Transformer XL¶
Overview¶
The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. It’s a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse previously computed hidden-states to attend to longer context (memory). This model also uses adaptive softmax inputs and outputs (tied).
The abstract from the paper is the following:
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens.
Tips:
Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right. The original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
Transformer-XL is one of the few models that has no sequence length limit.
The original code can be found here.
TransfoXLConfig¶
-
class
transformers.
TransfoXLConfig
(vocab_size=267735, cutoffs=[20000, 40000, 200000], d_model=1024, d_embed=1024, n_head=16, d_head=64, d_inner=4096, div_val=4, pre_lnorm=False, n_layer=18, mem_len=1600, clamp_len=1000, same_length=True, proj_share_all_but_first=True, attn_type=0, sample_softmax=- 1, adaptive=True, dropout=0.1, dropatt=0.0, untie_r=True, init='normal', init_range=0.01, proj_init_std=0.01, init_std=0.02, layer_norm_epsilon=1e-05, eos_token_id=0, **kwargs)[source]¶ This is the configuration class to store the configuration of a
TransfoXLModel
or aTFTransfoXLModel
. It is used to instantiate a Transformer-XL 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 Transformer XL architecture.Configuration objects inherit from
PretrainedConfig
and can be used to control the model outputs. Read the documentation fromPretrainedConfig
for more information.- Parameters
vocab_size (
int
, optional, defaults to 267735) – Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingTransfoXLModel
orTFTransfoXLModel
.cutoffs (
List[int]
, optional, defaults to[20000, 40000, 200000]
) – Cutoffs for the adaptive softmax.d_model (
int
, optional, defaults to 1024) – Dimensionality of the model’s hidden states.d_embed (
int
, optional, defaults to 1024) – Dimensionality of the embeddingsn_head (
int
, optional, defaults to 16) – Number of attention heads for each attention layer in the Transformer encoder.d_head (
int
, optional, defaults to 64) – Dimensionality of the model’s heads.d_inner (
int
, optional, defaults to 4096) – Inner dimension in FFdiv_val (
int
, optional, defaults to 4) – Divident value for adapative input and softmaxpre_lnorm (
boolean
, optional, defaults toFalse
) – Whether or not to apply LayerNorm to the input instead of the output in the blocks.n_layer (
int
, optional, defaults to 18) – Number of hidden layers in the Transformer encoder.mem_len (
int
, optional, defaults to 1600) – Length of the retained previous heads.clamp_len (
int
, optional, defaults to 1000) – Use the same pos embeddings after clamp_len.same_length (
boolean
, optional, defaults toTrue
) – Whether or not to use the same attn length for all tokensproj_share_all_but_first (
boolean
, optional, defaults toTrue
) – True to share all but first projs, False not to share.attn_type (
int
, optional, defaults to 0) – Attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.sample_softmax (
int
, optional, defaults to -1) – Number of samples in the sampled softmax.adaptive (
boolean
, optional, defaults toTrue
) – Whether or not to use adaptive softmax.dropout (
float
, optional, defaults to 0.1) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.dropatt (
float
, optional, defaults to 0) – The dropout ratio for the attention probabilities.untie_r (
boolean
, optional, defaults toTrue
) – Whether ot not to untie relative position biases.init (
str
, optional, defaults to"normal"
) – Parameter initializer to use.init_range (
float
, optional, defaults to 0.01) – Parameters initialized by U(-init_range, init_range).proj_init_std (
float
, optional, defaults to 0.01) – Parameters initialized by N(0, init_std)init_std (
float
, optional, defaults to 0.02) – Parameters initialized by N(0, init_std)layer_norm_epsilon (
float
, optional, defaults to 1e-5) – The epsilon to use in the layer normalization layers
Examples:
>>> from transformers import TransfoXLConfig, TransfoXLModel >>> # Initializing a Transformer XL configuration >>> configuration = TransfoXLConfig() >>> # Initializing a model from the configuration >>> model = TransfoXLModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config
TransfoXLTokenizer¶
-
class
transformers.
TransfoXLTokenizer
(special=None, min_freq=0, max_size=None, lower_case=False, delimiter=None, vocab_file=None, pretrained_vocab_file: str = None, never_split=None, unk_token='<unk>', eos_token='<eos>', additional_special_tokens=['<formula>'], language='en', **kwargs)[source]¶ Construct a Transformer-XL tokenizer adapted from Vocab class in the original code. The Transformer-XL tokenizer is a word-level tokenizer (no sub-word tokenization).
This tokenizer inherits from
PreTrainedTokenizer
which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.- Parameters
special (
List[str]
, optional) – A list of special tokens (to be treated by the original implementation of this tokenizer).min_freq (
int
, optional, defaults to 0) – The minimum number of times a token has to be present in order to be kept in the vocabulary (otherwise it will be mapped tounk_token
).max_size (
int
, optional) – The maximum size of the vocabulary. If left unset, it will default to the size of the vocabulary found after excluding the tokens according to themin_freq
rule.lower_case (
bool
, optional, defaults toFalse
) – Whether or not to lowercase the input when tokenizing.delimiter (
str
, optional) – The delimiter used between tokens.vocab_file (
str
, optional) – File containing the vocabulary (from the original implementation).pretrained_vocab_file (
str
, optional) – File containing the vocabulary as saved with thesave_pretrained()
method.never_split (
List[str]
, optional) – List of tokens that should never be split. If no list is specified, will simply use the existing special tokens.unk_token (
str
, optional, defaults to"<unk>"
) – The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.eos_token (
str
, optional, defaults to"<eos>"
) – The end of sequence token.additional_special_tokens (
List[str]
, optional, defaults to["<formula>"]
) – A list of additional special tokens (for the HuggingFace functionality).language (
str
, optional, defaults to"en"
) – The language of this tokenizer (used for mose preprocessing).
-
save_vocabulary
(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]¶ Save only the vocabulary of the tokenizer (vocabulary + added tokens).
This method won’t save the configuration and special token mappings of the tokenizer. Use
_save_pretrained()
to save the whole state of the tokenizer.- Parameters
save_directory (
str
) – The directory in which to save the vocabulary.filename_prefix (
str
, optional) – An optional prefix to add to the named of the saved files.
- Returns
Paths to the files saved.
- Return type
Tuple(str)
TransfoXL specific outputs¶
-
class
transformers.models.transfo_xl.modeling_transfo_xl.
TransfoXLModelOutput
(last_hidden_state: torch.FloatTensor, mems: List[torch.FloatTensor] = None, hidden_states: Optional[Tuple[torch.FloatTensor]] = None, attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]¶ Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).
- Parameters
last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
) – Sequence of hidden-states at the output of the last layer of the model.mems (
List[torch.FloatTensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) –Tuple of
torch.FloatTensor
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, 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=True
is passed or whenconfig.output_attentions=True
) –Tuple of
torch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
class
transformers.models.transfo_xl.modeling_transfo_xl.
TransfoXLLMHeadModelOutput
(losses: Optional[torch.FloatTensor] = None, prediction_scores: torch.FloatTensor = None, mems: List[torch.FloatTensor] = None, hidden_states: Optional[Tuple[torch.FloatTensor]] = None, attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]¶ Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).
- Parameters
losses (
torch.FloatTensor
of shape (batch_size, sequence_length-1), optional, returned whenlabels
is provided) – Language modeling losses (not reduced).prediction_scores (
torch.FloatTensor
of shape(batch_size, sequence_length, config.vocab_size)
) – Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax).mems (
List[torch.FloatTensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) –Tuple of
torch.FloatTensor
(one for the output of the embeddings + one for the output of each layer) of shape(batch_size, 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=True
is passed or whenconfig.output_attentions=True
) –Tuple of
torch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
class
transformers.models.transfo_xl.modeling_tf_transfo_xl.
TFTransfoXLModelOutput
(last_hidden_state: tensorflow.python.framework.ops.Tensor = None, mems: List[tensorflow.python.framework.ops.Tensor] = None, hidden_states: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None)[source]¶ Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).
- Parameters
last_hidden_state (
tf.Tensor
of shape(batch_size, sequence_length, hidden_size)
) – Sequence of hidden-states at the output of the last layer of the model.mems (
List[tf.Tensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) –Tuple of
tf.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=True
is passed or whenconfig.output_attentions=True
) –Tuple of
tf.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.
-
class
transformers.models.transfo_xl.modeling_tf_transfo_xl.
TFTransfoXLLMHeadModelOutput
(prediction_scores: tensorflow.python.framework.ops.Tensor = None, mems: List[tensorflow.python.framework.ops.Tensor] = None, hidden_states: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None)[source]¶ Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).
- Parameters
losses (
tf.Tensor
of shape (batch_size, sequence_length-1), optional, returned whenlabels
is provided) – Language modeling losses (not reduced).prediction_scores (
tf.Tensor
of shape(batch_size, sequence_length, config.vocab_size)
) – Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax).mems (
List[tf.Tensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) –Tuple of
tf.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=True
is passed or whenconfig.output_attentions=True
) –Tuple of
tf.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.
TransfoXLModel¶
-
class
transformers.
TransfoXLModel
(config)[source]¶ The bare Bert 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.
- Parameters
config (
TransfoXLConfig
) – 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, mems=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]¶ The
TransfoXLModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) –Indices of input sequence tokens in the vocabulary.
Indices can be obtained using
TransfoXLTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.mems (
List[torch.FloatTensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (seemems
output below). Can be used to speed up sequential decoding. The token ids which have their mems given to this model should not be passed asinput_ids
as they have already been computed.head_mask (
torch.FloatTensor
of 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.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) – Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices 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. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) – Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) – Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
TransfoXLModelOutput
(ifreturn_dict=True
is passed or whenconfig.return_dict=True
) or a tuple oftorch.FloatTensor
comprising various elements depending on the configuration (TransfoXLConfig
) and inputs.last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
) – Sequence of hidden-states at the output of the last layer of the model.mems (
List[torch.FloatTensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is 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=True
is 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.
- Return type
TransfoXLModelOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import TransfoXLTokenizer, TransfoXLModel >>> import torch >>> tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') >>> model = TransfoXLModel.from_pretrained('transfo-xl-wt103') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state
TransfoXLLMHeadModel¶
-
class
transformers.
TransfoXLLMHeadModel
(config)[source]¶ The Transformer-XL Model with a language modeling head on top (adaptive softmax with weights tied to the adaptive 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.
- Parameters
config (
TransfoXLConfig
) – 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, mems=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]¶ The
TransfoXLLMHeadModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) –Indices of input sequence tokens in the vocabulary.
Indices can be obtained using
TransfoXLTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.mems (
List[torch.FloatTensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (seemems
output below). Can be used to speed up sequential decoding. The token ids which have their mems given to this model should not be passed asinput_ids
as they have already been computed.head_mask (
torch.FloatTensor
of 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.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) – Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices 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. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) – Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) – Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of 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_ids
Indices are selected in[-100, 0, ..., config.vocab_size]
All labels set to-100
are ignored (masked), the loss is only computed for labels in[0, ..., config.vocab_size]
- Returns
A
TransfoXLLMHeadModelOutput
(ifreturn_dict=True
is passed or whenconfig.return_dict=True
) or a tuple oftorch.FloatTensor
comprising various elements depending on the configuration (TransfoXLConfig
) and inputs.losses (
torch.FloatTensor
of shape (batch_size, sequence_length-1), optional, returned whenlabels
is provided) Language modeling losses (not reduced).prediction_scores (
torch.FloatTensor
of shape(batch_size, sequence_length, config.vocab_size)
) – Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax).mems (
List[torch.FloatTensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is 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=True
is 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.
- Return type
TransfoXLLMHeadModelOutput
ortuple(torch.FloatTensor)
Example:
>>> import torch >>> from transformers import TransfoXLTokenizer, TransfoXLLMHeadModel >>> tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') >>> model = TransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs, labels=inputs["input_ids"]) >>> loss = outputs.loss >>> logits = outputs.logits
TransfoXLForSequenceClassification¶
-
class
transformers.
TransfoXLForSequenceClassification
(config)[source]¶ The Transformer-XL Model transformer with a sequence classification head on top (linear layer).
TransfoXLForSequenceClassification
uses the last token in order to do the classification, as other causal models (e.g. GPT-1) do.Since it does classification on the last token, it requires to know the position of the last token. If a
pad_token_id
is defined in the configuration, it finds the last token that is not a padding token in each row. If nopad_token_id
is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens wheninputs_embeds
are passed instead ofinput_ids
, it does the same (take the last value in each row of the batch).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.
- Parameters
config (
TransfoXLConfig
) – 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 thefrom_pretrained()
method to load the model weights.
-
forward
(input_ids=None, mems=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]¶ The
TransfoXLForSequenceClassification
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) –Indices of input sequence tokens in the vocabulary.
Indices can be obtained using
TransfoXLTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.mems (
List[torch.FloatTensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (seemems
output below). Can be used to speed up sequential decoding. The token ids which have their mems given to this model should not be passed asinput_ids
as they have already been computed.head_mask (
torch.FloatTensor
of 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.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) – Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices 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. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) – Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) – Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of shape(batch_size,)
, optional) – Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]
. Ifconfig.num_labels == 1
a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1
a classification loss is computed (Cross-Entropy).
- Returns
A
TransfoXLSequenceClassifierOutputWithPast
(ifreturn_dict=True
is passed or whenconfig.return_dict=True
) or a tuple oftorch.FloatTensor
comprising various elements depending on the configuration (TransfoXLConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) – Classification (or regression if config.num_labels==1) loss.logits (
torch.FloatTensor
of shape(batch_size, config.num_labels)
) – Classification (or regression if config.num_labels==1) scores (before SoftMax).mems (
List[torch.FloatTensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is 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=True
is 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.
- Return type
TransfoXLSequenceClassifierOutputWithPast
ortuple(torch.FloatTensor)
Example:
>>> from transformers import TransfoXLTokenizer, TransfoXLForSequenceClassification >>> import torch >>> tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') >>> model = TransfoXLForSequenceClassification.from_pretrained('transfo-xl-wt103') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 >>> outputs = model(**inputs, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits
TFTransfoXLModel¶
-
class
transformers.
TFTransfoXLModel
(*args, **kwargs)[source]¶ The bare Bert 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_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})
- Parameters
config (
TransfoXLConfig
) – 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids=None, mems=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs)[source]¶ The
TFTransfoXLModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
tf.Tensor
orNumpy array
of shape(batch_size, sequence_length)
) –Indices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.mems (
List[tf.Tensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (seemems
output below). Can be used to speed up sequential decoding. The token ids which have their mems given to this model should not be passed asinput_ids
as they have already been computed.head_mask (
tf.Tensor
orNumpy array
of 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.Tensor
orNumpy array
of shape(batch_size, sequence_length, hidden_size)
, optional) – Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices 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. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) – Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) – Whether or not to return aModelOutput
instead of a plain tuple.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
A
TFTransfoXLModelOutput
(ifreturn_dict=True
is passed or whenconfig.return_dict=True
) or a tuple oftf.Tensor
comprising various elements depending on the configuration (TransfoXLConfig
) and inputs.last_hidden_state (
tf.Tensor
of shape(batch_size, sequence_length, hidden_size)
) – Sequence of hidden-states at the output of the last layer of the model.mems (
List[tf.Tensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is 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=True
is 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.
- Return type
TFTransfoXLModelOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import TransfoXLTokenizer, TFTransfoXLModel >>> import tensorflow as tf >>> tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') >>> model = TFTransfoXLModel.from_pretrained('transfo-xl-wt103') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> outputs = model(inputs) >>> last_hidden_states = outputs.last_hidden_states
TFTransfoXLLMHeadModel¶
-
class
transformers.
TFTransfoXLLMHeadModel
(*args, **kwargs)[source]¶ The Transformer-XL Model with a language modeling head on top (adaptive softmax with weights tied to the adaptive 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.
Note
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using
tf.keras.Model.fit()
method which currently requires having all the tensors in the first argument of the model call function:model(inputs)
.If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
a single Tensor with
input_ids
only and nothing else:model(inputs_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})
- Parameters
config (
TransfoXLConfig
) – 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 thefrom_pretrained()
method to load the model weights.
-
call
(input_ids=None, mems=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, **kwargs)[source]¶ The
TFTransfoXLLMHeadModel
forward method, overrides the__call__()
special method.Note
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.- Parameters
input_ids (
tf.Tensor
orNumpy array
of shape(batch_size, sequence_length)
) –Indices of input sequence tokens in the vocabulary.
Indices can be obtained using
BertTokenizer
. Seetransformers.PreTrainedTokenizer.__call__()
andtransformers.PreTrainedTokenizer.encode()
for details.mems (
List[tf.Tensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (seemems
output below). Can be used to speed up sequential decoding. The token ids which have their mems given to this model should not be passed asinput_ids
as they have already been computed.head_mask (
tf.Tensor
orNumpy array
of 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.Tensor
orNumpy array
of shape(batch_size, sequence_length, hidden_size)
, optional) – Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices 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. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) – Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) – Whether or not to return aModelOutput
instead of a plain tuple.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
A
TFTransfoXLLMHeadModelOutput
(ifreturn_dict=True
is passed or whenconfig.return_dict=True
) or a tuple oftf.Tensor
comprising various elements depending on the configuration (TransfoXLConfig
) and inputs.losses (
tf.Tensor
of shape (batch_size, sequence_length-1), optional, returned whenlabels
is provided) Language modeling losses (not reduced).prediction_scores (
tf.Tensor
of shape(batch_size, sequence_length, config.vocab_size)
) – Prediction scores of the language modeling head (scores for each vocabulary token after SoftMax).mems (
List[tf.Tensor]
of lengthconfig.n_layers
) – Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (seemems
input) to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed.hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is 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=True
is 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.
- Return type
TFTransfoXLLMHeadModelOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import TransfoXLTokenizer, TFTransfoXLLMHeadModel >>> import tensorflow as tf >>> tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103') >>> model = TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> outputs = model(inputs) >>> logits = outputs.logits