Blenderbot (original) (raw)

Overview

The Blender chatbot model was proposed in Recipes for building an open-domain chatbot Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.

The abstract of the paper is the following:

Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.

This model was contributed by sshleifer. The authors’ code can be found here .

Usage tips and example

Blenderbot is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.

An example:

from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration

mname = "facebook/blenderbot-400M-distill" model = BlenderbotForConditionalGeneration.from_pretrained(mname) tokenizer = BlenderbotTokenizer.from_pretrained(mname) UTTERANCE = "My friends are cool but they eat too many carbs." inputs = tokenizer([UTTERANCE], return_tensors="pt") reply_ids = model.generate(**inputs) print(tokenizer.batch_decode(reply_ids)) [" That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"]

Implementation Notes

• Blenderbot uses a standard seq2seq model transformer based architecture.
• Available checkpoints can be found in the model hub.
• This is the default Blenderbot model class. However, some smaller checkpoints, such asfacebook/blenderbot_small_90M, have a different architecture and consequently should be used withBlenderbotSmall.

Resources

• Causal language modeling task guide
• Translation task guide
• Summarization task guide

BlenderbotConfig

class transformers.BlenderbotConfig

< source >

( vocab_size = 8008 max_position_embeddings = 128 encoder_layers = 2 encoder_ffn_dim = 10240 encoder_attention_heads = 32 decoder_layers = 24 decoder_ffn_dim = 10240 decoder_attention_heads = 32 encoder_layerdrop = 0.0 decoder_layerdrop = 0.0 use_cache = True is_encoder_decoder = True activation_function = 'gelu' d_model = 2560 dropout = 0.1 attention_dropout = 0.0 activation_dropout = 0.0 init_std = 0.02 decoder_start_token_id = 1 scale_embedding = False pad_token_id = 0 bos_token_id = 1 eos_token_id = 2 encoder_no_repeat_ngram_size = 3 forced_eos_token_id = 2 **kwargs )

Parameters

• vocab_size (int, optional, defaults to 50265) — Vocabulary size of the Blenderbot model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling BlenderbotModel or TFBlenderbotModel.
• d_model (int, optional, defaults to 1024) — Dimensionality of the layers and the pooler layer.
• encoder_layers (int, optional, defaults to 12) — Number of encoder layers.
• decoder_layers (int, optional, defaults to 12) — Number of decoder layers.
• encoder_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer encoder.
• decoder_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
• decoder_ffn_dim (int, optional, defaults to 4096) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
• encoder_ffn_dim (int, optional, defaults to 4096) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
• activation_function (str or function, optional, defaults to "gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu","relu", "silu" and "gelu_new" are supported.
• dropout (float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
• attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
• activation_dropout (float, optional, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer.
• max_position_embeddings (int, optional, defaults to 128) — 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).
• init_std (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
• encoder_layerdrop (float, optional, defaults to 0.0) — The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details.
• decoder_layerdrop (float, optional, defaults to 0.0) — The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details.
• scale_embedding (bool, optional, defaults to False) — Scale embeddings by diving by sqrt(d_model).
• use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models)
• forced_eos_token_id (int, optional, defaults to 2) — The id of the token to force as the last generated token when max_length is reached. Usually set toeos_token_id.

This is the configuration class to store the configuration of a BlenderbotModel. It is used to instantiate an Blenderbot 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 Blenderbotfacebook/blenderbot-3B architecture.

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

Example:

from transformers import BlenderbotConfig, BlenderbotModel

configuration = BlenderbotConfig()

model = BlenderbotModel(configuration)

configuration = model.config

BlenderbotTokenizer

class transformers.BlenderbotTokenizer

< source >

( vocab_file merges_file errors = 'replace' bos_token = '' eos_token = '' sep_token = '' cls_token = '' unk_token = '' pad_token = '' mask_token = '' 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. Seebytes.decode for more information.
• bos_token (str, optional, defaults to "<s>") — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.
• eos_token (str, optional, defaults to "</s>") — The end of sequence token.
  When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.
• sep_token (str, optional, defaults to "</s>") — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
• cls_token (str, optional, defaults to "<s>") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.
• unk_token (str, optional, defaults to "<unk>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
• pad_token (str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths.
• mask_token (str, optional, defaults to "<mask>") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
• 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. (Blenderbot tokenizer detect beginning of words by the preceding space).

