XLM-ProphetNet
This model is in maintenance mode only, we don’t accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
You can do so by running the following command: pip install -U transformers==4.40.2.
DISCLAIMER: If you see something strange, file a Github Issue and assign @patrickvonplaten
Overview
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.
Resources
XLMProphetNetConfig
class transformers.XLMProphetNetConfig
< source >( 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
- activation_dropout (
float, optional, defaults to 0.1) — The dropout ratio for activations inside the fully connected layer. - activation_function (
strorfunction, optional, defaults to"gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string,"gelu","relu","silu"and"gelu_new"are supported. - vocab_size (
int, optional, defaults to 30522) — Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by theinputs_idspassed when calling XLMProphetNetModel. - 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 theintermediate(often named feed-forward) layer in decoder. - num_decoder_layers (
int, optional, defaults to 12) — Number of decoder layers. - num_decoder_attention_heads (
int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder. - attention_dropout (
float, optional, defaults to 0.1) — The dropout ratio for the attention probabilities. - dropout (
float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. - max_position_embeddings (
int, optional, defaults to 512) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). - init_std (
float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - add_cross_attention (
bool, optional, defaults toTrue) — Whether cross-attention layers should be added to the model. - is_encoder_decoder (
bool, optional, defaults toTrue) — Whether this is an encoder/decoder model. - pad_token_id (
int, optional, defaults to 1) — Padding token id. - bos_token_id (
int, optional, defaults to 0) — Beginning of stream token id. - eos_token_id (
int, optional, defaults to 2) — End of stream token id. - ngram (
int, optional, defaults to 2) — Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first token. - num_buckets (
int, optional, defaults to 32) — The number of buckets to use for each attention layer. This is for relative position calculation. See the [T5 paper](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 toFalse) — Whether be trained predicting only the next first token. - eps (
float, optional, defaults to 0.0) — Controls theepsilonparameter value for label smoothing in the loss calculation. If set to 0, no label smoothing is performed. - use_cache (
bool, optional, defaults toTrue) — 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.
XLMProphetNetTokenizer
class transformers.XLMProphetNetTokenizer
< source >( 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
- vocab_file (
str) — Path to the vocabulary file. - bos_token (
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. - eos_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. - 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. - 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. - pad_token (
str, optional, defaults to"[PAD]") — The token used for padding, for example when batching sequences of different lengths. - cls_token (
str, optional, defaults to"[CLS]") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. - mask_token (
str, optional, defaults to"[MASK]") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. - sp_model_kwargs (
dict, optional) — Will be passed to theSentencePieceProcessor.__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.
-
- sp_model (
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.
build_inputs_with_special_tokens
< source >( token_ids_0: List token_ids_1: Optional = 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 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:
- single sequence:
X [SEP] - pair of sequences:
A [SEP] B [SEP]
Converts a sequence of tokens (strings for sub-words) in a single string.
create_token_type_ids_from_sequences
< source >( 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.
get_special_tokens_mask
< source >( token_ids_0: List token_ids_1: Optional = None already_has_special_tokens: 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 toFalse) — 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.
XLMProphetNetModel
class transformers.XLMProphetNetModel
< source >( config: XLMProphetNetConfig )
Parameters
- config (XLMProphetNetConfig) — 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 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.
forward
< source >( 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.deprecated.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqModelOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof 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.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- decoder_input_ids (
torch.LongTensorof 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_idas the starting token fordecoder_input_idsgeneration. Ifpast_key_valuesis used, optionally only the lastdecoder_input_idshave to be input (seepast_key_values). - decoder_attention_mask (
torch.BoolTensorof shape(batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens indecoder_input_ids. Causal mask will also be used by default. - head_mask (
torch.Tensorof 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.Tensorof 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.Tensorof 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_stateof 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 lengthconfig.n_layerswith 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_valuesare used, the user can optionally input only the lastdecoder_input_ids(those that don’t have their past key value states given to this model) of shape(batch_size, 1)instead of alldecoder_input_idsof shape(batch_size, sequence_length). - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values). - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.models.deprecated.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqModelOutput or tuple(torch.FloatTensor)
A transformers.models.deprecated.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.FloatTensorof 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_valuesis used only the last hidden-state of the sequences of shape(batch_size, 1, hidden_size)is output. -
last_hidden_state_ngram (
torch.FloatTensorof 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 whenuse_cache=Trueis passed or whenconfig.use_cache=True) — List oftorch.FloatTensorof lengthconfig.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_valuesinput) to speed up sequential decoding. -
decoder_hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 statesXLMProphetNetEncoder
class transformers.XLMProphetNetEncoder
< source >( config: XLMProphetNetConfig word_embeddings: Embedding = None )
Parameters
- config (XLMProphetNetConfig) — 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 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.
forward
< source >( 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
- input_ids (
torch.LongTensorof 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.
- attention_mask (
torch.Tensorof 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.
- head_mask (
torch.Tensorof 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. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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 (XLMProphetNetConfig) and inputs.
-
last_hidden_state (
torch.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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_stateXLMProphetNetDecoder
class transformers.XLMProphetNetDecoder
< source >( config: XLMProphetNetConfig word_embeddings: Optional = None )
Parameters
- config (XLMProphetNetConfig) — 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 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.
forward
< source >( 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.deprecated.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderModelOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof 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.
- attention_mask (
torch.Tensorof 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.
- head_mask (
torch.Tensorof 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. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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.FloatTensorof 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.FloatTensorof 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.Tensorof 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 lengthconfig.n_layerswith 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_valuesare used, the user can optionally input only the lastdecoder_input_ids(those that don’t have their past key value states given to this model) of shape(batch_size, 1)instead of alldecoder_input_idsof shape(batch_size, sequence_length). - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values).- 1 for tokens that are not masked,
- 0 for tokens that are masked.
Returns
transformers.models.deprecated.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderModelOutput or tuple(torch.FloatTensor)
A transformers.models.deprecated.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.FloatTensorof 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_valuesis used only the last hidden-state of the sequences of shape(batch_size, 1, hidden_size)is output. -
last_hidden_state_ngram (
torch.FloatTensorof 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 whenuse_cache=Trueis passed or whenconfig.use_cache=True) — List oftorch.FloatTensorof lengthconfig.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_valuesinput) to speed up sequential decoding. -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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_stateXLMProphetNetForConditionalGeneration
class transformers.XLMProphetNetForConditionalGeneration
< source >( config: XLMProphetNetConfig )
Parameters
- config (XLMProphetNetConfig) — 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 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.
forward
< source >( 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.deprecated.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqLMOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof 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.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- decoder_input_ids (
torch.LongTensorof 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_idas the starting token fordecoder_input_idsgeneration. Ifpast_key_valuesis used, optionally only the lastdecoder_input_idshave to be input (seepast_key_values). - decoder_attention_mask (
torch.BoolTensorof shape(batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens indecoder_input_ids. Causal mask will also be used by default. - head_mask (
torch.Tensorof 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.Tensorof 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.Tensorof 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_stateof 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 lengthconfig.n_layerswith 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_valuesare used, the user can optionally input only the lastdecoder_input_ids(those that don’t have their past key value states given to this model) of shape(batch_size, 1)instead of alldecoder_input_idsof shape(batch_size, sequence_length). - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values). - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. - labels (
torch.LongTensorof 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-100are ignored (masked), the loss is only computed for labels in[0, ..., config.vocab_size]
Returns
transformers.models.deprecated.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetSeq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.models.deprecated.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.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Language modeling loss. -
logits (
torch.FloatTensorof 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.FloatTensorof 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 whenuse_cache=Trueis passed or whenconfig.use_cache=True) — List oftorch.FloatTensorof lengthconfig.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_valuesinput) to speed up sequential decoding. -
decoder_hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 tokensXLMProphetNetForCausalLM
class transformers.XLMProphetNetForCausalLM
< source >( config: XLMProphetNetConfig )
Parameters
- config (XLMProphetNetConfig) — 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 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.
forward
< source >( 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.deprecated.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderLMOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof 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.
- attention_mask (
torch.Tensorof 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.
- head_mask (
torch.Tensorof 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. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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.FloatTensorof 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.FloatTensorof 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.Tensorof 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 lengthconfig.n_layerswith 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_valuesare used, the user can optionally input only the lastdecoder_input_ids(those that don’t have their past key value states given to this model) of shape(batch_size, 1)instead of alldecoder_input_idsof shape(batch_size, sequence_length). - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values).- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- labels (
torch.LongTensorof 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](seeinput_idsdocstring) Tokens with indices set to-100are ignored (masked), the loss is only computed for the tokens with labels n[0, ..., config.vocab_size]
Returns
transformers.models.deprecated.xlm_prophetnet.modeling_xlm_prophetnet.XLMProphetNetDecoderLMOutput or tuple(torch.FloatTensor)
A transformers.models.deprecated.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.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Language modeling loss. -
logits (
torch.FloatTensorof 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.FloatTensorof 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 whenuse_cache=Trueis passed or whenconfig.use_cache=True) — List oftorch.FloatTensorof lengthconfig.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_valuesinput) to speed up sequential decoding. -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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