
 
CTRL
	
Overview
	
CTRL model was proposed in CTRL: A Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong and
Richard Socher. It’s a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
The abstract from the paper is the following:
Large-scale language models show promising text generation capabilities, but users cannot easily control particular
aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
via model-based source attribution.
Tips:
CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
or links to generate coherent text. Refer to the original implementation for
more information.
CTRL is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than
the left.
CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
observed in the run_generation.py example script.
The PyTorch models can take the past_key_values as input, which is the previously computed key/value attention pairs.
TensorFlow models accepts past as input. Using the past_key_values value prevents the model from re-computing
pre-computed values in the context of text generation. See the forward
method for more information on the usage of this argument.
This model was contributed by keskarnitishr. The original code can be found
here.
Documentation resources
	
Text classification task guide
Causal language modeling task guide
CTRLConfig
	
class transformers.CTRLConfig
<
source
>
(
vocab_size = 246534
n_positions = 256
n_embd = 1280
dff = 8192
n_layer = 48
n_head = 16
resid_pdrop = 0.1
embd_pdrop = 0.1
layer_norm_epsilon = 1e-06
initializer_range = 0.02
use_cache = True
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 246534) —
Vocabulary size of the CTRL model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling CTRLModel or TFCTRLModel.
n_positions (int, optional, defaults to 256) —
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).
n_embd (int, optional, defaults to 1280) —
Dimensionality of the embeddings and hidden states.
dff (int, optional, defaults to 8192) —
Dimensionality of the inner dimension of the feed forward networks (FFN).
n_layer (int, optional, defaults to 48) —
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.
resid_pdrop (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (int, optional, defaults to 0.1) —
The dropout ratio for the embeddings.
layer_norm_epsilon (float, optional, defaults to 1e-6) —
The epsilon to use in the layer normalization layers
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models).
This is the configuration class to store the configuration of a CTRLModel or a TFCTRLModel. It is used to
instantiate a CTRL 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
ctrl architecture from SalesForce.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Examples:
	Copied
 from transformers import CTRLConfig, CTRLModel
 # Initializing a CTRL configuration
 configuration = CTRLConfig()
 # Initializing a model (with random weights) from the configuration
 model = CTRLModel(configuration)
 # Accessing the model configuration
 configuration = model.config
CTRLTokenizer
	
class transformers.CTRLTokenizer
<
source
>
(
vocab_file
merges_file
unk_token = '<unk>'
**kwargs
)
Parameters 
vocab_file (str) —
Path to the vocabulary file.
merges_file (str) —
Path to the 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.
Construct a CTRL tokenizer. Based on Byte-Pair-Encoding.
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
save_vocabulary
<
source
>
(
save_directory: str
filename_prefix: typing.Optional[str] = None
)
CTRLModel
	
class transformers.CTRLModel
<
source
>
(
config
)
Parameters 
config (CTRLConfig) — 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 CTRL 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.LongTensor] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPast or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
input_ids_length = sequence_length if past_key_values is None else past_key_values[0].shape[-2]
(sequence_length of input past key value states). Indices of input sequence tokens in the vocabulary.
If past_key_values is used, only input IDs that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.FloatTensor]] of length config.n_layers) —
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decoding. The input_ids which have
their past given to this model should not be passed as input ids as they have already been computed.
attention_mask (torch.FloatTensor of 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.
What are attention masks?
token_type_ids (torch.LongTensor of 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.
What are token type IDs?
position_ids (torch.LongTensor of 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].
What are position IDs?
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 passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under 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.BaseModelOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPast 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 (CTRLConfig) 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.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if
config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values
input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The CTRLModel 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:
	Copied
 from transformers import AutoTokenizer, CTRLModel
 import torch
 tokenizer = AutoTokenizer.from_pretrained("ctrl")
 model = CTRLModel.from_pretrained("ctrl")
 # CTRL was trained with control codes as the first token
 inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
 assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 5, 1280]
CTRLLMHeadModel
	