Constructs a Blenderbot tokenizer, derived from the GPT-2 tokenizer, using 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 BlenderbotTokenizer

tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B") tokenizer.add_prefix_space = False tokenizer("Hello world")["input_ids"] [47, 921, 86, 1085, 2]

tokenizer(" Hello world")["input_ids"] [6950, 1085, 2]

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.

build_inputs_with_special_tokens

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

• token_ids_0 (List[int]) — List of IDs to which the special tokens will be added
• token_ids_1 (List[int], optional) — Will be ignored

list of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Blenderbot sequence has the following format:

• single sequence: X </s>

BlenderbotTokenizerFast

class transformers.BlenderbotTokenizerFast

< source >

( vocab_file = None merges_file = None tokenizer_file = None errors = 'replace' bos_token = '' eos_token = '' sep_token = '' cls_token = '' unk_token = '' pad_token = '' mask_token = '' add_prefix_space = False trim_offsets = True **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. Seebytes.decode for more information.
• bos_token (str, optional, defaults to "<s>") — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.
• eos_token (str, optional, defaults to "</s>") — The end of sequence token.
  When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.
• sep_token (str, optional, defaults to "</s>") — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
• cls_token (str, optional, defaults to "<s>") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.
• unk_token (str, optional, defaults to "<unk>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
• pad_token (str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths.
• mask_token (str, optional, defaults to "<mask>") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
• 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. (Blenderbot tokenizer detect beginning of words by the preceding space).
• trim_offsets (bool, optional, defaults to True) — Whether the post processing step should trim offsets to avoid including whitespaces.

Construct a “fast” Blenderbot tokenizer (backed by HuggingFace’s tokenizers library), derived from the GPT-2 tokenizer, using 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 BlenderbotTokenizerFast

tokenizer = BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B") tokenizer("Hello world")["input_ids"] [6950, 1085, 2]

tokenizer(" Hello world")["input_ids"] [6950, 1085, 2]

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 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.

build_inputs_with_special_tokens

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

• token_ids_0 (List[int]) — List of IDs to which the special tokens will be added
• token_ids_1 (List[int], optional) — Will be ignored

list of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Blenderbot sequence has the following format:

• single sequence: X </s>

BlenderbotModel

See BartModel for arguments to forward and generate

class transformers.BlenderbotModel

< source >

( config: BlenderbotConfig )

Parameters

• config (BlenderbotConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.

The bare Blenderbot Model 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 attention_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Union[typing.Tuple, transformers.modeling_outputs.BaseModelOutput, NoneType] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.Tensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.Tensor 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?
• decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are decoder input IDs?
  Blenderbot uses the bos_token_id as the starting token for decoder_input_ids generation. Ifpast_key_values is used, optionally only the last decoder_input_ids have to be input (seepast_key_values).
• decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
• head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• encoder_outputs (tuple(tuple(torch.FloatTensor), optional) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
• 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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
  If past_key_values 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 alldecoder_input_ids of shape (batch_size, sequence_length).
• 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.
• decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) — Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convertdecoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.
• use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (seepast_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.

A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BlenderbotConfig) 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 decoder 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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
• decoder_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 decoder at the output of each layer plus the optional initial embedding outputs.
• decoder_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 of the decoder, 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).
  Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
• encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
• encoder_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 encoder at the output of each layer plus the optional initial embedding outputs.
• encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The BlenderbotModel forward method, overrides the __call__ special method.

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

Example:

from transformers import AutoTokenizer, BlenderbotModel

model = BlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill") tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt") decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids
outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_input_ids)

last_hidden_states = outputs.last_hidden_state list(last_hidden_states.shape) [1, 6, 1280]

BlenderbotForConditionalGeneration

See BartForConditionalGeneration for arguments to forward and generate

class transformers.BlenderbotForConditionalGeneration

< source >

( config: BlenderbotConfig )

Parameters

• config (BlenderbotConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.

The Blenderbot Model with a language modeling head. Can be used for summarization. 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.Tensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Union[typing.Tuple, transformers.modeling_outputs.BaseModelOutput, NoneType] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.Tensor] = None decoder_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.Seq2SeqLMOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.Tensor 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?
• decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are decoder input IDs?
  Blenderbot uses the bos_token_id as the starting token for decoder_input_ids generation. Ifpast_key_values is used, optionally only the last decoder_input_ids have to be input (seepast_key_values).
• decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
• head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• encoder_outputs (tuple(tuple(torch.FloatTensor), optional) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
• 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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
  If past_key_values 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 alldecoder_input_ids of shape (batch_size, sequence_length).
• 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.
• decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) — Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convertdecoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.
• use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (seepast_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 masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].

A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BlenderbotConfig) and inputs.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
• logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
• past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)) and 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
• decoder_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 decoder at the output of each layer plus the initial embedding outputs.
• decoder_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 of the decoder, 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).
  Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
• encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
• encoder_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 encoder at the output of each layer plus the initial embedding outputs.
• encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The BlenderbotForConditionalGeneration forward method, overrides the __call__ special method.

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

Conversation example:

from transformers import AutoTokenizer, BlenderbotForConditionalGeneration

mname = "facebook/blenderbot-400M-distill" model = BlenderbotForConditionalGeneration.from_pretrained(mname) tokenizer = AutoTokenizer.from_pretrained(mname) UTTERANCE = "My friends are cool but they eat too many carbs." print("Human: ", UTTERANCE) Human: My friends are cool but they eat too many carbs.

inputs = tokenizer([UTTERANCE], return_tensors="pt") reply_ids = model.generate(**inputs) print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]) Bot: That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?

REPLY = "I'm not sure" print("Human: ", REPLY) Human: I'm not sure

NEXT_UTTERANCE = ( ... "My friends are cool but they eat too many carbs. That's unfortunate. " ... "Are they trying to lose weight or are they just trying to be healthier? " ... " I'm not sure." ... ) inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt") next_reply_ids = model.generate(**inputs) print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0]) Bot: I see. Well, it's good that they're trying to change their eating habits.

BlenderbotForCausalLM

class transformers.BlenderbotForCausalLM

< source >

( config )

forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.FloatTensor] = None encoder_attention_mask: typing.Optional[torch.FloatTensor] = None head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.List[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.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.Tensor 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?
• encoder_hidden_states (torch.FloatTensor 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 (torch.FloatTensor 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]:
• head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• 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 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
  If past_key_values 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).
• labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
• 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).
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
• 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.

A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BlenderbotConfig) 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 (seepast_key_values input) to speed up sequential decoding.

Example:

from transformers import AutoTokenizer, BlenderbotForCausalLM

tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill") model = BlenderbotForCausalLM.from_pretrained("facebook/blenderbot-400M-distill", add_cross_attention=False) assert model.config.is_decoder, f"{model.class} has to be configured as a decoder." inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs)

logits = outputs.logits expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size] list(logits.shape) == expected_shape True

TFBlenderbotModel

class transformers.TFBlenderbotModel

< source >

( config: BlenderbotConfig *inputs **kwargs )

Parameters

• config (BlenderbotConfig) — 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 BLENDERBOT Model 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 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 withsubclassing 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: tf.Tensor | None = None attention_mask: tf.Tensor | None = None decoder_input_ids: tf.Tensor | None = None decoder_attention_mask: tf.Tensor | None = None decoder_position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None decoder_head_mask: tf.Tensor | None = None cross_attn_head_mask: tf.Tensor | None = None encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None past_key_values: List[tf.Tensor] | None = None inputs_embeds: tf.Tensor | None = None decoder_inputs_embeds: 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 **kwargs ) → transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor)

Parameters

• input_ids (tf.Tensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (tf.Tensor 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?
• decoder_input_ids (tf.Tensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are decoder input IDs?
  Blenderbot uses the bos_token_id as the starting token for decoder_input_ids generation. Ifpast_key_values is used, optionally only the last decoder_input_ids have to be input (seepast_key_values).
• decoder_attention_mask (tf.Tensor of shape (batch_size, target_sequence_length), optional) — will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
• decoder_position_ids (tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
• head_mask (tf.Tensor of shape (encoder_layers, encoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• decoder_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• cross_attn_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• encoder_outputs (tf.FloatTensor, optional) — hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape (batch_size, sequence_length, hidden_size) is a sequence of
• 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_key_values 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 alldecoder_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 (seepast_key_values). Set to False during training, True during generation
• 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).

A transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or a tuple of tf.Tensor (ifreturn_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BlenderbotConfig) 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 decoder 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) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.
• decoder_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 decoder at the output of each layer plus the initial embedding outputs.
• decoder_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, 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.
• encoder_last_hidden_state (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 of the model.
• encoder_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 encoder at the output of each layer plus the initial embedding outputs.
• encoder_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 encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFBlenderbotModel forward method, overrides the __call__ special method.

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

Example:

from transformers import AutoTokenizer, TFBlenderbotModel import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill") model = TFBlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill")

inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") outputs = model(inputs)

last_hidden_states = outputs.last_hidden_state

TFBlenderbotForConditionalGeneration

class transformers.TFBlenderbotForConditionalGeneration

< source >

( config *inputs **kwargs )

Parameters

• config (BlenderbotConfig) — 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 BLENDERBOT Model with a language modeling head. Can be used for summarization. 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 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 withsubclassing 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: tf.Tensor | None = None attention_mask: tf.Tensor | None = None decoder_input_ids: tf.Tensor | None = None decoder_attention_mask: tf.Tensor | None = None decoder_position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None decoder_head_mask: tf.Tensor | None = None cross_attn_head_mask: tf.Tensor | None = None encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None past_key_values: List[tf.Tensor] | None = None inputs_embeds: tf.Tensor | None = None decoder_inputs_embeds: 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: tf.Tensor | None = None training: Optional[bool] = False ) → transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)

Parameters

• input_ids (tf.Tensor of shape ({0})) — Indices of input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (tf.Tensor of shape ({0}), 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?
• decoder_input_ids (tf.Tensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are decoder input IDs?
  Blenderbot uses the bos_token_id as the starting token for decoder_input_ids generation. Ifpast_key_values is used, optionally only the last decoder_input_ids have to be input (seepast_key_values).
• decoder_attention_mask (tf.Tensor of shape (batch_size, target_sequence_length), optional) — will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
• decoder_position_ids (tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
• head_mask (tf.Tensor of shape (encoder_layers, encoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• decoder_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• cross_attn_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• encoder_outputs (tf.FloatTensor, optional) — hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape (batch_size, sequence_length, hidden_size) is a sequence of
• 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_key_values 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 alldecoder_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 (seepast_key_values). Set to False during training, True during generation
• 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 masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].

A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or a tuple of tf.Tensor (ifreturn_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BlenderbotConfig) 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.
• logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
• past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
  Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.
• decoder_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 decoder at the output of each layer plus the initial embedding outputs.
• decoder_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, 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.
• encoder_last_hidden_state (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 of the model.
• encoder_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 encoder at the output of each layer plus the initial embedding outputs.
• encoder_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 encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFBlenderbotForConditionalGeneration forward method, overrides the __call__ special method.

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

Conversation example::

from transformers import AutoTokenizer, TFBlenderbotForConditionalGeneration

mname = "facebook/blenderbot-400M-distill" model = TFBlenderbotForConditionalGeneration.from_pretrained(mname) tokenizer = AutoTokenizer.from_pretrained(mname) UTTERANCE = "My friends are cool but they eat too many carbs." print("Human: ", UTTERANCE)

inputs = tokenizer([UTTERANCE], return_tensors="tf") reply_ids = model.generate(**inputs) print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])

REPLY = "I'm not sure" print("Human: ", REPLY) NEXT_UTTERANCE = ( ... "My friends are cool but they eat too many carbs. That's unfortunate. " ... "Are they trying to lose weight or are they just trying to be healthier? " ... " I'm not sure." ... ) inputs = tokenizer([NEXT_UTTERANCE], return_tensors="tf") next_reply_ids = model.generate(**inputs) print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])

FlaxBlenderbotModel

class transformers.FlaxBlenderbotModel

< source >

( config: BlenderbotConfig input_shape: typing.Tuple[int] = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs )

Parameters

• config (BlenderbotConfig) — 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 MBart 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 Linenflax.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: Array attention_mask: typing.Optional[jax.Array] = None decoder_input_ids: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None position_ids: typing.Optional[jax.Array] = None decoder_position_ids: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None ) → transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (jnp.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (jnp.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?
• decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are decoder input IDs?
  For translation and summarization training, decoder_input_ids should be provided. If nodecoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.
• decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
  If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
• 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].
• decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
• 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.

A transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BlenderbotConfig) 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 decoder 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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
• decoder_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 decoder at the output of each layer plus the initial embedding outputs.
• decoder_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 of the decoder, 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).
  Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
• encoder_last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
• encoder_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 encoder at the output of each layer plus the initial embedding outputs.
• encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxBlenderbotPreTrainedModel forward method, overrides the __call__ special method.

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

Example:

from transformers import AutoTokenizer, FlaxBlenderbotModel

tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill") model = FlaxBlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill")

inputs = tokenizer("Hello, my dog is cute", return_tensors="jax") outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state

encode

< source >

( input_ids: Array attention_mask: typing.Optional[jax.Array] = None position_ids: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None ) → transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (jnp.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (jnp.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].
• 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.

A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (<class 'transformers.models.blenderbot.configuration_blenderbot.BlenderbotConfig'>) 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.
• 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.

Example:

from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration

model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill") tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

text = "My friends are cool but they eat too many carbs." inputs = tokenizer(text, max_length=1024, return_tensors="jax") encoder_outputs = model.encode(**inputs)

decode

< source >

( decoder_input_ids encoder_outputs encoder_attention_mask: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None decoder_position_ids: typing.Optional[jax.Array] = None past_key_values: dict = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None ) → transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

Parameters

• decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) — Indices of decoder input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are decoder input IDs?
  For translation and summarization training, decoder_input_ids should be provided. If nodecoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.
• encoder_outputs (tuple(tuple(jnp.ndarray)) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
• encoder_attention_mask (jnp.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?
• decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
  If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
• decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder 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.

A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (<class 'transformers.models.blenderbot.configuration_blenderbot.BlenderbotConfig'>) 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 ifconfig.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 ifconfig.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_valuesinput) 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.

Example:

import jax.numpy as jnp from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration

model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill") tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

text = "My friends are cool but they eat too many carbs." inputs = tokenizer(text, max_length=1024, return_tensors="jax") encoder_outputs = model.encode(**inputs)

decoder_start_token_id = model.config.decoder_start_token_id decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

outputs = model.decode(decoder_input_ids, encoder_outputs) last_decoder_hidden_states = outputs.last_hidden_state

FlaxBlenderbotForConditionalGeneration

class transformers.FlaxBlenderbotForConditionalGeneration

< source >

( config: BlenderbotConfig input_shape: typing.Tuple[int] = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs )

Parameters

• config (BlenderbotConfig) — 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 Blenderbot Model with a language modeling head. Can be used for summarization. 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 Linenflax.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: Array attention_mask: typing.Optional[jax.Array] = None decoder_input_ids: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None position_ids: typing.Optional[jax.Array] = None decoder_position_ids: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None ) → transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (jnp.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (jnp.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?
• decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are decoder input IDs?
  For translation and summarization training, decoder_input_ids should be provided. If nodecoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.
• decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
  If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
• 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].
• decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
• 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.

A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (BlenderbotConfig) 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).
• 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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
• decoder_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 decoder at the output of each layer plus the initial embedding outputs.
• decoder_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 of the decoder, 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).
  Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
• encoder_last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
• encoder_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 encoder at the output of each layer plus the initial embedding outputs.
• encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxBlenderbotPreTrainedModel forward method, overrides the __call__ special method.

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

Conversation example::

from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration

model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill") tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

UTTERANCE = "My friends are cool but they eat too many carbs." inputs = tokenizer([UTTERANCE], max_length=1024, return_tensors="np")

reply_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5, early_stopping=True).sequences print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in reply_ids])

encode

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( input_ids: Array attention_mask: typing.Optional[jax.Array] = None position_ids: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None ) → transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (jnp.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (jnp.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].
• 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.

A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (<class 'transformers.models.blenderbot.configuration_blenderbot.BlenderbotConfig'>) 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.
• 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.

Example:

from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration

model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill") tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

text = "My friends are cool but they eat too many carbs." inputs = tokenizer(text, max_length=1024, return_tensors="jax") encoder_outputs = model.encode(**inputs)

decode

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( decoder_input_ids encoder_outputs encoder_attention_mask: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None decoder_position_ids: typing.Optional[jax.Array] = None past_key_values: dict = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None ) → transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

Parameters

• decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) — Indices of decoder input sequence tokens in the vocabulary.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are decoder input IDs?
  For translation and summarization training, decoder_input_ids should be provided. If nodecoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.
• encoder_outputs (tuple(tuple(jnp.ndarray)) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
• encoder_attention_mask (jnp.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?
• decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
  If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
• decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder 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.

A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (<class 'transformers.models.blenderbot.configuration_blenderbot.BlenderbotConfig'>) 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 (seepast_key_values input) to speed up sequential decoding.

Example:

import jax.numpy as jnp from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration

model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill") tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")

text = "My friends are cool but they eat too many carbs." inputs = tokenizer(text, max_length=1024, return_tensors="jax") encoder_outputs = model.encode(**inputs)

decoder_start_token_id = model.config.decoder_start_token_id decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

outputs = model.decode(decoder_input_ids, encoder_outputs) logits = outputs.logits

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