
 
OpenAI GPT2
	
Overview
	
OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec
Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever from OpenAI. It’s a causal (unidirectional)
transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.
The abstract from the paper is the following:
GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million
web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
10X the amount of data.
Tips:
GPT-2 is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than
the left.
GPT-2 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 GPT-2 to generate syntactically coherent text as it can be
observed in the run_generation.py example script.
The model can take the past_key_values (for PyTorch) or past (for TF) as input, which is the previously computed
key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing
pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the
GPT2Model.forward() method, or for TF the past argument of the
TFGPT2Model.call() method for more information on its usage.
Enabling the scale_attn_by_inverse_layer_idx and reorder_and_upcast_attn flags will apply the training stability
improvements from Mistral (for PyTorch only).
Write With Transformer is a webapp created and hosted by
Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
This model was contributed by thomwolf. The original code can be found here.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT2. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Text Generation
A blog on how to Finetune a non-English GPT-2 Model with Hugging Face.
A blog on How to generate text: using different decoding methods for language generation with Transformers with GPT-2.
A blog on Training CodeParrot 🦜 from Scratch, a large GPT-2 model.
A blog on Faster Text Generation with TensorFlow and XLA with GPT-2.
A blog on How to train a Language Model with Megatron-LM with a GPT-2 model.
A notebook on how to finetune GPT2 to generate lyrics in the style of your favorite artist. 🌎
A notebook on how to finetune GPT2 to generate tweets in the style of your favorite Twitter user. 🌎
Causal language modeling chapter of the 🤗 Hugging Face Course.
GPT2LMHeadModel is supported by this causal language modeling example script, text generation example script, and notebook.
TFGPT2LMHeadModel is supported by this causal language modeling example script and notebook.
FlaxGPT2LMHeadModel is supported by this causal language modeling example script and notebook.
Text classification task guide
Token classification task guide
Causal language modeling task guide
GPT2Config
	
class transformers.GPT2Config
<
source
>
(
vocab_size = 50257
n_positions = 1024
n_embd = 768
n_layer = 12
n_head = 12
n_inner = None
activation_function = 'gelu_new'
resid_pdrop = 0.1
embd_pdrop = 0.1
attn_pdrop = 0.1
layer_norm_epsilon = 1e-05
initializer_range = 0.02
summary_type = 'cls_index'
summary_use_proj = True
summary_activation = None
summary_proj_to_labels = True
summary_first_dropout = 0.1
scale_attn_weights = True
use_cache = True
bos_token_id = 50256
eos_token_id = 50256
scale_attn_by_inverse_layer_idx = False
reorder_and_upcast_attn = False
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 50257) —
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling GPT2Model or TFGPT2Model.
n_positions (int, optional, defaults to 1024) —
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 768) —
Dimensionality of the embeddings and hidden states.
n_layer (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
n_head (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (int, optional, defaults to None) —
Dimensionality of the inner feed-forward layers. None will set it to 4 times n_embd
activation_function (str, optional, defaults to "gelu") —
Activation function, to be selected in the list ["relu", "silu", "gelu", "tanh", "gelu_new"].
resid_pdrop (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (float, optional, defaults to 0.1) —
The dropout ratio for the embeddings.
attn_pdrop (float, optional, defaults to 0.1) —
The dropout ratio for the attention.
layer_norm_epsilon (float, optional, defaults to 1e-5) —
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.
summary_type (string, optional, defaults to "cls_index") —
Argument used when doing sequence summary, used in the models GPT2DoubleHeadsModel and
TFGPT2DoubleHeadsModel.
Has to be one of the following options:
"last": Take the last token hidden state (like XLNet).
"first": Take the first token hidden state (like BERT).
"mean": Take the mean of all tokens hidden states.
"cls_index": Supply a Tensor of classification token position (like GPT/GPT-2).
"attn": Not implemented now, use multi-head attention.
summary_use_proj (bool, optional, defaults to True) —
Argument used when doing sequence summary, used in the models GPT2DoubleHeadsModel and
TFGPT2DoubleHeadsModel.
Whether or not to add a projection after the vector extraction.
summary_activation (str, optional) —
Argument used when doing sequence summary. Used in for the multiple choice head in
GPT2DoubleHeadsModel.
Pass "tanh" for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (bool, optional, defaults to True) —
Argument used when doing sequence summary, used in the models GPT2DoubleHeadsModel and
TFGPT2DoubleHeadsModel.
Whether the projection outputs should have config.num_labels or config.hidden_size classes.
summary_first_dropout (float, optional, defaults to 0.1) —
Argument used when doing sequence summary, used in the models GPT2DoubleHeadsModel and
TFGPT2DoubleHeadsModel.
The dropout ratio to be used after the projection and activation.
scale_attn_weights (bool, optional, defaults to True) —
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (bool, optional, defaults to False) —
Whether to additionally scale attention weights by 1 / layer_idx + 1.
reorder_and_upcast_attn (bool, optional, defaults to False) —
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
This is the configuration class to store the configuration of a GPT2Model or a TFGPT2Model. It is used to
instantiate a GPT-2 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 GPT-2
gpt2 architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import GPT2Config, GPT2Model
 # Initializing a GPT2 configuration
 configuration = GPT2Config()
 # Initializing a model (with random weights) from the configuration
 model = GPT2Model(configuration)
 # Accessing the model configuration
 configuration = model.config
GPT2Tokenizer
	
