Blenderbot

**DISCLAIMER:** If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title) .

## [](#overview)Overview

The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://arxiv.org/pdf/2004.13637.pdf) 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._

Tips:

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

This model was contributed by [sshleifer](https://huggingface.co/sshleifer). The authors’ code can be found [here](https://github.com/facebookresearch/ParlAI) .

## [](#implementation-notes)Implementation Notes

-   Blenderbot uses a standard [seq2seq model transformer](https://arxiv.org/pdf/1706.03762.pdf) based architecture.
-   Available checkpoints can be found in the [model hub](https://huggingface.co/models?search=blenderbot).
-   This is the _default_ Blenderbot model class. However, some smaller checkpoints, such as `facebook/blenderbot_small_90M`, have a different architecture and consequently should be used with [BlenderbotSmall](blenderbot-small).

## [](#usage)Usage

Here is an example of model usage:

```
>>> 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?
"]```

## [](#documentation-resources)Documentation resources

-   [Causal language modeling task guide](../tasks/language_modeling)
-   [Translation task guide](../tasks/translation)
-   [Summarization task guide](../tasks/summarization)

## [](#transformers.BlenderbotConfig)BlenderbotConfig

### class transformers.BlenderbotConfig

[](#transformers.BlenderbotConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/configuration_blenderbot.py#L36)

( vocab\_size = 8008max\_position\_embeddings = 128encoder\_layers = 2encoder\_ffn\_dim = 10240encoder\_attention\_heads = 32decoder\_layers = 24decoder\_ffn\_dim = 10240decoder\_attention\_heads = 32encoder\_layerdrop = 0.0decoder\_layerdrop = 0.0use\_cache = Trueis\_encoder\_decoder = Trueactivation\_function = 'gelu'd\_model = 2560dropout = 0.1attention\_dropout = 0.0activation\_dropout = 0.0init\_std = 0.02decoder\_start\_token\_id = 1scale\_embedding = Falsepad\_token\_id = 0bos\_token\_id = 1eos\_token\_id = 2encoder\_no\_repeat\_ngram\_size = 3forced\_eos\_token\_id = 2\*\*kwargs )

This is the configuration class to store the configuration of a [BlenderbotModel](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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 Blenderbot [facebook/blenderbot-3B](https://huggingface.co/facebook/blenderbot-3B) architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

```
>>> from transformers import BlenderbotConfig, BlenderbotModel

>>> 
>>> configuration = BlenderbotConfig()

>>> 
>>> model = BlenderbotModel(configuration)

>>> 
>>> configuration = model.config```

## [](#transformers.BlenderbotTokenizer)BlenderbotTokenizer

### class transformers.BlenderbotTokenizer

[](#transformers.BlenderbotTokenizer)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/tokenization_blenderbot.py#L92)

( vocab\_filemerges\_fileerrors = 'replace'bos\_token = '
'eos\_token = '
'sep\_token = '
'cls\_token = '
'unk\_token = '<unk>'pad\_token = '<pad>'mask\_token = '<mask>'add\_prefix\_space = False\*\*kwargs )

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](/docs/transformers/v4.30.0/en/main_classes/tokenizer#transformers.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

[](#transformers.BlenderbotTokenizer.build_inputs_with_special_tokens)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/tokenization_blenderbot.py#L400)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.BlenderbotTokenizer.build_inputs_with_special_tokens.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs to which the special tokens will be added
-   [](#transformers.BlenderbotTokenizer.build_inputs_with_special_tokens.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Will be ignored

list of [input IDs](../glossary#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 
`

## [](#transformers.BlenderbotTokenizerFast)BlenderbotTokenizerFast

### class transformers.BlenderbotTokenizerFast

[](#transformers.BlenderbotTokenizerFast)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py#L50)

( vocab\_file = Nonemerges\_file = Nonetokenizer\_file = Noneerrors = 'replace'bos\_token = '
'eos\_token = '
'sep\_token = '
'cls\_token = '
'unk\_token = '<unk>'pad\_token = '<pad>'mask\_token = '<mask>'add\_prefix\_space = Falsetrim\_offsets = True\*\*kwargs )

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](/docs/transformers/v4.30.0/en/main_classes/tokenizer#transformers.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

[](#transformers.BlenderbotTokenizerFast.build_inputs_with_special_tokens)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py#L284)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.BlenderbotTokenizerFast.build_inputs_with_special_tokens.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs to which the special tokens will be added
-   [](#transformers.BlenderbotTokenizerFast.build_inputs_with_special_tokens.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Will be ignored

list of [input IDs](../glossary#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 
`

## [](#transformers.BlenderbotModel)BlenderbotModel

See `transformers.BartModel` for arguments to _forward_ and _generate_

### class transformers.BlenderbotModel

[](#transformers.BlenderbotModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_blenderbot.py#L1099)

( config: BlenderbotConfig )

Parameters

-   [](#transformers.BlenderbotModel.config)**config** ([BlenderbotConfig](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.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 [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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

[](#transformers.BlenderbotModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_blenderbot.py#L1141)

( input\_ids: typing.Optional\[torch.LongTensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonedecoder\_input\_ids: typing.Optional\[torch.LongTensor\] = Nonedecoder\_attention\_mask: typing.Optional\[torch.LongTensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonedecoder\_head\_mask: typing.Optional\[torch.Tensor\] = Nonecross\_attn\_head\_mask: typing.Optional\[torch.Tensor\] = Noneencoder\_outputs: typing.Union\[typing.Tuple, transformers.modeling\_outputs.BaseModelOutput, NoneType\] = Nonepast\_key\_values: typing.Optional\[typing.List\[torch.FloatTensor\]\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonedecoder\_inputs\_embeds: typing.Optional\[torch.FloatTensor\] = Noneuse\_cache: typing.Optional\[bool\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.Seq2SeqModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or `tuple(torch.FloatTensor)`

The [BlenderbotModel](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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 `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> 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]```

## [](#transformers.BlenderbotForConditionalGeneration)BlenderbotForConditionalGeneration

See [BartForConditionalGeneration](/docs/transformers/v4.30.0/en/model_doc/bart#transformers.BartForConditionalGeneration) for arguments to _forward_ and _generate_

### class transformers.BlenderbotForConditionalGeneration

[](#transformers.BlenderbotForConditionalGeneration)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_blenderbot.py#L1239)

( config: BlenderbotConfig )

Parameters

-   [](#transformers.BlenderbotForConditionalGeneration.config)**config** ([BlenderbotConfig](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.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 [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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

[](#transformers.BlenderbotForConditionalGeneration.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_blenderbot.py#L1300)

( input\_ids: typing.Optional\[torch.LongTensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonedecoder\_input\_ids: typing.Optional\[torch.LongTensor\] = Nonedecoder\_attention\_mask: typing.Optional\[torch.LongTensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonedecoder\_head\_mask: typing.Optional\[torch.Tensor\] = Nonecross\_attn\_head\_mask: typing.Optional\[torch.Tensor\] = Noneencoder\_outputs: typing.Union\[typing.Tuple, transformers.modeling\_outputs.BaseModelOutput, NoneType\] = Nonepast\_key\_values: typing.Optional\[typing.List\[torch.FloatTensor\]\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonedecoder\_inputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneuse\_cache: typing.Optional\[bool\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.Seq2SeqLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`

The [BlenderbotForConditionalGeneration](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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 `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

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.```

## [](#transformers.BlenderbotForCausalLM)BlenderbotForCausalLM

### class transformers.BlenderbotForCausalLM

[](#transformers.BlenderbotForCausalLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_blenderbot.py#L1436)

( config )

#### forward

[](#transformers.BlenderbotForCausalLM.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_blenderbot.py#L1469)

( input\_ids: LongTensor = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Noneencoder\_hidden\_states: typing.Optional\[torch.FloatTensor\] = Noneencoder\_attention\_mask: typing.Optional\[torch.FloatTensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonecross\_attn\_head\_mask: typing.Optional\[torch.Tensor\] = Nonepast\_key\_values: typing.Optional\[typing.List\[torch.FloatTensor\]\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneuse\_cache: typing.Optional\[bool\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.CausalLMOutputWithCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`

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```

## [](#transformers.TFBlenderbotModel)TFBlenderbotModel

### class transformers.TFBlenderbotModel

[](#transformers.TFBlenderbotModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_tf_blenderbot.py#L1122)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFBlenderbotModel.config)**config** ([BlenderbotConfig](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

-   having all inputs as keyword arguments (like PyTorch models), or
-   having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

-   a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
-   a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
-   a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFBlenderbotModel.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_tf_blenderbot.py#L1150)

( input\_ids: tf.Tensor | None = Noneattention\_mask: tf.Tensor | None = Nonedecoder\_input\_ids: tf.Tensor | None = Nonedecoder\_attention\_mask: tf.Tensor | None = Nonedecoder\_position\_ids: tf.Tensor | None = Nonehead\_mask: tf.Tensor | None = Nonedecoder\_head\_mask: tf.Tensor | None = Nonecross\_attn\_head\_mask: tf.Tensor | None = Noneencoder\_outputs: Optional\[Union\[Tuple, TFBaseModelOutput\]\] = Nonepast\_key\_values: List\[tf.Tensor\] | None = Noneinputs\_embeds: tf.Tensor | None = Nonedecoder\_inputs\_embeds: tf.Tensor | None = Noneuse\_cache: Optional\[bool\] = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonetraining: Optional\[bool\] = False\*\*kwargs ) → [transformers.modeling\_tf\_outputs.TFSeq2SeqModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput) or `tuple(tf.Tensor)`

The [TFBlenderbotModel](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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 `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> 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```

## [](#transformers.TFBlenderbotForConditionalGeneration)TFBlenderbotForConditionalGeneration

### class transformers.TFBlenderbotForConditionalGeneration

[](#transformers.TFBlenderbotForConditionalGeneration)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_tf_blenderbot.py#L1243)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFBlenderbotForConditionalGeneration.config)**config** ([BlenderbotConfig](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

-   having all inputs as keyword arguments (like PyTorch models), or
-   having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

-   a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
-   a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
-   a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFBlenderbotForConditionalGeneration.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_tf_blenderbot.py#L1297)

( input\_ids: tf.Tensor | None = Noneattention\_mask: tf.Tensor | None = Nonedecoder\_input\_ids: tf.Tensor | None = Nonedecoder\_attention\_mask: tf.Tensor | None = Nonedecoder\_position\_ids: tf.Tensor | None = Nonehead\_mask: tf.Tensor | None = Nonedecoder\_head\_mask: tf.Tensor | None = Nonecross\_attn\_head\_mask: tf.Tensor | None = Noneencoder\_outputs: Optional\[Union\[Tuple, TFBaseModelOutput\]\] = Nonepast\_key\_values: List\[tf.Tensor\] | None = Noneinputs\_embeds: tf.Tensor | None = Nonedecoder\_inputs\_embeds: tf.Tensor | None = Noneuse\_cache: Optional\[bool\] = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.modeling\_tf\_outputs.TFSeq2SeqLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput) or `tuple(tf.Tensor)`

The [TFBlenderbotForConditionalGeneration](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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 `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

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])```

## [](#transformers.FlaxBlenderbotModel)FlaxBlenderbotModel

### class transformers.FlaxBlenderbotModel

[](#transformers.FlaxBlenderbotModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_flax_blenderbot.py#L1217)

( config: BlenderbotConfiginput\_shape: typing.Tuple\[int\] = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = True\*\*kwargs )

Parameters

-   [](#transformers.FlaxBlenderbotModel.config)**config** ([BlenderbotConfig](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel.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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) 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](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxBlenderbotModel.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_flax_blenderbot.py#L1152)

( input\_ids: ndarrayattention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_input\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_attention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneposition\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_position\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetrain: bool = Falseparams: dict = Nonedropout\_rng: PRNGKey = None ) → [transformers.modeling\_flax\_outputs.FlaxSeq2SeqModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput) or `tuple(torch.FloatTensor)`

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 `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> 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

[](#transformers.FlaxBlenderbotModel.encode)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_flax_blenderbot.py#L973)

( input\_ids: ndarrayattention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneposition\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetrain: bool = Falseparams: dict = Nonedropout\_rng: PRNGKey = None ) → [transformers.modeling\_flax\_outputs.FlaxBaseModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or `tuple(torch.FloatTensor)`

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

[](#transformers.FlaxBlenderbotModel.decode)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_flax_blenderbot.py#L1036)

( decoder\_input\_idsencoder\_outputsencoder\_attention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_attention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_position\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonepast\_key\_values: dict = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetrain: bool = Falseparams: dict = Nonedropout\_rng: PRNGKey = None ) → [transformers.modeling\_flax\_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions) or `tuple(torch.FloatTensor)`

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```

## [](#transformers.FlaxBlenderbotForConditionalGeneration)FlaxBlenderbotForConditionalGeneration

### class transformers.FlaxBlenderbotForConditionalGeneration

[](#transformers.FlaxBlenderbotForConditionalGeneration)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_flax_blenderbot.py#L1302)

( config: BlenderbotConfiginput\_shape: typing.Tuple\[int\] = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = True\*\*kwargs )

Parameters

-   [](#transformers.FlaxBlenderbotForConditionalGeneration.config)**config** ([BlenderbotConfig](/docs/transformers/v4.30.0/en/model_doc/blenderbot#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel.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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) 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](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxBlenderbotForConditionalGeneration.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_flax_blenderbot.py#L1152)

( input\_ids: ndarrayattention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_input\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_attention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneposition\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_position\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetrain: bool = Falseparams: dict = Nonedropout\_rng: PRNGKey = None ) → [transformers.modeling\_flax\_outputs.FlaxSeq2SeqLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput) or `tuple(torch.FloatTensor)`

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 `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

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

[](#transformers.FlaxBlenderbotForConditionalGeneration.encode)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_flax_blenderbot.py#L973)

( input\_ids: ndarrayattention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneposition\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetrain: bool = Falseparams: dict = Nonedropout\_rng: PRNGKey = None ) → [transformers.modeling\_flax\_outputs.FlaxBaseModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput) or `tuple(torch.FloatTensor)`

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

[](#transformers.FlaxBlenderbotForConditionalGeneration.decode)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blenderbot/modeling_flax_blenderbot.py#L1306)

( decoder\_input\_idsencoder\_outputsencoder\_attention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_attention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_position\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonepast\_key\_values: dict = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetrain: bool = Falseparams: dict = Nonedropout\_rng: PRNGKey = None ) → [transformers.modeling\_flax\_outputs.FlaxCausalLMOutputWithCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`

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```