class transformers.CTRLLMHeadModel
<
source
>
(
config
)
Parameters 
config (CTRLConfig) — 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 CTRL 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.LongTensor] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
input_ids_length = sequence_length if past_key_values is None else past_key_values[0].shape[-2]
(sequence_length of input past key value states). Indices of input sequence tokens in the vocabulary.
If past_key_values is used, only input IDs that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.FloatTensor]] of length config.n_layers) —
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decoding. The input_ids which have
their past given to this model should not be passed as input ids as they have already been computed.
attention_mask (torch.FloatTensor of 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.
What are attention masks?
token_type_ids (torch.LongTensor of 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.
What are token type IDs?
position_ids (torch.LongTensor of 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].
What are position IDs?
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 passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput 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 set
labels = 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
transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithPast 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 (CTRLConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head))
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The CTRLLMHeadModel 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:
	Copied
 import torch
 from transformers import AutoTokenizer, CTRLLMHeadModel
 tokenizer = AutoTokenizer.from_pretrained("ctrl")
 model = CTRLLMHeadModel.from_pretrained("ctrl")
 # CTRL was trained with control codes as the first token
 inputs = tokenizer("Wikipedia The llama is", return_tensors="pt")
 assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
 sequence_ids = model.generate(inputs["input_ids"])
 sequences = tokenizer.batch_decode(sequence_ids)
 sequences
['Wikipedia The llama is a member of the family Bovidae. It is native to the Andes of Peru,']
 outputs = model(**inputs, labels=inputs["input_ids"])
 round(outputs.loss.item(), 2)
9.21
 list(outputs.logits.shape)
[1, 5, 246534]
CTRLForSequenceClassification
	
class transformers.CTRLForSequenceClassification
<
source
>
(
config
)
Parameters 
config (CTRLConfig) — 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 CTRL Model transformer with a sequence classification head on top (linear layer).
CTRLForSequenceClassification uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) 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 no pad_token_id is defined, it simply takes the last value in each row of the batch. Since it cannot
guess the padding tokens when inputs_embeds are passed instead of input_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.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
use_cache: typing.Optional[bool] = 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.LongTensor of shape (batch_size, sequence_length)) —
input_ids_length = sequence_length if past_key_values is None else past_key_values[0].shape[-2]
(sequence_length of input past key value states). Indices of input sequence tokens in the vocabulary.
If past_key_values is used, only input IDs that do not have their past calculated should be passed as
input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.FloatTensor]] of length config.n_layers) —
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
past_key_values output below). Can be used to speed up sequential decoding. The input_ids which have
their past given to this model should not be passed as input ids as they have already been computed.
attention_mask (torch.FloatTensor of 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.
What are attention masks?
token_type_ids (torch.LongTensor of 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.
What are token type IDs?
position_ids (torch.LongTensor of 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].
What are position IDs?
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 passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput 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]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a 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 (CTRLConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels 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).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The CTRLForSequenceClassification 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:
	Copied
 import torch
 from transformers import AutoTokenizer, CTRLForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("ctrl")
 model = CTRLForSequenceClassification.from_pretrained("ctrl")
 # CTRL was trained with control codes as the first token
 inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
 assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_class_id = logits.argmax().item()
 model.config.id2label[predicted_class_id]
'LABEL_0'
	Copied
 import torch
 torch.manual_seed(42)
 # 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 = CTRLForSequenceClassification.from_pretrained("ctrl", num_labels=num_labels)
 labels = torch.tensor(1)
 loss = model(**inputs, labels=labels).loss
 round(loss.item(), 2)
0.35
Example of multi-label classification:
	Copied
 import torch
 from transformers import AutoTokenizer, CTRLForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("ctrl")
 model = CTRLForSequenceClassification.from_pretrained("ctrl", problem_type="multi_label_classification")
 # CTRL was trained with control codes as the first token
 inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
 assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_class_id = logits.argmax().item()
 model.config.id2label[predicted_class_id]
'LABEL_0'
	Copied
 # 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 = CTRLForSequenceClassification.from_pretrained("ctrl", num_labels=num_labels)
 num_labels = len(model.config.id2label)
 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()
TFCTRLModel
	
class transformers.TFCTRLModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (CTRLConfig) — 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 CTRL 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_ids only 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]) or model([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: TFModelInputType | None = None
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
use_cache: Optional[bool] = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutputWithPast or tuple(tf.Tensor)
Parameters 
input_ids (Numpy array or tf.Tensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past is None else past[0].shape[-2] (sequence_length of
input past key value states).
Indices of input sequence tokens in the vocabulary.
If past is used, only input IDs that do not have their past calculated should be passed as input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
past (List[tf.Tensor] of length config.n_layers) —
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
past output below). Can be used 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.
attention_mask (tf.Tensor or Numpy array of 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.
What are attention masks?
token_type_ids (tf.Tensor or Numpy array of 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.
What are token type IDs?
position_ids (tf.Tensor or Numpy array of 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].
What are position IDs?
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 (tf.Tensor or Numpy array of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
use_cache (bool, optional) —
If set to True, past key value states are returned and can be used to speed up decoding (see past).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under 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. See hidden_states under 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 to False) —
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.TFBaseModelOutputWithPast or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPast 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 (CTRLConfig) 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.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.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 when output_attentions=True is passed or when config.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.
The TFCTRLModel 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:
	Copied
 from transformers import AutoTokenizer, TFCTRLModel
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("ctrl")
 model = TFCTRLModel.from_pretrained("ctrl")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
 outputs = model(inputs)
 last_hidden_states = outputs.last_hidden_state
TFCTRLLMHeadModel
	
class transformers.TFCTRLLMHeadModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (CTRLConfig) — 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 CTRL 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_ids only 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]) or model([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: TFModelInputType | None = None
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
use_cache: Optional[bool] = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFCausalLMOutputWithPast or tuple(tf.Tensor)
Parameters 
input_ids (Numpy array or tf.Tensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past is None else past[0].shape[-2] (sequence_length of
input past key value states).
Indices of input sequence tokens in the vocabulary.
If past is used, only input IDs that do not have their past calculated should be passed as input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
past (List[tf.Tensor] of length config.n_layers) —
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
past output below). Can be used 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.
attention_mask (tf.Tensor or Numpy array of 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.
What are attention masks?
token_type_ids (tf.Tensor or Numpy array of 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.
What are token type IDs?
position_ids (tf.Tensor or Numpy array of 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].
What are position IDs?
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 (tf.Tensor or Numpy array of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
use_cache (bool, optional) —
If set to True, past key value states are returned and can be used to speed up decoding (see past).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under 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. See hidden_states under 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 to False) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
labels (tf.Tensor of shape (batch_size, sequence_length), optional) —
Labels for computing the cross entropy classification loss. Indices should be in [0, ..., config.vocab_size - 1].
Returns
transformers.modeling_tf_outputs.TFCausalLMOutputWithPast or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFCausalLMOutputWithPast 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 (CTRLConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.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 when output_attentions=True is passed or when config.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.
The TFCTRLLMHeadModel 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:
	Copied
 from transformers import AutoTokenizer, TFCTRLLMHeadModel
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("ctrl")
 model = TFCTRLLMHeadModel.from_pretrained("ctrl")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
 outputs = model(inputs)
 logits = outputs.logits
TFCTRLForSequenceClassification
	
class transformers.TFCTRLForSequenceClassification
<
source
>
(
*args
**kwargs
)
Parameters 
config (CTRLConfig) — 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 CTRL Model transformer with a sequence classification head on top (linear layer).
TFCTRLForSequenceClassification uses the last token in order to do the classification, as other causal models
(e.g. GPT-1, GPT-2) 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
no pad_token_id is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when inputs_embeds are passed instead of input_ids, it does the same (take the last value in
each row of the batch).
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_ids only 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]) or model([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: TFModelInputType | None = None
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
use_cache: Optional[bool] = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters 
input_ids (Numpy array or tf.Tensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past is None else past[0].shape[-2] (sequence_length of
input past key value states).
Indices of input sequence tokens in the vocabulary.
If past is used, only input IDs that do not have their past calculated should be passed as input_ids.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
past (List[tf.Tensor] of length config.n_layers) —
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
past output below). Can be used 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.
attention_mask (tf.Tensor or Numpy array of 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.
What are attention masks?
token_type_ids (tf.Tensor or Numpy array of 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.
What are token type IDs?
position_ids (tf.Tensor or Numpy array of 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].
What are position IDs?
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 (tf.Tensor or Numpy array of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
use_cache (bool, optional) —
If set to True, past key value states are returned and can be used to speed up decoding (see past).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under 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. See hidden_states under 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 to False) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
labels (tf.Tensor of shape (batch_size, sequence_length), optional) —
Labels for computing the cross entropy classification loss. Indices should be in [0, ..., config.vocab_size - 1].
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 (CTRLConfig) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.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 when output_attentions=True is passed or when config.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.
The TFCTRLForSequenceClassification 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:
	Copied
 from transformers import AutoTokenizer, TFCTRLForSequenceClassification
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("ctrl")
 model = TFCTRLForSequenceClassification.from_pretrained("ctrl")
 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])
	Copied
 # 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 = TFCTRLForSequenceClassification.from_pretrained("ctrl", num_labels=num_labels)
 labels = tf.constant(1)
 loss = model(**inputs, labels=labels).loss
←CPMANT
DeBERTa→
CTRL
Overview
Documentation resources
CTRLConfig
CTRLTokenizer
CTRLModel
CTRLLMHeadModel
CTRLForSequenceClassification
TFCTRLModel
TFCTRLLMHeadModel
TFCTRLForSequenceClassification
