNet specific outputs

yping.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   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), optional) —
Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.   past_key_values (List[torch


ProphetNet specific outputs

.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


ProphetNet specific outputs

=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 l


ProphetNet specific outputs

ayer) 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 i


ProphetNet specific outputs

s 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.    Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
 class transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput  < source > ( l


ProphetNet specific outputs

ast_hidden_state: FloatTensor last_hidden_state_ngram: typing.Optional[torch.FloatTensor] = None past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None hidden_states_ngram: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None ngram_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   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 * decode


ProphetNet specific outputs

r_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 a


ProphetNet specific outputs

t 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),


ProphetNet specific outputs

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


ProphetNet specific outputs

ophetNetDecoderLMOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None logits: FloatTensor = None logits_ngram: typing.Optional[torch.FloatTensor] = None past_key_values: typing.Optional[typing.Tuple[torch.FloatTensor]] = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None hidden_states_ngram: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None ngram_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   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_ng


ProphetNet specific outputs

ram (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_seque


ProphetNet specific outputs

nce_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 t


ProphetNet specific outputs

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


ProphetNetModel

class transformers.ProphetNetModel  < source > ( config: ProphetNetConfig  )   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.    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 here. 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


ProphetNetModel

 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.  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Optional[torch.Tensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None inputs_embeds: typing.Optional[torch.Tensor] = None decoder_inputs_embeds: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing


ProphetNetModel

.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput or tuple(torch.FloatTensor)  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 AutoTokenizer. See PreTrainedTokenizer.encode() and
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.
What are attention masks?   decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
In


ProphetNetModel

dices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder 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.   head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask


ProphetNetModel

 to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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_l


ProphetNetModel

ength - 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


ProphetNetModel

 a ModelOutput instead of a plain tuple.   Returns
transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput or tuple(torch.FloatTensor)

A transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) 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), optional) — Sequence of predict stream hidden-states at the output of t


ProphetNetModel

he 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


ProphetNetModel

_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 pa


ProphetNetModel

ssed 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.
encod


ProphetNetModel

er_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.
  The ProphetNetModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be


ProphetNetModel

 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.  Example:   Copied >>> from transformers import AutoTokenizer, ProphetNetModel
>>> tokenizer = AutoTokenizer.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 hidde


ProphetNetEncoder

class transformers.ProphetNetEncoder  < source > ( config: ProphetNetConfig word_embeddings: Embedding = None  )   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.    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 here. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
f


ProphetNetEncoder

ile 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. 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 randomly initialized word embeddings.  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[


ProphetNetEncoder

bool] = None  ) → transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)  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 AutoTokenizer. See PreTrainedTokenizer.encode() and
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.
What are attention masks?   head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mas


ProphetNetEncoder

k values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is 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
transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)

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


ProphetNetEncoder

te (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, if the model has an embedding layer, +
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 optional 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 af


ProphetNetEncoder

ter the attention softmax, used to compute the weighted average in the self-attention
heads.
  The ProphetNetEncoder forward method, overrides the __call__ special method. 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.  Example:   Copied >>> from transformers import AutoTokenizer, ProphetNetEncoder
>>> import torch
>>> tokenizer = AutoTokenizer.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_stat


ProphetNetDecoder

class transformers.ProphetNetDecoder  < source > ( config: ProphetNetConfig word_embeddings: typing.Optional[torch.nn.modules.sparse.Embedding] = None  )   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.    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 here. Checkpoints were converted
from original Fairseq checkpoints. For more information on the checkpoint conversion, please take a look at the
file convert_prophetnet_origina


ProphetNetDecoder

l_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. 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 randomly initialized word embeddings.  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None inputs_embeds: typi


ProphetNetDecoder

ng.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput or tuple(torch.FloatTensor)  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 AutoTokenizer. See PreTrainedTokenizer.encode() and
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.
What are attention masks?