class transformers.GPT2Tokenizer
<
source
>
(
vocab_file
merges_file
errors = 'replace'
unk_token = '<|endoftext|>'
bos_token = '<|endoftext|>'
eos_token = '<|endoftext|>'
pad_token = None
add_prefix_space = False
add_bos_token = False
**kwargs
)
Parameters 
vocab_file (str) —
Path to the vocabulary file.
merges_file (str) —
Path to the merges file.
errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.
unk_token (str, optional, defaults to <|endoftext|>) —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (str, optional, defaults to <|endoftext|>) —
The beginning of sequence token.
eos_token (str, optional, defaults to <|endoftext|>) —
The end of sequence token.
add_prefix_space (bool, optional, defaults to False) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (GPT2 tokenizer detect beginning of words by the preceding space).
Construct a GPT-2 tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
	Copied
 from transformers import GPT2Tokenizer
 tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
 tokenizer("Hello world")["input_ids"]
[15496, 995]
 tokenizer(" Hello world")["input_ids"]
[18435, 995]
You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one).
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
)
GPT2TokenizerFast
	
class transformers.GPT2TokenizerFast
<
source
>
(
vocab_file = None
merges_file = None
tokenizer_file = None
unk_token = '<|endoftext|>'
bos_token = '<|endoftext|>'
eos_token = '<|endoftext|>'
add_prefix_space = False
**kwargs
)
Parameters 
vocab_file (str) —
Path to the vocabulary file.
merges_file (str) —
Path to the merges file.
errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.
unk_token (str, optional, defaults to <|endoftext|>) —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (str, optional, defaults to <|endoftext|>) —
The beginning of sequence token.
eos_token (str, optional, defaults to <|endoftext|>) —
The end of sequence token.
add_prefix_space (bool, optional, defaults to False) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (GPT2 tokenizer detect beginning of words by the preceding space).
trim_offsets (bool, optional, defaults to True) —
Whether or not the post-processing step should trim offsets to avoid including whitespaces.
Construct a “fast” GPT-2 tokenizer (backed by HuggingFace’s tokenizers library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
	Copied
 from transformers import GPT2TokenizerFast
 tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
 tokenizer("Hello world")["input_ids"]
[15496, 995]
 tokenizer(" Hello world")["input_ids"]
[18435, 995]
You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer, but since
the model was not pretrained this way, it might yield a decrease in performance.
When used with is_split_into_words=True, this tokenizer needs to be instantiated with add_prefix_space=True.
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
GPT2 specific outputs
	
class transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
<
source
>
(
loss: typing.Optional[torch.FloatTensor] = None
mc_loss: typing.Optional[torch.FloatTensor] = None
logits: FloatTensor = None
mc_logits: FloatTensor = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None
attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None
)
Parameters 
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) —
Language modeling loss.
mc_loss (torch.FloatTensor of shape (1,), optional, returned when mc_labels is provided) —
Multiple choice classification loss.
logits (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mc_logits (torch.FloatTensor of shape (batch_size, num_choices)) —
Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
past_key_values (Tuple[Tuple[torch.Tensor]], optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of length config.n_layers, containing tuples of tensors of shape (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(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 + 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 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).
GPT2Attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads.
Base class for outputs of models predicting if two sentences are consecutive or not.
class transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
<
source
>
(
logits: tf.Tensor = None
mc_logits: tf.Tensor = None
past_key_values: List[tf.Tensor] | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
)
Parameters 
logits (tf.Tensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mc_logits (tf.Tensor of shape (batch_size, num_choices)) —
Prediction scores of the multiple choice classification head (scores for each choice 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.
Base class for outputs of models predicting if two sentences are consecutive or not.
GPT2Model
	
