XLM
Overview
The XLM model was proposed in Cross-lingual Language Model Pretraining by Guillaume Lample, Alexis Conneau. It’s a transformer pretrained using one of the following objectives:
- a causal language modeling (CLM) objective (next token prediction),
- a masked language modeling (MLM) objective (BERT-like), or
- a Translation Language Modeling (TLM) object (extension of BERT’s MLM to multiple language inputs)
The abstract from the paper is the following:
Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT’16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT’16 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.
Tips:
- XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
- XLM has multilingual checkpoints which leverage a specific
langparameter. Check out the multi-lingual page for more information.
This model was contributed by thomwolf. The original code can be found here.
XLMConfig
class transformers.XLMConfig
< source >( vocab_size = 30145 emb_dim = 2048 n_layers = 12 n_heads = 16 dropout = 0.1 attention_dropout = 0.1 gelu_activation = True sinusoidal_embeddings = False causal = False asm = False n_langs = 1 use_lang_emb = True max_position_embeddings = 512 embed_init_std = 0.02209708691207961 layer_norm_eps = 1e-12 init_std = 0.02 bos_index = 0 eos_index = 1 pad_index = 2 unk_index = 3 mask_index = 5 is_encoder = True summary_type = 'first' summary_use_proj = True summary_activation = None summary_proj_to_labels = True summary_first_dropout = 0.1 start_n_top = 5 end_n_top = 5 mask_token_id = 0 lang_id = 0 pad_token_id = 2 bos_token_id = 0 **kwargs )
Parameters
-
vocab_size (
int, optional, defaults to 30145) — Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by theinputs_idspassed when calling XLMModel or TFXLMModel. -
emb_dim (
int, optional, defaults to 2048) — Dimensionality of the encoder layers and the pooler layer. -
n_layer (
int, optional, defaults to 12) — Number of hidden layers in the Transformer encoder. -
n_head (
int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer encoder. -
dropout (
float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. -
attention_dropout (
float, optional, defaults to 0.1) — The dropout probability for the attention mechanism -
gelu_activation (
bool, optional, defaults toTrue) — Whether or not to use gelu for the activations instead of relu. -
sinusoidal_embeddings (
bool, optional, defaults toFalse) — Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings. -
causal (
bool, optional, defaults toFalse) — Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in order to only attend to the left-side context instead if a bidirectional context. -
asm (
bool, optional, defaults toFalse) — Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction layer. -
n_langs (
int, optional, defaults to 1) — The number of languages the model handles. Set to 1 for monolingual models. -
use_lang_emb (
bool, optional, defaults toTrue) — Whether to use language embeddings. Some models use additional language embeddings, see the multilingual models page for information on how to use them. -
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). -
embed_init_std (
float, optional, defaults to 2048^-0.5) — The standard deviation of the truncated_normal_initializer for initializing the embedding matrices. -
init_std (
int, optional, defaults to 50257) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the embedding matrices. -
layer_norm_eps (
float, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers. -
bos_index (
int, optional, defaults to 0) — The index of the beginning of sentence token in the vocabulary. -
eos_index (
int, optional, defaults to 1) — The index of the end of sentence token in the vocabulary. -
pad_index (
int, optional, defaults to 2) — The index of the padding token in the vocabulary. -
unk_index (
int, optional, defaults to 3) — The index of the unknown token in the vocabulary. -
mask_index (
int, optional, defaults to 5) — The index of the masking token in the vocabulary. -
is_encoder(
bool, optional, defaults toTrue) — Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al. -
summary_type (
string, optional, defaults to “first”) — Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.Has to be one of the following options:
"last": Take the last token hidden state (like XLNet)."first": Take the first token hidden state (like BERT)."mean": Take the mean of all tokens hidden states."cls_index": Supply a Tensor of classification token position (like GPT/GPT-2)."attn": Not implemented now, use multi-head attention.
-
summary_use_proj (
bool, optional, defaults toTrue) — Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.Whether or not to add a projection after the vector extraction.
-
summary_activation (
str, optional) — Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.Pass
"tanh"for a tanh activation to the output, any other value will result in no activation. -
summary_proj_to_labels (
bool, optional, defaults toTrue) — Used in the sequence classification and multiple choice models.Whether the projection outputs should have
config.num_labelsorconfig.hidden_sizeclasses. -
summary_first_dropout (
float, optional, defaults to 0.1) — Used in the sequence classification and multiple choice models.The dropout ratio to be used after the projection and activation.
-
start_n_top (
int, optional, defaults to 5) — Used in the SQuAD evaluation script. -
end_n_top (
int, optional, defaults to 5) — Used in the SQuAD evaluation script. -
mask_token_id (
int, optional, defaults to 0) — Model agnostic parameter to identify masked tokens when generating text in an MLM context. -
lang_id (
int, optional, defaults to 1) — The ID of the language used by the model. This parameter is used when generating text in a given language.
This is the configuration class to store the configuration of a XLMModel or a TFXLMModel. It is used to instantiate a XLM 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 xlm-mlm-en-2048 architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Examples:
>>> from transformers import XLMConfig, XLMModel
>>> # Initializing a XLM configuration
>>> configuration = XLMConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = XLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configXLMTokenizer
class transformers.XLMTokenizer
< source >( vocab_file merges_file unk_token = '<unk>' bos_token = '<s>' sep_token = '</s>' pad_token = '<pad>' cls_token = '</s>' mask_token = '<special1>' additional_special_tokens = ['<special0>', '<special1>', '<special2>', '<special3>', '<special4>', '<special5>', '<special6>', '<special7>', '<special8>', '<special9>'] lang2id = None id2lang = None do_lowercase_and_remove_accent = True **kwargs )
Parameters
-
vocab_file (
str) — Vocabulary file. -
merges_file (
str) — Merges file. -
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. -
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. -
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. -
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"</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. -
mask_token (
str, optional, defaults to"<special1>") — 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["<special0>","<special1>","<special2>","<special3>","<special4>","<special5>","<special6>","<special7>","<special8>","<special9>"]) — List of additional special tokens. -
lang2id (
Dict[str, int], optional) — Dictionary mapping languages string identifiers to their IDs. -
id2lang (
Dict[int, str], optional) — Dictionary mapping language IDs to their string identifiers. -
do_lowercase_and_remove_accent (
bool, optional, defaults toTrue) — Whether to lowercase and remove accents when tokenizing.
Construct an XLM tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following:
- Moses preprocessing and tokenization for most supported languages.
- Language specific tokenization for Chinese (Jieba), Japanese (KyTea) and Thai (PyThaiNLP).
- Optionally lowercases and normalizes all inputs text.
- The arguments
special_tokensand the functionset_special_tokens, can be used to add additional symbols (like ”classify”) to a vocabulary. - The
lang2idattribute maps the languages supported by the model with their IDs if provided (automatically set for pretrained vocabularies). - The
id2langattributes does reverse mapping if provided (automatically set for pretrained vocabularies).
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
build_inputs_with_special_tokens
< source >(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters
-
token_ids_0 (
List[int]) — List of IDs to which the special tokens will be added. -
token_ids_1 (
List[int], optional) — Optional 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. An XLM sequence has the following format:
- single sequence:
<s> X </s> - pair of sequences:
<s> A </s> B </s>
get_special_tokens_mask
< source >(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = 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.
create_token_type_ids_from_sequences
< source >(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters
-
token_ids_0 (
List[int]) — List of IDs. -
token_ids_1 (
List[int], optional) — Optional second list of IDs for sequence pairs.
Returns
List[int]
List of token type IDs according to the given sequence(s).
Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLM sequence
pair mask has the following format:
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |If token_ids_1 is None, this method only returns the first portion of the mask (0s).