ProphetNetDecoder

 head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is 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 de


ProphetNetDecoder

coder.   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]:   cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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 th


ProphetNetDecoder

e 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
transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput or tuple(torch.FloatTensor)

A transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) 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 hi


ProphetNetDecoder

dden-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 wh


ProphetNetDecoder

en 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


ProphetNetDecoder

 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, decode


ProphetNetDecoder

r_sequence_length).
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the
  The ProphetNetDecoder forward method, overrides the __call__ special method. 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.  Example:   Copied >>> from transformers import AutoTokenizer, ProphetNetDecoder
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
>>> model = ProphetNetDecoder.from_pretrained("microsoft/prophetnet-large-uncased", add_cross_attention=False)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_st


ProphetNetForConditionalGeneration

class transformers.ProphetNetForConditionalGeneration  < source > ( config: ProphetNetConfig  )   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.    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 here. 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 t


ProphetNetForConditionalGeneration

orch.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.  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Optional[torch.Tensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None inputs_embeds: typing.Optional[torch.Tensor] = None decoder_inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Opt


ProphetNetForConditionalGeneration

ional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput or tuple(torch.FloatTensor)  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 AutoTokenizer. See PreTrainedTokenizer.encode() and
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.
What are attention masks?   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 AutoTokenizer. S


ProphetNetForConditionalGeneration

ee PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder 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.   head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values s


ProphetNetForConditionalGeneration

elected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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 us


ProphetNetForConditionalGeneration

ed 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/re


ProphetNetForConditionalGeneration

gression 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
transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.models.prophetnet.modeling_prophetnet.ProphetNetSeq2SeqLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) 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, c


ProphetNetForConditionalGeneration

onfig.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 p


ProphetNetForConditionalGeneration

lus 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_att


ProphetNetForConditionalGeneration

entions=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.
en


ProphetNetForConditionalGeneration

coder_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.
  The ProphetNetForConditionalGeneration forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within


ProphetNetForConditionalGeneration

 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.  Example:   Copied >>> from transformers import AutoTokenizer, ProphetNetForConditionalGeneration
>>> tokenizer = AutoTokenizer.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 t


ProphetNetForCausalLM

class transformers.ProphetNetForCausalLM  < source > ( config: ProphetNetConfig  )   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.    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 here. 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


ProphetNetForCausalLM

_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.  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.prophetnet.modeling_prophe


ProphetNetForCausalLM

tnet.ProphetNetDecoderLMOutput or tuple(torch.FloatTensor)  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 AutoTokenizer. See PreTrainedTokenizer.encode() and
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.
What are attention masks?   head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   output_attentions (bool, optio


ProphetNetForCausalLM

nal) —
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]:   cross_attn_head_


ProphetNetForCausalLM

mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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).


ProphetNetForCausalLM

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
transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput or tuple(torch.FloatTensor)

A transformers.models.prophetnet.modeling_prophetnet.ProphetNetDecoderLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) 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


ProphetNetForCausalLM

.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=T


ProphetNetForCausalLM

rue 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


ProphetNetForCausalLM

 (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 de


ProphetNetForCausalLM

coder, after the attention softmax, used to
compute the weighted average in the
  The ProphetNetForCausalLM forward method, overrides the __call__ special method. 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.  Example:   Copied >>> from transformers import AutoTokenizer, ProphetNetForCausalLM
>>> import torch
>>> tokenizer = AutoTokenizer.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 EncoderDeco


ProphetNetForCausalLM

der framework
>>> from transformers import BertTokenizer, EncoderDecoderModel, AutoTokenizer
>>> import torch
>>> tokenizer_enc = BertTokenizer.from_pretrained("bert-large-uncased")
>>> tokenizer_dec = AutoTokenizer.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 = out


Overview

DiT was proposed in DiT: Self-supervised Pre-training for Document Image Transformer by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
DiT applies the self-supervised objective of BEiT (BERT pre-training of Image Transformers) to 42 million document images, allowing for state-of-the-art results on tasks including:
document image classification: the RVL-CDIP dataset (a collection of
400,000 images belonging to one of 16 classes). document layout analysis: the PubLayNet dataset (a collection of more
than 360,000 document images constructed by automatically parsing PubMed XML files). table detection: the ICDAR 2019 cTDaR dataset (a collection of
600 training images and 240 testing images).
The abstract from the paper is the following:
Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we propose DiT,


Overview

a self-supervised pre-trained Document Image Transformer model using large-scale unlabeled text images for Document AI tasks, which is essential since no supervised counterparts ever exist due to the lack of human labeled document images. We leverage DiT as the backbone network in a variety of vision-based Document AI tasks, including document image classification, document layout analysis, as well as table detection. Experiment results have illustrated that the self-supervised pre-trained DiT model achieves new state-of-the-art results on these downstream tasks, e.g. document image classification (91.11 → 92.69), document layout analysis (91.0 → 94.9) and table detection (94.23 → 96.55).
Summary of the approach. Taken from the [original paper](https://arxiv.org/abs/2203.02378).
One can directly use the weights of DiT with the AutoModel API:
  Copied from transformers import AutoModel
model = AutoModel.from_pretrained("microsoft/dit-base")
This will load the model pre-trained