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. 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 PyTorch models can take the past as input, which is the previously computed key/value attention pairs. Using this past value prevents the model from re-computing pre-computed values in the context of text generation. See reusing the past in generative models for more information on the usage of this argument.

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.

Note

This class is nearly identical to the PyTorch implementation of BERT in Huggingface Transformers. For more information, visit the corresponding section in their documentation.

GPT2Config

class transformers.GPT2Config(vocab_size=50257, n_positions=1024, n_ctx=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, gradient_checkpointing=False, use_cache=True, bos_token_id=50256, eos_token_id=50256, **kwargs)

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 small architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

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_ctx (int, optional, defaults to 1024) – Dimensionality of the causal mask (usually same as n_positions).

  • 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 (int, 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.

  • gradient_checkpointing (bool, optional, defaults to False) – Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.

  • use_cache (bool, optional, defaults to True) – Whether or not the model should return the last key/values attentions (not used by all models).

Example:

>>> from transformers import GPT2Model, GPT2Config

>>> # Initializing a GPT2 configuration
>>> configuration = GPT2Config()

>>> # Initializing a model from the configuration
>>> model = GPT2Model(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

GPT2Tokenizer

class transformers.GPT2Tokenizer(vocab_file, merges_file, errors='replace', unk_token='<|endoftext|>', bos_token='<|endoftext|>', eos_token='<|endoftext|>', add_prefix_space=False, **kwargs)

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:

>>> 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.

Note

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.

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).

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str]

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

Parameters
  • save_directory (str) – The directory in which to save the vocabulary.

  • filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.

Returns

Paths to the files saved.

Return type

Tuple(str)

GPT2TokenizerFast

class transformers.GPT2TokenizerFast(vocab_file, merges_file, tokenizer_file=None, unk_token='<|endoftext|>', bos_token='<|endoftext|>', eos_token='<|endoftext|>', add_prefix_space=False, **kwargs)

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:

>>> 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 or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.

Note

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.

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.

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str]

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

Parameters
  • save_directory (str) – The directory in which to save the vocabulary.

  • filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.

Returns

Paths to the files saved.

Return type

Tuple(str)

slow_tokenizer_class

alias of transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer

GPT2 specific outputs

class transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput(loss: Optional[torch.FloatTensor] = None, mc_loss: Optional[torch.FloatTensor] = None, logits: torch.FloatTensor = None, mc_logits: torch.FloatTensor = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, hidden_states: Optional[Tuple[torch.FloatTensor]] = None, attentions: Optional[Tuple[torch.FloatTensor]] = None)

Base class for outputs of models predicting if two sentences are consecutive or not.

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).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

GPT2Model

class transformers.GPT2Model(config)

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.

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.

forward(input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

The GPT2Model forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
  • input_ids (torch.LongTensor of shape (batch_size, 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 GPT2Tokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.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.

    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

A BaseModelOutputWithPastAndCrossAttentions (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor 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 + 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).

    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.

Return type

BaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

Example:

>>> from transformers import GPT2Tokenizer, GPT2Model
>>> import torch

>>> tokenizer = GPT2Tokenizer.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

GPT2ModelWithHeads

class transformers.GPT2ModelWithHeads(config)

The GPT2 Model that allows the loading of different heads dor different tasks. This enables a flexible use of the models and adpters.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters:
config (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.

property active_head

The active prediction head configuration of this model. Can be either the name of a single available head (string) or a list of multiple available heads. In case of a list of heads, the same base model is forwarded through all specified heads.

Returns

A string or a list of strings describing the active head configuration.

Return type

Union[str, List[str]]

add_adapter(adapter_name: str, config=None)

Adds a new adapter module of the specified type to the model.

Parameters
  • adapter_name (str) – The name of the adapter module to be added.

  • config (str or dict, optional) –

    The adapter configuration, can be either:

    • the string identifier of a pre-defined configuration dictionary

    • a configuration dictionary specifying the full config

    • if not given, the default configuration for this adapter type will be used

add_classification_head(head_name, num_labels=2, layers=2, activation_function='tanh', overwrite_ok=False, multilabel=False, id2label=None)

Adds a sequence classification head on top of the model.