XLM specific outputs
class transformers.models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
< source >( loss: typing.Optional[torch.FloatTensor] = None start_top_log_probs: typing.Optional[torch.FloatTensor] = None start_top_index: typing.Optional[torch.LongTensor] = None end_top_log_probs: typing.Optional[torch.FloatTensor] = None end_top_index: typing.Optional[torch.LongTensor] = None cls_logits: typing.Optional[torch.FloatTensor] = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None )
Parameters
-
loss (
torch.FloatTensorof shape(1,), optional, returned if bothstart_positionsandend_positionsare provided) — Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses. -
start_top_log_probs (
torch.FloatTensorof shape(batch_size, config.start_n_top), optional, returned ifstart_positionsorend_positionsis not provided) — Log probabilities for the top config.start_n_top start token possibilities (beam-search). -
start_top_index (
torch.LongTensorof shape(batch_size, config.start_n_top), optional, returned ifstart_positionsorend_positionsis not provided) — Indices for the top config.start_n_top start token possibilities (beam-search). -
end_top_log_probs (
torch.FloatTensorof shape(batch_size, config.start_n_top * config.end_n_top), optional, returned ifstart_positionsorend_positionsis not provided) — Log probabilities for the topconfig.start_n_top * config.end_n_topend token possibilities (beam-search). -
end_top_index (
torch.LongTensorof shape(batch_size, config.start_n_top * config.end_n_top), optional, returned ifstart_positionsorend_positionsis not provided) — Indices for the topconfig.start_n_top * config.end_n_topend token possibilities (beam-search). -
cls_logits (
torch.FloatTensorof shape(batch_size,), optional, returned ifstart_positionsorend_positionsis not provided) — Log probabilities for theis_impossiblelabel of the answers. -
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, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(torch.FloatTensor), optional, returned 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.
Base class for outputs of question answering models using a SquadHead.
XLMModel
class transformers.XLMModel
< source >( config )
Parameters
- config (XLMConfig) — 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 XLM Model transformer 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.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
langs: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
lengths: typing.Optional[torch.Tensor] = None
cache: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = 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.Indices can be obtained using XLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
attention_mask (
torch.FloatTensorof 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.
-
langs (
torch.LongTensorof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
torch.LongTensorof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, torch.FloatTensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
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 (XLMConfig) 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 XLMModel 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 XLMTokenizer, XLMModel
>>> import torch
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = XLMModel.from_pretrained("xlm-mlm-en-2048")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_stateXLMWithLMHeadModel
class transformers.XLMWithLMHeadModel
< source >( config )
Parameters
- config (XLMConfig) — 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 XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
langs: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
lengths: typing.Optional[torch.Tensor] = None
cache: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using XLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
attention_mask (
torch.FloatTensorof 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.
-
langs (
torch.LongTensorof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
torch.LongTensorof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, torch.FloatTensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
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, sequence_length), optional) — Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can setlabels = input_idsIndices are selected in[-100, 0, ..., config.vocab_size]All labels set to-100are ignored (masked), the loss is only computed for labels in[0, ..., config.vocab_size]
Returns
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MaskedLMOutput 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 (XLMConfig) and inputs.
-
loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Masked language modeling (MLM) loss. -
logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
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 XLMWithLMHeadModel 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 XLMTokenizer, XLMWithLMHeadModel
>>> import torch
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
>>> inputs = tokenizer("The capital of France is <special1>.", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # retrieve index of <special1>
>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
>>> tokenizer.decode(predicted_token_id)
XLMForSequenceClassification
class transformers.XLMForSequenceClassification
< source >( config )
Parameters
- config (XLMConfig) — 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.
XLM Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE 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.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
langs: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
lengths: typing.Optional[torch.Tensor] = None
cache: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using XLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
attention_mask (
torch.FloatTensorof 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.
-
langs (
torch.LongTensorof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
torch.LongTensorof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, torch.FloatTensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
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[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SequenceClassifierOutput 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 (XLMConfig) and inputs.
-
loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Classification (or regression if config.num_labels==1) loss. -
logits (
torch.FloatTensorof shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax). -
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 XLMForSequenceClassification 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 of single-label classification:
>>> import torch
>>> from transformers import XLMTokenizer, XLMForSequenceClassification
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = XLMForSequenceClassification.from_pretrained("xlm-mlm-en-2048")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = XLMForSequenceClassification.from_pretrained("xlm-mlm-en-2048", num_labels=num_labels)
>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
Example of multi-label classification:
>>> import torch
>>> from transformers import XLMTokenizer, XLMForSequenceClassification
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = XLMForSequenceClassification.from_pretrained("xlm-mlm-en-2048", problem_type="multi_label_classification")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = XLMForSequenceClassification.from_pretrained(
... "xlm-mlm-en-2048", num_labels=num_labels, problem_type="multi_label_classification"
... )
>>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
... torch.float
... )
>>> loss = model(**inputs, labels=labels).loss
>>> loss.backward()XLMForMultipleChoice
class transformers.XLMForMultipleChoice
< source >( config *inputs **kwargs )
Parameters
- config (XLMConfig) — 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.
XLM Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG 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.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
langs: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
lengths: typing.Optional[torch.Tensor] = None
cache: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, num_choices, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using XLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
attention_mask (
torch.FloatTensorof shape(batch_size, num_choices, 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.
-
langs (
torch.LongTensorof shape(batch_size, num_choices, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
torch.LongTensorof shape(batch_size, num_choices, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof shape(batch_size, num_choices, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
torch.LongTensorof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, torch.FloatTensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, num_choices, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
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 multiple choice classification loss. Indices should be in[0, ..., num_choices-1]wherenum_choicesis the size of the second dimension of the input tensors. (Seeinput_idsabove)
Returns
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MultipleChoiceModelOutput 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 (XLMConfig) and inputs.
-
loss (
torch.FloatTensorof shape (1,), optional, returned whenlabelsis provided) — Classification loss. -
logits (
torch.FloatTensorof shape(batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).Classification scores (before SoftMax).
-
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 XLMForMultipleChoice 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 XLMTokenizer, XLMForMultipleChoice
>>> import torch
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = XLMForMultipleChoice.from_pretrained("xlm-mlm-en-2048")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0) # choice0 is correct (according to Wikipedia ;)), batch size 1
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
>>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels) # batch size is 1
>>> # the linear classifier still needs to be trained
>>> loss = outputs.loss
>>> logits = outputs.logitsXLMForTokenClassification
class transformers.XLMForTokenClassification
< source >( config )
Parameters
- config (XLMConfig) — 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.
XLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) 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.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
langs: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
lengths: typing.Optional[torch.Tensor] = None
cache: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using XLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
attention_mask (
torch.FloatTensorof 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.
-
langs (
torch.LongTensorof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
torch.LongTensorof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, torch.FloatTensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
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, sequence_length), optional) — Labels for computing the token classification loss. Indices should be in[0, ..., config.num_labels - 1].
Returns
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.TokenClassifierOutput 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 (XLMConfig) and inputs.
-
loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Classification loss. -
logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax). -
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 XLMForTokenClassification 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 XLMTokenizer, XLMForTokenClassification
>>> import torch
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = XLMForTokenClassification.from_pretrained("xlm-mlm-en-2048")
>>> inputs = tokenizer(
... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_token_class_ids = logits.argmax(-1)
>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
XLMForQuestionAnsweringSimple
class transformers.XLMForQuestionAnsweringSimple
< source >( config )
Parameters
- config (XLMConfig) — 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.
XLM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
langs: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
lengths: typing.Optional[torch.Tensor] = None
cache: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
start_positions: typing.Optional[torch.Tensor] = None
end_positions: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using XLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
attention_mask (
torch.FloatTensorof 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.
-
langs (
torch.LongTensorof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
torch.LongTensorof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, torch.FloatTensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
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. -
start_positions (
torch.LongTensorof shape(batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. -
end_positions (
torch.LongTensorof shape(batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.QuestionAnsweringModelOutput 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 (XLMConfig) and inputs.
-
loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. -
start_logits (
torch.FloatTensorof shape(batch_size, sequence_length)) — Span-start scores (before SoftMax). -
end_logits (
torch.FloatTensorof shape(batch_size, sequence_length)) — Span-end scores (before SoftMax). -
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 XLMForQuestionAnsweringSimple 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 XLMTokenizer, XLMForQuestionAnsweringSimple
>>> import torch
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = XLMForQuestionAnsweringSimple.from_pretrained("xlm-mlm-en-2048")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
XLMForQuestionAnswering
class transformers.XLMForQuestionAnswering
< source >( config )
Parameters
- config (XLMConfig) — 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.
XLM Model with a beam-search span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
langs: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
lengths: typing.Optional[torch.Tensor] = None
cache: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
start_positions: typing.Optional[torch.Tensor] = None
end_positions: typing.Optional[torch.Tensor] = None
is_impossible: typing.Optional[torch.Tensor] = None
cls_index: typing.Optional[torch.Tensor] = None
p_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using XLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
attention_mask (
torch.FloatTensorof 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.
