transformers documentation

XLM-ProphetNet

XLM-ProphetNet

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 โ€œngramโ€ language modeling instead of just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual โ€œwiki100โ€ Wikipedia dump.

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 Authorsโ€™ code can be found here.

XLMProphetNetConfig

class transformers.XLMProphetNetConfig < >

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

This class overrides ProphetNetConfig. Please check the superclass for the appropriate documentation alongside usage examples.

XLMProphetNetTokenizer

class transformers.XLMProphetNetTokenizer < >

( 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: typing.Union[typing.Dict[str, typing.Any], NoneType] = None **kwargs )

Parameters

  • vocab_file (str) — Path to the vocabulary file.
  • bos_token (str, optional, defaults to "<s>") — 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 "</s>") — 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 "</s>") — 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.
  • cls_token (str, optional, defaults to "<s>") — 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.
  • 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.
  • 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.
  • additional_special_tokens (List[str], optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]) — Additional special tokens used by the tokenizer.
  • sp_model_kwargs (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.

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.

Attributes: sp_model (SentencePieceProcessor): The SentencePiece processor that is used for every conversion (string, tokens and IDs).

build_inputs_with_special_tokens < >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) โ†’ 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]
convert_tokens_to_string < >

( tokens )

Converts a sequence of tokens (strings for sub-words) in a single string.

create_token_type_ids_from_sequences < >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) โ†’ List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs.
  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

Returns

List[int]

List of zeros.

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 < >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) โ†’ List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs.
  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.
  • already_has_special_tokens (bool, optional, defaults to False) — Whether or not the token list is already formatted with special tokens for the model.

Returns

List[int]

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

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

XLMProphetNetModel

class transformers.XLMProphetNetModel < >

( config )

This class overrides ProphetNetModel. Please check the superclass for the appropriate documentation alongside usage examples.

Example:

>>> from transformers import XLMProphetNetTokenizer, XLMProphetNetModel

>>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
>>> model = XLMProphetNetModel.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')

>>> 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

XLMProphetNetEncoder

class transformers.XLMProphetNetEncoder < >

( config: ProphetNetConfig word_embeddings: Embedding = None )

This class overrides ProphetNetEncoder. Please check the superclass for the appropriate documentation alongside usage examples.

Example:

>>> from transformers import XLMProphetNetTokenizer, XLMProphetNetEncoder
>>> import torch

>>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
>>> model = XLMProphetNetEncoder.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)

>>> last_hidden_states = outputs.last_hidden_state

XLMProphetNetDecoder

class transformers.XLMProphetNetDecoder < >

( config: ProphetNetConfig word_embeddings: Embedding = None )

This class overrides ProphetNetDecoder. Please check the superclass for the appropriate documentation alongside usage examples.

Example:

>>> from transformers import XLMProphetNetTokenizer, XLMProphetNetDecoder
>>> import torch

>>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
>>> model = XLMProphetNetDecoder.from_pretrained('patrickvonplaten/xprophetnet-large-uncased-standalone', add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state

XLMProphetNetForConditionalGeneration

class transformers.XLMProphetNetForConditionalGeneration < >

( config: ProphetNetConfig )

This class overrides ProphetNetForConditionalGeneration. Please check the superclass for the appropriate documentation alongside usage examples.

Example:

>>> from transformers import XLMProphetNetTokenizer, XLMProphetNetForConditionalGeneration

>>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
>>> model =  XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')

>>> 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

XLMProphetNetForCausalLM

class transformers.XLMProphetNetForCausalLM < >

( config )

This class overrides ProphetNetForCausalLM. Please check the superclass for the appropriate documentation alongside usage examples.

Example:

>>> from transformers import XLMProphetNetTokenizer, XLMProphetNetForCausalLM
>>> import torch

>>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
>>> model = XLMProphetNetForCausalLM.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
>>> 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 EncoderDecoderModel, XLMProphetNetTokenizer, XLMRobertaTokenizer
>>> import torch

>>> tokenizer_enc = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
>>> tokenizer_dec = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("xlm-roberta-large", 'microsoft/xprophetnet-large-wiki100-cased')

>>> 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