class transformers.GPT2Model
<
source
>
(
config
)
Parameters 
config (GPT2Config) — 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 GPT2 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.Tensor]]] = 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
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: 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.BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][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.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, input_ids_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.
If past_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).
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.BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions 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 (GPT2Config) 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.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=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 of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
The GPT2Model 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, GPT2Model
 import torch
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = GPT2Model.from_pretrained("gpt2")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
GPT2LMHeadModel
	
class transformers.GPT2LMHeadModel
<
source
>
(
config
)
Parameters 
config (GPT2Config) — 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 GPT2 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.Tensor]]] = 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
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: 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.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][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.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, input_ids_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.
If past_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).
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.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions 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 (GPT2Config) 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).
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.
cross_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).
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if config.is_decoder = True.
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.
The GPT2LMHeadModel 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, GPT2LMHeadModel
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = GPT2LMHeadModel.from_pretrained("gpt2")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])
 loss = outputs.loss
 logits = outputs.logits
GPT2DoubleHeadsModel
	
class transformers.GPT2DoubleHeadsModel
<
source
>
(
config
)
Parameters 
config (GPT2Config) — 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 GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
input embeddings, the classification head takes as input the input of a specified classification token index in the
input sequence).
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.Tensor]]] = 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
mc_token_ids: typing.Optional[torch.LongTensor] = None
labels: typing.Optional[torch.LongTensor] = None
mc_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
**kwargs
)
→
transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][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.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, input_ids_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.
If past_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).
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.
mc_token_ids (torch.LongTensor of shape (batch_size, num_choices), optional, default to index of the last token of the input) —
Index of the classification token in each input sequence. Selected in the range [0, input_ids.size(-1) - 1].
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 - 1]. All labels set to
-100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size - 1]
mc_labels (torch.LongTensor of shape (batch_size), optional) —
Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices]
where num_choices is the size of the second dimension of the input tensors. (see input_ids above)
Returns
transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor)
A transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput 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 (GPT2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
mc_loss (torch.FloatTensor of shape (1,), optional, returned when mc_labels is provided) — Multiple choice classification loss.
logits (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mc_logits (torch.FloatTensor of shape (batch_size, num_choices)) — Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
past_key_values (Tuple[Tuple[torch.Tensor]], optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of length config.n_layers, containing tuples of tensors of shape (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(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 + 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 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).
GPT2Attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads.
The GPT2DoubleHeadsModel 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, GPT2DoubleHeadsModel
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = GPT2DoubleHeadsModel.from_pretrained("gpt2")
 # Add a [CLS] to the vocabulary (we should train it also!)
 num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"})
 # Update the model embeddings with the new vocabulary size
 embedding_layer = model.resize_token_embeddings(len(tokenizer))
 choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
 encoded_choices = [tokenizer.encode(s) for s in choices]
 cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices]
 input_ids = torch.tensor(encoded_choices).unsqueeze(0)  # Batch size: 1, number of choices: 2
 mc_token_ids = torch.tensor([cls_token_location])  # Batch size: 1
 outputs = model(input_ids, mc_token_ids=mc_token_ids)
 lm_logits = outputs.logits
 mc_logits = outputs.mc_logits
GPT2ForQuestionAnswering
	
class transformers.GPT2ForQuestionAnswering
<
source
>
(
config
)
Parameters 
config (GPT2Config) — 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 GPT-2 Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from 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
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
start_positions: typing.Optional[torch.LongTensor] = None
end_positions: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][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.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, input_ids_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.
If past_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).
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.
start_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPT2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end 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 GPT2ForQuestionAnswering 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.
This example uses a random model as the real ones are all very big. To get proper results, you should use
gpt2 instead of gpt2. If you get out-of-memory when loading that checkpoint, you can try
adding device_map="auto" in the from_pretrained call.
Example:
	Copied
 from transformers import AutoTokenizer, GPT2ForQuestionAnswering
 import torch
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = GPT2ForQuestionAnswering.from_pretrained("gpt2")
 question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
 inputs = tokenizer(question, text, return_tensors="pt")
 with torch.no_grad():
...     outputs = model(**inputs)
 answer_start_index = outputs.start_logits.argmax()
 answer_end_index = outputs.end_logits.argmax()
 predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
 # target is "nice puppet"
 target_start_index = torch.tensor([14])
 target_end_index = torch.tensor([15])
 outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
 loss = outputs.loss
GPT2ForSequenceClassification
	
class transformers.GPT2ForSequenceClassification
<
source
>
(
config
)
Parameters 
config (GPT2Config) — 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 GPT2 Model transformer with a sequence classification head on top (linear layer).
GPT2ForSequenceClassification 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
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.Tensor]]] = 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.SequenceClassifierOutputWithPast or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][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.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, input_ids_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.
If past_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).
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.SequenceClassifierOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SequenceClassifierOutputWithPast 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 (GPT2Config) 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).
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 GPT2ForSequenceClassification 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, GPT2ForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialogRPT-updown")
 model = GPT2ForSequenceClassification.from_pretrained("microsoft/DialogRPT-updown")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_class_id = logits.argmax().item()
 # 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 = GPT2ForSequenceClassification.from_pretrained("microsoft/DialogRPT-updown", num_labels=num_labels)
 labels = torch.tensor([1])
 loss = model(**inputs, labels=labels).loss
Example of multi-label classification:
	Copied
 import torch
 from transformers import AutoTokenizer, GPT2ForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialogRPT-updown")
 model = GPT2ForSequenceClassification.from_pretrained("microsoft/DialogRPT-updown", problem_type="multi_label_classification")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
 # 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 = GPT2ForSequenceClassification.from_pretrained(
...     "microsoft/DialogRPT-updown", num_labels=num_labels, problem_type="multi_label_classification"
... )
 labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
 loss = model(**inputs, labels=labels).loss
GPT2ForTokenClassification
	
class transformers.GPT2ForTokenClassification
<
source
>
(
config
)
Parameters 
config (GPT2Config) — 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.
GPT2 Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = 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.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length if past_key_values is None else
past_key_values[0][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.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers) —
Contains precomputed 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
What are attention masks?
token_type_ids (torch.LongTensor of shape (batch_size, input_ids_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.
If past_key_values is used, optionally only the last inputs_embeds have to be input (see
past_key_values).
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 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.TokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.TokenClassifierOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPT2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided)  — Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification 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 GPT2ForTokenClassification 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, GPT2ForTokenClassification
 import torch
 tokenizer = AutoTokenizer.from_pretrained("brad1141/gpt2-finetuned-comp2")
 model = GPT2ForTokenClassification.from_pretrained("brad1141/gpt2-finetuned-comp2")
 inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_token_class_ids = logits.argmax(-1)
 # Note that tokens are classified rather then input words which means that
 # there might be more predicted token classes than words.
 # Multiple token classes might account for the same word
 predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
 predicted_tokens_classes
['Lead', 'Lead', 'Lead', 'Position', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead']
 labels = predicted_token_class_ids
 loss = model(**inputs, labels=labels).loss
 round(loss.item(), 2)
0.25
TFGPT2Model
	
class transformers.TFGPT2Model
<
source
>
(
*args
**kwargs
)
Parameters 
config (GPT2Config) — 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 GPT2 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
encoder_hidden_states: np.ndarray | tf.Tensor | None = None
encoder_attention_mask: 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.TFBaseModelOutputWithPastAndCrossAttentions 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_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 (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_key_values 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
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 (Numpy array or tf.Tensor 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 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.
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).
encoder_hidden_states  (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
past_key_values (Tuple[Tuple[tf.Tensor]] of length config.n_layers) —
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past are used, the user can optionally input only the last decoder_input_ids (those that don’t have
their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past). Set to False during training, True during generation
Returns
transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions 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 (GPT2Config) 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.FloatTensor), 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.
cross_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 of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
The TFGPT2Model 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, TFGPT2Model
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = TFGPT2Model.from_pretrained("gpt2")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
 outputs = model(inputs)
 last_hidden_states = outputs.last_hidden_state
TFGPT2LMHeadModel
	