-
langs (
torch.LongTensorof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
torch.LongTensorof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, torch.FloatTensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
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. -
start_positions (
torch.LongTensorof shape(batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. -
end_positions (
torch.LongTensorof shape(batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. -
is_impossible (
torch.LongTensorof shape(batch_size,), optional) — Labels whether a question has an answer or no answer (SQuAD 2.0) -
cls_index (
torch.LongTensorof shape(batch_size,), optional) — Labels for position (index) of the classification token to use as input for computing plausibility of the answer. -
p_mask (
torch.FloatTensorof shape(batch_size, sequence_length), optional) — Optional mask of tokens which can’t be in answers (e.g. [CLS], [PAD], …). 1.0 means token should be masked. 0.0 mean token is not masked.
Returns
transformers.models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput or tuple(torch.FloatTensor)
A transformers.models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput 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 (XLMConfig) and inputs.
-
loss (
torch.FloatTensorof shape(1,), optional, returned if bothstart_positionsandend_positionsare provided) — Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses. -
start_top_log_probs (
torch.FloatTensorof shape(batch_size, config.start_n_top), optional, returned ifstart_positionsorend_positionsis not provided) — Log probabilities for the top config.start_n_top start token possibilities (beam-search). -
start_top_index (
torch.LongTensorof shape(batch_size, config.start_n_top), optional, returned ifstart_positionsorend_positionsis not provided) — Indices for the top config.start_n_top start token possibilities (beam-search). -
end_top_log_probs (
torch.FloatTensorof shape(batch_size, config.start_n_top * config.end_n_top), optional, returned ifstart_positionsorend_positionsis not provided) — Log probabilities for the topconfig.start_n_top * config.end_n_topend token possibilities (beam-search). -
end_top_index (
torch.LongTensorof shape(batch_size, config.start_n_top * config.end_n_top), optional, returned ifstart_positionsorend_positionsis not provided) — Indices for the topconfig.start_n_top * config.end_n_topend token possibilities (beam-search). -
cls_logits (
torch.FloatTensorof shape(batch_size,), optional, returned ifstart_positionsorend_positionsis not provided) — Log probabilities for theis_impossiblelabel of the answers. -
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, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(torch.FloatTensor), optional, returned 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 XLMForQuestionAnswering 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 XLMTokenizer, XLMForQuestionAnswering
>>> import torch
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = XLMForQuestionAnswering.from_pretrained("xlm-mlm-en-2048")
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(
... 0
... ) # Batch size 1
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])
>>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.lossTFXLMModel
class transformers.TFXLMModel
< source >( *args **kwargs )
Parameters
- config (XLMConfig) — 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 XLM Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with
input_idsonly and nothing else:model(input_ids) - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])ormodel([input_ids, attention_mask, token_type_ids]) - a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call
< source >(
input_ids = None
attention_mask = None
langs = None
token_type_ids = None
position_ids = None
lengths = None
cache = None
head_mask = None
inputs_embeds = None
output_attentions = None
output_hidden_states = None
return_dict = None
training = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
attention_mask (
Numpy arrayortf.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.
-
langs (
tf.TensororNumpy arrayof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
tf.TensororNumpy arrayof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, tf.Tensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
Numpy arrayortf.Tensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
tf.Tensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. -
training (
bool, optional, defaults toFalse) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
Returns
transformers.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (XLMConfig) and inputs.
-
last_hidden_state (
tf.Tensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model. -
hidden_states (
tuple(tf.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(tf.Tensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftf.Tensor(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 TFXLMModel 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 XLMTokenizer, TFXLMModel
>>> import tensorflow as tf
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = TFXLMModel.from_pretrained("xlm-mlm-en-2048")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> last_hidden_states = outputs.last_hidden_stateTFXLMWithLMHeadModel
class transformers.TFXLMWithLMHeadModel
< source >( *args **kwargs )
Parameters
- config (XLMConfig) — 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 XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with
input_idsonly and nothing else:model(input_ids) - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])ormodel([input_ids, attention_mask, token_type_ids]) - a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call
< source >(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
langs: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
lengths: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
cache: typing.Union[typing.Dict[str, tensorflow.python.framework.ops.Tensor], NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
training: bool = False
)
→
transformers.models.xlm.modeling_tf_xlm.TFXLMWithLMHeadModelOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
attention_mask (
Numpy arrayortf.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.
-
langs (
tf.TensororNumpy arrayof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
tf.TensororNumpy arrayof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, tf.Tensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
Numpy arrayortf.Tensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
tf.Tensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. -
training (
bool, optional, defaults toFalse) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
Returns
transformers.models.xlm.modeling_tf_xlm.TFXLMWithLMHeadModelOutput or tuple(tf.Tensor)
A transformers.models.xlm.modeling_tf_xlm.TFXLMWithLMHeadModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (XLMConfig) and inputs.
-
logits (
tf.Tensorof shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
hidden_states (
tuple(tf.Tensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(tf.Tensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftf.Tensor(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 TFXLMWithLMHeadModel 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 XLMTokenizer, TFXLMWithLMHeadModel
>>> import tensorflow as tf
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> logits = outputs.logitsTFXLMForSequenceClassification
class transformers.TFXLMForSequenceClassification
< source >( *args **kwargs )
Parameters
- config (XLMConfig) — 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.
XLM Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with
input_idsonly and nothing else:model(input_ids) - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])ormodel([input_ids, attention_mask, token_type_ids]) - a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call
< source >(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
langs: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
lengths: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
cache: typing.Union[typing.Dict[str, tensorflow.python.framework.ops.Tensor], NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
attention_mask (
Numpy arrayortf.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.
-
langs (
tf.TensororNumpy arrayof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
tf.TensororNumpy arrayof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, tf.Tensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
Numpy arrayortf.Tensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
tf.Tensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. -
training (
bool, optional, defaults toFalse) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). -
labels (
tf.Tensorof shape(batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (XLMConfig) and inputs.
-
loss (
tf.Tensorof shape(batch_size, ), optional, returned whenlabelsis provided) — Classification (or regression if config.num_labels==1) loss. -
logits (
tf.Tensorof shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax). -
hidden_states (
tuple(tf.Tensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(tf.Tensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftf.Tensor(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 TFXLMForSequenceClassification 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 XLMTokenizer, TFXLMForSequenceClassification
>>> import tensorflow as tf
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = TFXLMForSequenceClassification.from_pretrained("xlm-mlm-en-2048")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> logits = model(**inputs).logits
>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = TFXLMForSequenceClassification.from_pretrained("xlm-mlm-en-2048", num_labels=num_labels)
>>> labels = tf.constant(1)
>>> loss = model(**inputs, labels=labels).loss
>>> round(float(loss), 2)
TFXLMForMultipleChoice
class transformers.TFXLMForMultipleChoice
< source >( *args **kwargs )
Parameters
- config (XLMConfig) — 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.
XLM Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with
input_idsonly and nothing else:model(input_ids) - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])ormodel([input_ids, attention_mask, token_type_ids]) - a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call
< source >(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
langs: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
lengths: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
cache: typing.Union[typing.Dict[str, tensorflow.python.framework.ops.Tensor], NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy arrayortf.Tensorof shape(batch_size, num_choices, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
attention_mask (
Numpy arrayortf.Tensorof shape(batch_size, num_choices, 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.
-
langs (
tf.TensororNumpy arrayof shape(batch_size, num_choices, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
Numpy arrayortf.Tensorof shape(batch_size, num_choices, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
Numpy arrayortf.Tensorof shape(batch_size, num_choices, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
tf.TensororNumpy arrayof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, tf.Tensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
Numpy arrayortf.Tensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
tf.Tensorof shape(batch_size, num_choices, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. -
training (
bool, optional, defaults toFalse) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
Returns
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (XLMConfig) and inputs.
-
loss (
tf.Tensorof shape (batch_size, ), optional, returned whenlabelsis provided) — Classification loss. -
logits (
tf.Tensorof shape(batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).Classification scores (before SoftMax).
-
hidden_states (
tuple(tf.Tensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(tf.Tensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftf.Tensor(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 TFXLMForMultipleChoice 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 XLMTokenizer, TFXLMForMultipleChoice
>>> import tensorflow as tf
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = TFXLMForMultipleChoice.from_pretrained("xlm-mlm-en-2048")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
>>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> outputs = model(inputs) # batch size is 1
>>> # the linear classifier still needs to be trained
>>> logits = outputs.logitsTFXLMForTokenClassification
class transformers.TFXLMForTokenClassification
< source >( *args **kwargs )
Parameters
- config (XLMConfig) — 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.
XLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with
input_idsonly and nothing else:model(input_ids) - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])ormodel([input_ids, attention_mask, token_type_ids]) - a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call
< source >(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
langs: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
lengths: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
cache: typing.Union[typing.Dict[str, tensorflow.python.framework.ops.Tensor], NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
attention_mask (
Numpy arrayortf.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.
-
langs (
tf.TensororNumpy arrayof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
tf.TensororNumpy arrayof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, tf.Tensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
Numpy arrayortf.Tensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
tf.Tensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. -
training (
bool, optional, defaults toFalse) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). -
labels (
tf.Tensorof shape(batch_size, sequence_length), optional) — Labels for computing the token classification loss. Indices should be in[0, ..., config.num_labels - 1].
Returns
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFTokenClassifierOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (XLMConfig) and inputs.
-
loss (
tf.Tensorof shape(n,), optional, where n is the number of unmasked labels, returned whenlabelsis provided) — Classification loss. -
logits (
tf.Tensorof shape(batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax). -
hidden_states (
tuple(tf.Tensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(tf.Tensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftf.Tensor(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 TFXLMForTokenClassification 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 XLMTokenizer, TFXLMForTokenClassification
>>> import tensorflow as tf
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = TFXLMForTokenClassification.from_pretrained("xlm-mlm-en-2048")
>>> inputs = tokenizer(
... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
... )
>>> logits = model(**inputs).logits
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
>>> predicted_tokens_classes
TFXLMForQuestionAnsweringSimple
class transformers.TFXLMForQuestionAnsweringSimple
< source >( *args **kwargs )
Parameters
- config (XLMConfig) — 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.
XLM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer
on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with
input_idsonly and nothing else:model(input_ids) - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])ormodel([input_ids, attention_mask, token_type_ids]) - a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call
< source >(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
langs: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
lengths: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
cache: typing.Union[typing.Dict[str, tensorflow.python.framework.ops.Tensor], NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
start_positions: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
end_positions: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using BertTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
attention_mask (
Numpy arrayortf.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.
-
langs (
tf.TensororNumpy arrayof shape(batch_size, sequence_length), optional) — A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is inmodel.config.lang2id(which is a dictionary string to int) and the language id to language name mapping is inmodel.config.id2lang(dictionary int to string).See usage examples detailed in the multilingual documentation.
-
token_type_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
Numpy arrayortf.Tensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
lengths (
tf.TensororNumpy arrayof shape(batch_size,), optional) — Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in[0, ..., input_ids.size(-1)]. -
cache (
Dict[str, tf.Tensor], optional) — Dictionary string totorch.FloatTensorthat contains precomputed hidden states (key and values in the attention blocks) as computed by the model (seecacheoutput below). Can be used to speed up sequential decoding.The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
-
head_mask (
Numpy arrayortf.Tensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
tf.Tensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. -
training (
bool, optional, defaults toFalse) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). -
start_positions (
tf.Tensorof shape(batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. -
end_positions (
tf.Tensorof shape(batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.
Returns
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (XLMConfig) and inputs.
-
loss (
tf.Tensorof shape(batch_size, ), optional, returned whenstart_positionsandend_positionsare provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. -
start_logits (
tf.Tensorof shape(batch_size, sequence_length)) — Span-start scores (before SoftMax). -
end_logits (
tf.Tensorof shape(batch_size, sequence_length)) — Span-end scores (before SoftMax). -
hidden_states (
tuple(tf.Tensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftf.Tensor(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-
attentions (
tuple(tf.Tensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftf.Tensor(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 TFXLMForQuestionAnsweringSimple 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 XLMTokenizer, TFXLMForQuestionAnsweringSimple
>>> import tensorflow as tf
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
>>> model = TFXLMForQuestionAnsweringSimple.from_pretrained("xlm-mlm-en-2048")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(**inputs)
>>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
>>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens)