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The XLM-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.
XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for “ngram” language modeling instead of just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual “wiki100” Wikipedia dump. XLM-ProphetNet’s model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained on the cross-lingual dataset XGLUE.
The abstract from the paper is the following:
In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.
The Authors’ code can be found here.
( activation_dropout: Optional = 0.1 activation_function: Union = 'gelu' vocab_size: Optional = 30522 hidden_size: Optional = 1024 encoder_ffn_dim: Optional = 4096 num_encoder_layers: Optional = 12 num_encoder_attention_heads: Optional = 16 decoder_ffn_dim: Optional = 4096 num_decoder_layers: Optional = 12 num_decoder_attention_heads: Optional = 16 attention_dropout: Optional = 0.1 dropout: Optional = 0.1 max_position_embeddings: Optional = 512 init_std: Optional = 0.02 is_encoder_decoder: Optional = True add_cross_attention: Optional = True decoder_start_token_id: Optional = 0 ngram: Optional = 2 num_buckets: Optional = 32 relative_max_distance: Optional = 128 disable_ngram_loss: Optional = False eps: Optional = 0.0 use_cache: Optional = True pad_token_id: Optional = 0 bos_token_id: Optional = 1 eos_token_id: Optional = 2 **kwargs )
Parameters
float, optional, defaults to 0.1) —
The dropout ratio for activations inside the fully connected layer. 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. int, optional, defaults to 30522) —
Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
the inputs_ids passed when calling XLMProphetNetModel. int, optional, defaults to 1024) —
Dimensionality of the layers and the pooler layer. int, optional, defaults to 4096) —
Dimensionality of the “intermediate” (often named feed-forward) layer in decoder. int, optional, defaults to 12) —
Number of encoder layers. int, optional, defaults to 16) —
Number of attention heads for each attention layer in the Transformer encoder. int, optional, defaults to 4096) —
Dimensionality of the intermediate (often named feed-forward) layer in decoder. int, optional, defaults to 12) —
Number of decoder layers. int, optional, defaults to 16) —
Number of attention heads for each attention layer in the Transformer decoder. float, optional, defaults to 0.1) —
The dropout ratio for the attention probabilities. float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. 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). float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. bool, optional, defaults to True) —
Whether cross-attention layers should be added to the model. bool, optional, defaults to True) —
Whether this is an encoder/decoder model. int, optional, defaults to 1) —
Padding token id. int, optional, defaults to 0) —
Beginning of stream token id. int, optional, defaults to 2) —
End of stream token id. 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. 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. 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. bool, optional, defaults to False) —
Whether be trained predicting only the next first token. 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. 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 XLMProphetNetModel. It is used to instantiate a XLMProphetNet 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 XLMProphetNet microsoft/xprophetnet-large-wiki100-cased architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
( vocab_file bos_token = '[SEP]' eos_token = '[SEP]' sep_token = '[SEP]' unk_token = '[UNK]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' sp_model_kwargs: Optional = None **kwargs )
Parameters
str) —
Path to the vocabulary file. str, optional, defaults to "[SEP]") —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the cls_token.
str, optional, defaults to "[SEP]") —
The end of sequence token.
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the 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. 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. str, optional, defaults to "[PAD]") —
The token used for padding, for example when batching sequences of different lengths. str, optional, defaults to "[CLS]") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens. str, optional, defaults to "[MASK]") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict. dict, optional) —
Will be passed to the SentencePieceProcessor.__init__() method. The Python wrapper for
SentencePiece can be used, among other things,
to set:
enable_sampling: Enable subword regularization.
nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout.
nbest_size = {0,1}: No sampling is performed.nbest_size > 1: samples from the nbest_size results.nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
SentencePieceProcessor) —
The SentencePiece processor that is used for every conversion (string, tokens and IDs). Adapted from RobertaTokenizer and XLNetTokenizer. Based on SentencePiece.
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
( token_ids_0: List token_ids_1: Optional = None ) → List[int]
Parameters
List[int]) —
List of IDs to which the special tokens will be added List[int], optional) —
Optional second 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 XLMProphetNet sequence has the following format:
X [SEP]A [SEP] B [SEP]Converts a sequence of tokens (strings for sub-words) in a single string.
( token_ids_0: List token_ids_1: Optional = None ) → List[int]
Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLMProphetNet does not make use of token type ids, therefore a list of zeros is returned.
( token_ids_0: List token_ids_1: Optional = None already_has_special_tokens: bool = False ) → List[int]
Parameters
List[int]) —
List of IDs. List[int], optional) —
Optional second list of IDs for sequence pairs. 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.
( config: XLMProphetNetConfig )
Parameters
The bare XLMProphetNet 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 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.
( input_ids: Optional = None attention_mask: Optional = None decoder_input_ids: Optional = None decoder_attention_mask: Optional = None head_mask: Optional = None decoder_head_mask: Optional = None cross_attn_head_mask: Optional = None encoder_outputs: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None decoder_inputs_embeds: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqModelOutput or tuple(torch.FloatTensor)
Parameters
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.
torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
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.
XLMProphetNet 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).
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. 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]:
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]:
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]:
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. 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).
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). bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqModelOutput or tuple(torch.FloatTensor)
A transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqModelOutput 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 (XLMProphetNetConfig) 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 the last layer of the decoder of the model.
past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, decoder_sequence_length, hidden_size).
Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.
decoder_ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, ngram * decoder_sequence_length, hidden_size).
Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
decoder_ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).
Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, encoder_sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, encoder_sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
The XLMProphetNetModel 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:
>>> from transformers import AutoTokenizer, XLMProphetNetModel
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetModel.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state # main stream hidden states
>>> last_hidden_states_ngram = outputs.last_hidden_state_ngram # predict hidden states( config: XLMProphetNetConfig word_embeddings: Embedding = None )
Parameters
The standalone encoder part of the XLMProphetNetModel. 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 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 XLMProphetNetEncoder with pre-defined word
embeddings instead of randomly initialized word embeddings.
( input_ids: Optional = None attention_mask: Optional = None head_mask: Optional = None inputs_embeds: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)
Parameters
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.
torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
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]:
bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. 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 (XLMProphetNetConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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 after the attention softmax, used to compute the weighted average in the self-attention heads.
The XLMProphetNetEncoder 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:
>>> from transformers import AutoTokenizer, XLMProphetNetEncoder
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetEncoder.from_pretrained("patrickvonplaten/prophetnet-large-uncased-standalone")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state( config: XLMProphetNetConfig word_embeddings: Optional = None )
Parameters
The standalone decoder part of the XLMProphetNetModel. 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 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 XLMProphetNetEncoder with pre-defined word
embeddings instead of randomly initialized word embeddings.
( input_ids: Optional = None attention_mask: Optional = None encoder_hidden_states: Optional = None encoder_attention_mask: Optional = None head_mask: Optional = None cross_attn_head_mask: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderModelOutput or tuple(torch.FloatTensor)
Parameters
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.
torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
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]:
bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. 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. 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]: 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]:
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).
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).
Returns
transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderModelOutput or tuple(torch.FloatTensor)
A transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderModelOutput 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 (XLMProphetNetConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, decoder_sequence_length, hidden_size)) — Sequence of main stream hidden-states at the output of the last layer of the decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
last_hidden_state_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) — Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model.
past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, decoder_sequence_length, hidden_size).
Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.
ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, ngram * decoder_sequence_length, hidden_size).
Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).
Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the
The XLMProphetNetDecoder 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:
>>> from transformers import AutoTokenizer, XLMProphetNetDecoder
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetDecoder.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone", add_cross_attention=False)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state( config: XLMProphetNetConfig )
Parameters
The XLMProphetNet 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 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.
( input_ids: Optional = None attention_mask: Optional = None decoder_input_ids: Optional = None decoder_attention_mask: Optional = None head_mask: Optional = None decoder_head_mask: Optional = None cross_attn_head_mask: Optional = None encoder_outputs: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None decoder_inputs_embeds: Optional = None labels: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqLMOutput or tuple(torch.FloatTensor)
Parameters
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.
torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
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.
XLMProphetNet 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).
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. 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]:
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]:
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]:
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. 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).
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). bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [-100, 0, ..., config.vocab_size - 1]. All labels set to -100 are ignored (masked), the loss is only computed for
labels in [0, ..., config.vocab_size] Returns
transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqLMOutput 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 (XLMProphetNetConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, decoder_sequence_length, config.vocab_size)) — Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).
logits_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) — Prediction scores of the predict stream language modeling head (scores for each vocabulary token before
SoftMax).
past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, decoder_sequence_length, hidden_size).
Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.
decoder_ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, ngram * decoder_sequence_length, hidden_size).
Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
decoder_ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).
Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, encoder_sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, encoder_sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, encoder_sequence_length). Attentions weights of the encoder, after the attention
softmax, used to compute the weighted average in the self-attention heads.
The XLMProphetNetForConditionalGeneration 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:
>>> from transformers import AutoTokenizer, XLMProphetNetForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetForConditionalGeneration.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> logits_next_token = outputs.logits # logits to predict next token as usual
>>> logits_ngram_next_tokens = outputs.logits_ngram # logits to predict 2nd, 3rd, ... next tokens( config: XLMProphetNetConfig )
Parameters
The standalone decoder part of the XLMProphetNetModel 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_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.
( input_ids: Optional = None attention_mask: Optional = None encoder_hidden_states: Optional = None encoder_attention_mask: Optional = None head_mask: Optional = None cross_attn_head_mask: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None labels: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderLMOutput or tuple(torch.FloatTensor)
Parameters
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.
torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
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]:
bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. 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. 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]: 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]:
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).
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).
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.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderLMOutput or tuple(torch.FloatTensor)
A transformers.models.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderLMOutput 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 (XLMProphetNetConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, decoder_sequence_length, config.vocab_size)) — Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).
logits_ngram (torch.FloatTensor of shape (batch_size, ngram * decoder_sequence_length, config.vocab_size)) — Prediction scores of the predict stream language modeling head (scores for each vocabulary token before
SoftMax).
past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_attn_heads, decoder_sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, decoder_sequence_length, hidden_size).
Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.
ngram_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, ngram * decoder_sequence_length, hidden_size).
Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
ngram_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, decoder_sequence_length, decoder_sequence_length).
Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the weighted average in the
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_attn_heads, encoder_sequence_length, decoder_sequence_length).
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to compute the weighted average in the
The XLMProphetNetForCausalLM 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:
>>> from transformers import AutoTokenizer, XLMProphetNetForCausalLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetForCausalLM.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # Model can also be used with EncoderDecoder framework
>>> from transformers import BertTokenizer, EncoderDecoderModel, AutoTokenizer
>>> import torch
>>> tokenizer_enc = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
>>> tokenizer_dec = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained(
... "google-bert/bert-large-uncased", "patrickvonplaten/xprophetnet-large-uncased-standalone"
... )
>>> ARTICLE = (
... "the us state department said wednesday it had received no "
... "formal word from bolivia that it was expelling the us ambassador there "
... "but said the charges made against him are `` baseless ."
... )
>>> input_ids = tokenizer_enc(ARTICLE, return_tensors="pt").input_ids
>>> labels = tokenizer_dec(
... "us rejects charges against its ambassador in bolivia", return_tensors="pt"
... ).input_ids
>>> outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-1], labels=labels[:, 1:])
>>> loss = outputs.loss