class transformers.TFGPT2LMHeadModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (GPT2Config) — 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 GPT2 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
encoder_hidden_states: np.ndarray | tf.Tensor | None = None
encoder_attention_mask: 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.TFCausalLMOutputWithCrossAttentions 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_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 (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_key_values 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
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 (Numpy array or tf.Tensor 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 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.
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).
encoder_hidden_states  (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
past_key_values (Tuple[Tuple[tf.Tensor]] of length config.n_layers) —
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past are used, the user can optionally input only the last decoder_input_ids (those that don’t have
their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past). Set to False during training, True during generation
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.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions 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 (GPT2Config) 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).
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.
cross_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 of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
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.
The TFGPT2LMHeadModel 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, TFGPT2LMHeadModel
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = TFGPT2LMHeadModel.from_pretrained("gpt2")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
 outputs = model(inputs)
 logits = outputs.logits
TFGPT2DoubleHeadsModel
	
class transformers.TFGPT2DoubleHeadsModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (GPT2Config) — 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 GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
input embeddings, the classification head takes as input the input of a specified classification token index in the
input sequence).
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
mc_token_ids: 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.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput 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_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 (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_key_values 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
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 (Numpy array or tf.Tensor 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 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.
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).
mc_token_ids (tf.Tensor or Numpy array of shape (batch_size, num_choices), optional, default to index of the last token of the input) —
Index of the classification token in each input sequence. Selected in the range [0, input_ids.size(-1) - 1].
Returns
transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput or tuple(tf.Tensor)
A transformers.models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput 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 (GPT2Config) and inputs.
logits (tf.Tensor of shape (batch_size, num_choices, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mc_logits (tf.Tensor of shape (batch_size, num_choices)) — Prediction scores of the multiple choice classification head (scores for each choice 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 TFGPT2DoubleHeadsModel 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.
Examples:
	Copied
 import tensorflow as tf
 from transformers import AutoTokenizer, TFGPT2DoubleHeadsModel
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = TFGPT2DoubleHeadsModel.from_pretrained("gpt2")
 # Add a [CLS] to the vocabulary (we should train it also!)
 num_added_tokens = tokenizer.add_special_tokens({"cls_token": "[CLS]"})
 embedding_layer = model.resize_token_embeddings(
...     len(tokenizer)
... )  # Update the model embeddings with the new vocabulary size
 choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
 encoded_choices = [tokenizer.encode(s) for s in choices]
 cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices]
 input_ids = tf.constant(encoded_choices)[None, :]  # Batch size: 1, number of choices: 2
 mc_token_ids = tf.constant([cls_token_location])  # Batch size: 1
 outputs = model(input_ids, mc_token_ids=mc_token_ids)
 lm_prediction_scores, mc_prediction_scores = outputs[:2]
TFGPT2ForSequenceClassification
	
class transformers.TFGPT2ForSequenceClassification
<
source
>
(
*args
**kwargs
)
Parameters 
config (GPT2Config) — 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 GPT2 Model transformer with a sequence classification head on top (linear layer).
TFGPT2ForSequenceClassification 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
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.TFSequenceClassifierOutputWithPast 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_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 (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_key_values 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.
If past_key_values is used, attention_mask needs to contain the masking strategy that was used for
past_key_values. In other words, the attention_mask always has to have the length:
len(past_key_values) + len(input_ids)
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 (Numpy array or tf.Tensor 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 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.
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.TFSequenceClassifierOutputWithPast or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast 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 (GPT2Config) 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).
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 TFGPT2ForSequenceClassification 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, TFGPT2ForSequenceClassification
 import tensorflow as tf
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialogRPT-updown")
 model = TFGPT2ForSequenceClassification.from_pretrained("microsoft/DialogRPT-updown")
 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 = TFGPT2ForSequenceClassification.from_pretrained("microsoft/DialogRPT-updown", num_labels=num_labels)
 labels = tf.constant(1)
 loss = model(**inputs, labels=labels).loss
TFSequenceClassifierOutputWithPast
	
class transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast
<
source
>
(
loss: tf.Tensor | None = None
logits: tf.Tensor = None
past_key_values: List[tf.Tensor] | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
)
Parameters 
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).
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.
Base class for outputs of sentence classification models.
TFGPT2Tokenizer
	
class transformers.TFGPT2Tokenizer
<
source
>
(
*args
**kwargs
)
Parameters 
vocab (Dict[str, int]) — Vocabulary dict for Byte Pair Tokenizer
merges (List[str]) — Merges list for Byte Pair Tokenizer
This is an in-graph tokenizer for GPT2. It should be initialized similarly to other tokenizers, using the
from_pretrained() method. It can also be initialized with the from_tokenizer() method, which imports settings
from an existing standard tokenizer object.
In-graph tokenizers, unlike other Hugging Face tokenizers, are actually Keras layers and are designed to be run
when the model is called, rather than during preprocessing. As a result, they have somewhat more limited options
than standard tokenizer classes. They are most useful when you want to create an end-to-end model that goes
straight from tf.string inputs to outputs.
from_config
<
source
>
(
config
)
Parameters 
config (Dict) — Dictionary with keys such as stated in get_config.
Creates TFGPT2Tokenizer from configurations
from_pretrained
<
source
>
(
pretrained_model_name_or_path: typing.Union[str, os.PathLike]
*init_inputs
**kwargs
)
Parameters 
pretrained_model_name_or_path (Union[str, os.PathLike]) — Path to pretrained model
Creates TFGPT2Tokenizer from pretrained GPT2Tokenizer
Examples:
	Copied
from transformers import TFGPT2Tokenizer
tf_tokenizer = TFGPT2Tokenizer.from_pretrained("gpt2")
from_tokenizer
<
source
>
(
tokenizer: GPT2Tokenizer
*args
**kwargs
)
Parameters 
tokenizer (GPT2Tokenizer) —
Creates TFGPT2Tokenizer from GPT2Tokenizer
Examples:
	Copied
from transformers import AutoTokenizer, TFGPT2Tokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tf_tokenizer = TFGPT2Tokenizer.from_tokenizer(tokenizer)
FlaxGPT2Model
	
class transformers.FlaxGPT2Model
<
source
>
(
config: GPT2Config
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
**kwargs
)
Parameters 
config (GPT2Config) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from FlaxPreTrainedModel. 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 Flax Linen
flax.nn.Module subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
position_ids = None
encoder_hidden_states: typing.Optional[jax.Array] = None
encoder_attention_mask: typing.Optional[jax.Array] = None
params: dict = None
past_key_values: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length. Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (numpy.ndarray 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?
position_ids (numpy.ndarray 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].
past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) —
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].
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_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions 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 (GPT2Config) and inputs.
last_hidden_state (jnp.ndarray 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(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) 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(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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.
cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
The FlaxGPT2PreTrainedModel 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, FlaxGPT2Model
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = FlaxGPT2Model.from_pretrained("gpt2")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
FlaxGPT2LMHeadModel
	
class transformers.FlaxGPT2LMHeadModel
<
source
>
(
config: GPT2Config
input_shape: typing.Tuple = (1, 1)
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
**kwargs
)
Parameters 
config (GPT2Config) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
This model inherits from FlaxPreTrainedModel. 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 Flax Linen
flax.nn.Module subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
input_ids
attention_mask = None
position_ids = None
encoder_hidden_states: typing.Optional[jax.Array] = None
encoder_attention_mask: typing.Optional[jax.Array] = None
params: dict = None
past_key_values: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters 
input_ids (numpy.ndarray of shape (batch_size, input_ids_length)) —
input_ids_length = sequence_length. Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (numpy.ndarray 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?
position_ids (numpy.ndarray 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].
past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) —
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].
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_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions 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 (GPT2Config) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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.
cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value
states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
Only relevant if config.is_decoder = True.
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.
The FlaxGPT2PreTrainedModel 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, FlaxGPT2LMHeadModel
 tokenizer = AutoTokenizer.from_pretrained("gpt2")
 model = FlaxGPT2LMHeadModel.from_pretrained("gpt2")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
 outputs = model(**inputs)
 # retrieve logts for next token
 next_token_logits = outputs.logits[:, -1]
←GPT-J
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OpenAI GPT2
Overview
Resources
GPT2Config
GPT2Tokenizer
GPT2TokenizerFast
GPT2 specific outputs
GPT2Model
GPT2LMHeadModel
GPT2DoubleHeadsModel
GPT2ForQuestionAnswering
GPT2ForSequenceClassification
GPT2ForTokenClassification
TFGPT2Model
TFGPT2LMHeadModel
TFGPT2DoubleHeadsModel
TFGPT2ForSequenceClassification
TFSequenceClassifierOutputWithPast
TFGPT2Tokenizer
FlaxGPT2Model
FlaxGPT2LMHeadModel