Parameters
  • head_name (str) – The name of the head.

  • num_labels (int, optional) – Number of classification labels. Defaults to 2.

  • layers (int, optional) – Number of layers. Defaults to 2.

  • activation_function (str, optional) – Activation function. Defaults to ‘tanh’.

  • overwrite_ok (bool, optional) – Force overwrite if a head with the same name exists. Defaults to False.

  • multilabel (bool, optional) – Enable multilabel classification setup. Defaults to False.

add_fusion(adapter_names: Union[transformers.adapters.composition.Fuse, list], adapter_fusion_config=None, override_kwargs=None)

Adds AdapterFusion to the model with alll the necessary configurations and weight initializations

Parameters
  • adapter_names – a list of adapter names which should be fused

  • adapter_fusion_config (str or dict) –

    adapter fusion configuration, can be either:

    • a string identifying a pre-defined adapter fusion configuration

    • a dictionary representing the adapter fusion configuration

    • the path to a file containing the adapter fusion configuration

  • override_kwargs – dictionary items for values which should be overwritten in the default AdapterFusion configuration

forward(input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, head=None, return_dict=None, **kwargs)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

freeze_model(freeze=True)

Freezes all weights of the model.

get_labels(head_name=None)

Returns the labels the given head is assigning/predictin

Parameters
  • head_name – (str, optional) the name of the head which labels should be returned. Default is None.

  • the name is None the labels of the active head are returned (If) –

Returns: labels

get_labels_dict(head_name=None)

Returns the id2label dict for the given hea

Parameters
  • head_name – (str, optional) the name of the head which labels should be returned. Default is None.

  • the name is None the labels of the active head are returned (If) –

Returns: id2label

load_adapter(adapter_name_or_path: str, config: Union[dict, str] = None, version: str = None, model_name: str = None, load_as: str = None, with_head: bool = True, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None, **kwargs) → str

Loads a pre-trained pytorch adapter module from the local file system or a remote location.

Parameters
  • adapter_name_or_path (str) –

    can be either:

    • the identifier of a pre-trained task adapter to be loaded from Adapter Hub

    • a path to a directory containing adapter weights saved using model.saved_adapter()

    • a URL pointing to a zip folder containing a saved adapter module

  • config (dict or str, optional) – The requested configuration of the adapter. If not specified, will be either: - the default adapter config for the requested adapter if specified - the global default adapter config

  • version (str, optional) – The version of the adapter to be loaded.

  • model_name (str, optional) – The string identifier of the pre-trained model.

  • load_as (str, optional) – Load the adapter using this name. By default, the name with which the adapter was saved will be used.

Returns

The name with which the adapter was added to the model.

Return type

str

load_adapter_fusion(adapter_fusion_name_or_path: str, load_as: str = None, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None, **kwargs) → str

Loads a pre-trained pytorch adapter module from the local file system or a remote location.

Parameters
  • adapter_fusion_name_or_path (str) –

    can be either:

    • the identifier of a pre-trained task adapter fusion module to be loaded from Adapter Hub

    • a path to a directory containing adapter weights saved using model.saved_adapter()

    • a URL pointing to a zip folder containing a saved adapter module

  • config (dict or str, optional) – The requested configuration of the adapter fusion. If not specified, will be either: - the default adapter config for the requested adapter fusion if specified - the global default adapter fusion config

  • model_name (str, optional) – The string identifier of the pre-trained model.

  • load_as (str, optional) – Load the adapter using this name. By default, the name with which the adapter was saved will be used.

Returns

The name with which the adapter was added to the model.

Return type

str

pre_transformer_forward()

This method should be called by every adapter-implementing model at the very beginning of the forward() method.

save_adapter(save_directory: str, adapter_name: str, with_head: bool = True, meta_dict: dict = None, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None)

Saves an adapter and its configuration file to a directory so that it can be shared or reloaded using load_adapter().

Parameters
  • save_directory (str) – Path to a directory where the adapter should be saved.

  • adapter_name (str) – Name of the adapter to be saved.

Raises

ValueError – If the given adapter name is invalid.

save_adapter_fusion(save_directory: str, adapter_names: list, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None)

Saves an adapter and its configuration file to a directory so that it can be shared or reloaded using load_adapter().

Parameters
  • save_directory (str) – Path to a directory where the adapter should be saved.

  • adapter_name (str) – Name of the adapter to be saved.

Raises

ValueError – If the given adapter name is invalid.

save_all_adapter_fusions(save_directory: str, meta_dict: dict = None, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None)

Saves all adapters of this model together with their configuration to subfolders of the given location.

Parameters

save_directory (str) – Path to a directory where the adapters should be saved.

save_all_adapters(save_directory: str, with_head: bool = True, meta_dict: dict = None, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None)

Saves all adapters of this model together with their configuration to subfolders of the given location.

Parameters

save_directory (str) – Path to a directory where the adapters should be saved.

set_active_adapters(adapter_setup: Union[list, transformers.adapters.composition.AdapterCompositionBlock], skip_layers: Optional[List[int]] = None)

Sets the adapter modules to be used by default in every forward pass. This setting can be overriden by passing the adapter_names parameter in the foward() pass. If no adapter with the given name is found, no module of the respective type will be activated. In case the calling model class supports named prediction heads, this method will attempt to activate a prediction head with the name of the last adapter in the list of passed adapter names.

Parameters

adapter_setup (list) – The list of adapters to be activated by default. Can be a fusion or stacking configuration.

set_adapter_fusion_config(adapter_fusion_config, override_kwargs=None)

Sets the adapter fusion configuration.

Parameters

adapter_fusion_config (str or dict) –

adapter fusion configuration, can be either:

  • a string identifying a pre-defined adapter fusion configuration

  • a dictionary representing the adapter fusion configuration

  • the path to a file containing the adapter fusion configuration

train_adapter(adapter_setup: Union[list, transformers.adapters.composition.AdapterCompositionBlock])

Sets the model into mode for training the given adapters.

train_fusion(adapter_setup: Union[list, transformers.adapters.composition.AdapterCompositionBlock], unfreeze_adapters=False)

Sets the model into mode for training of adapter fusion determined by a list of adapter names.

GPT2LMHeadModel

class transformers.GPT2LMHeadModel(config)

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.

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.

forward(input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

The GPT2LMHeadModel forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
  • input_ids (torch.LongTensor of shape (batch_size, 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 GPT2Tokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.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.

    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

A CausalLMOutputWithCrossAttentions (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor 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 + 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).

    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.

Return type

CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

Example:

>>> import torch
>>> from transformers import GPT2Tokenizer, GPT2LMHeadModel

>>> tokenizer = GPT2Tokenizer.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(config)

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.

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.

forward(input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, mc_token_ids=None, labels=None, mc_labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs)

The GPT2DoubleHeadsModel forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
  • input_ids (torch.LongTensor of shape (batch_size, 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 GPT2Tokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.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.

    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

A GPT2DoubleHeadsModelOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor 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).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> import torch
>>> from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel

>>> tokenizer = GPT2Tokenizer.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]'})

>>> 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 = 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

Return type

GPT2DoubleHeadsModelOutput or tuple(torch.FloatTensor)

GPT2ForSequenceClassification

class transformers.GPT2ForSequenceClassification(config)

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.

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.

forward(input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

The GPT2ForSequenceClassification forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Parameters
  • input_ids (torch.LongTensor of shape (batch_size, 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 GPT2Tokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.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.

    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

A SequenceClassifierOutputWithPast (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor 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(tupel(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 + 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).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Return type

SequenceClassifierOutputWithPast or tuple(torch.FloatTensor)

Example:

>>> from transformers import GPT2Tokenizer, GPT2ForSequenceClassification
>>> import torch

>>> tokenizer = GPT2Tokenizer.from_pretrained('microsoft/dialogrpt')
>>> model = GPT2ForSequenceClassification.from_pretrained('microsoft/dialogrpt')

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits