RoCBertForQuestionAnswering

] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None start_positions: typing.Optional[torch.Tensor] = None end_positions: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   input_shape_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the shape vocabulary.
Indi


RoCBertForQuestionAnswering

ces can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   input_pronunciation_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the pronunciation vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.


RoCBertForQuestionAnswering

What are token type IDs?   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tenso


RoCBertForQuestionAnswering

rs of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   start_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.   end_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into


RoCBertForQuestionAnswering

 account for computing the loss.   Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (RoCBertConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the


RoCBertForQuestionAnswering

embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The RoCBertForQuestionAnswering forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>>


RoCBertForQuestionAnswering

 from transformers import AutoTokenizer, RoCBertForQuestionAnswering
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("ArthurZ/dummy-rocbert-qa")
>>> model = RoCBertForQuestionAnswering.from_pretrained("ArthurZ/dummy-rocbert-qa")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
''
>>> # target is "nice puppet"
>>> target_start_index = torch.tensor([14])
>>> target_end_index = torch.tensor([15])
>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.loss
>>> round(loss.item(),


Overview

The Swin Transformer was proposed in Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
The abstract from the paper is the following:
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone
for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains,
such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text.
To address these differences, we propose a hierarchical Transformer whose representation is computed with \bold{S}hifted
\bold{win}dows. The shifted windowing


Overview

scheme brings greater efficiency by limiting self-attention computation to non-overlapping
local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at
various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it
compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense
prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation
(53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and
+2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of


Overview

 Transformer-based models as vision backbones.
The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures.
Tips:
One can use the AutoImageProcessor API to prepare images for the model. Swin pads the inputs supporting any input height and width (if divisible by 32). Swin can be used as a backbone. When output_hidden_states = True, it will output both hidden_states and reshaped_hidden_states. The reshaped_hidden_states have a shape of (batch, num_channels, height, width) rather than (batch_size, sequence_length, num_channels).
Swin Transformer architecture. Taken from the original paper.
This model was contributed by novice03. The Tensorflow version of this model was contributed by amyeroberts. The original code can be found here


Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Swin Transformer.
 Image Classification
SwinForImageClassification is supported by this example script and notebook. See also: Image classification task guide
Besides that:
SwinForMaskedImageModeling is supported by this example script.
If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.


SwinConfig

class transformers.SwinConfig  < source > ( image_size = 224 patch_size = 4 num_channels = 3 embed_dim = 96 depths = [2, 2, 6, 2] num_heads = [3, 6, 12, 24] window_size = 7 mlp_ratio = 4.0 qkv_bias = True hidden_dropout_prob = 0.0 attention_probs_dropout_prob = 0.0 drop_path_rate = 0.1 hidden_act = 'gelu' use_absolute_embeddings = False initializer_range = 0.02 layer_norm_eps = 1e-05 encoder_stride = 32 out_features = None out_indices = None **kwargs  )   Parameters   image_size (int, optional, defaults to 224) —
The size (resolution) of each image.   patch_size (int, optional, defaults to 4) —
The size (resolution) of each patch.   num_channels (int, optional, defaults to 3) —
The number of input channels.   embed_dim (int, optional, defaults to 96) —
Dimensionality of patch embedding.   depths (list(int), optional, defaults to [2, 2, 6, 2]) —
Depth of each layer in the Transformer encoder.   num_heads (list(int), optional, defaults to [3, 6, 12, 24]) —
Number of attention heads in


SwinConfig

 each layer of the Transformer encoder.   window_size (int, optional, defaults to 7) —
Size of windows.   mlp_ratio (float, optional, defaults to 4.0) —
Ratio of MLP hidden dimensionality to embedding dimensionality.   qkv_bias (bool, optional, defaults to True) —
Whether or not a learnable bias should be added to the queries, keys and values.   hidden_dropout_prob (float, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings and encoder.   attention_probs_dropout_prob (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.   drop_path_rate (float, optional, defaults to 0.1) —
Stochastic depth rate.   hidden_act (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder. If string, "gelu", "relu",
"selu" and "gelu_new" are supported.   use_absolute_embeddings (bool, optional, defaults to False) —
Whether or not to add absolute position embeddi


SwinConfig

ngs to the patch embeddings.   initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.   layer_norm_eps (float, optional, defaults to 1e-05) —
The epsilon used by the layer normalization layers.   encoder_stride (int, optional, defaults to 32) —
Factor to increase the spatial resolution by in the decoder head for masked image modeling.   out_features (List[str], optional) —
If used as backbone, list of features to output. Can be any of "stem", "stage1", "stage2", etc.
(depending on how many stages the model has). If unset and out_indices is set, will default to the
corresponding stages. If unset and out_indices is unset, will default to the last stage.   out_indices (List[int], optional) —
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and out_features is set, will default to the corresponding


SwinConfig

 stages.
If unset and out_features is unset, will default to the last stage.    This is the configuration class to store the configuration of a SwinModel. It is used to instantiate a Swin
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 Swin
microsoft/swin-tiny-patch4-window7-224
architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.  Example:   Copied >>> from transformers import SwinConfig, SwinModel
>>> # Initializing a Swin microsoft/swin-tiny-patch4-window7-224 style configuration
>>> configuration = SwinConfig()
>>> # Initializing a model (with random weights) from the microsoft/swin-tiny-patch4-window7-224 style configuration
>>> model = SwinModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.con


SwinModel

class transformers.SwinModel  < source > ( config add_pooling_layer = True use_mask_token = False  )   Parameters   config (SwinConfig) — 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 Swin Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch torch.nn.Module sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( pixel_values: typing.Optional[torch.FloatTensor] = None bool_masked_pos: typing.Optional[torch.BoolTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) →


SwinModel

transformers.models.swin.modeling_swin.SwinModelOutput or tuple(torch.FloatTensor)  Parameters   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.call()
for details.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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.   bool_masked_pos


SwinModel

 (torch.BoolTensor of shape (batch_size, num_patches), optional) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).   Returns
transformers.models.swin.modeling_swin.SwinModelOutput or tuple(torch.FloatTensor)

A transformers.models.swin.modeling_swin.SwinModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (SwinConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (torch.FloatTensor of shape (batch_size, hidden_size), optional, returned when add_pooling_layer=True is passed) — Average pooling of the last layer hidden-state.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch


SwinModel

.FloatTensor (one for the output of the embeddings + one for the output of each stage) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each stage) 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.
reshaped_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each stage) of
shape (batch_size, hidden_size, height, width).
Hidden-states of the model at the output of each layer plus the initial embedding outpu


SwinModel

ts reshaped to
include the spatial dimensions.
  The SwinModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoImageProcessor, SwinModel
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> model = SwinModel.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> inputs = image_processor(image, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 49, 7


SwinForMaskedImageModeling

class transformers.SwinForMaskedImageModeling  < source > ( config  )   Parameters   config (SwinConfig) — 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.    Swin Model with a decoder on top for masked image modeling, as proposed in SimMIM. Note that we provide a script to pre-train this model on custom data in our examples
directory. This model is a PyTorch torch.nn.Module sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( pixel_values: typing.Optional[torch.FloatTensor] = None bool_masked_pos: typing.Optional[torch.BoolTensor] = None head_mask: typing.Optional[torch.FloatTensor


SwinForMaskedImageModeling

] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.swin.modeling_swin.SwinMaskedImageModelingOutput or tuple(torch.FloatTensor)  Parameters   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.call()
for details.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.


SwinForMaskedImageModeling

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.   bool_masked_pos (torch.BoolTensor of shape (batch_size, num_patches)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).   Returns
transformers.models.swin.modeling_swin.SwinMaskedImageModelingOutput or tuple(torch.FloatTensor)

A transformers.models.swin.modeling_swin.SwinMaskedImageModelingOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (SwinConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when bool_masked_pos is provided) — Masked image mo


SwinForMaskedImageModeling

deling (MLM) loss.
reconstruction (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Reconstructed pixel values.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each stage) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each stage) 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
head


SwinForMaskedImageModeling

s.
reshaped_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each stage) of
shape (batch_size, hidden_size, height, width).
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
  The SwinForMaskedImageModeling forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Examples:   Copied >>> from transformers import AutoImageProcessor, SwinForMaskedImageModeling
>>> im


SwinForMaskedImageModeling

port torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-base-simmim-window6-192")
>>> model = SwinForMaskedImageModeling.from_pretrained("microsoft/swin-base-simmim-window6-192")
>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
>>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
>>> # create random boolean mask of shape (batch_size, num_patches)
>>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
>>> list(reconstructed_pixel_values.shape)
[1, 3, 192, 192


SwinForImageClassification

class transformers.SwinForImageClassification  < source > ( config  )   Parameters   config (SwinConfig) — 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.    Swin Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet. This model is a PyTorch torch.nn.Module sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( pixel_values: typing.Optional[torch.FloatTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None output_atten


SwinForImageClassification

tions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.swin.modeling_swin.SwinImageClassifierOutput or tuple(torch.FloatTensor)  Parameters   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.call()
for details.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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_hid


SwinForImageClassification

den_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).   Returns
transformers.models.swin.modeling_swin.SwinImageClassifierOutput or tuple(torch.FloatTensor)

A transformers.models.swin.modeling_swin.SwinImageClassifierOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elemen


SwinForImageClassification

ts depending on the configuration (SwinConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each stage) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=


SwinForImageClassification

True) — Tuple of torch.FloatTensor (one for each stage) 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.
reshaped_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each stage) of
shape (batch_size, hidden_size, height, width).
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
  The SwinForImageClassification 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
i


SwinForImageClassification

nstance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoImageProcessor, SwinForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> model = SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> inputs = image_processor(image, return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
tabby, tabby


TFSwinModel

class transformers.TFSwinModel  < source > ( *args **kwargs  )   Parameters   config (SwinConfig) — 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 Swin Model transformer outputting raw hidden-states without any specific head on top.
This model is a Tensorflow
tf.keras.layers.Layer sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.  call  < source > ( pixel_values: tf.Tensor | None = None bool_masked_pos: tf.Tensor | None = None head_mask: tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False  ) → transformers.models.swin.modeling_


TFSwinModel

tf_swin.TFSwinModelOutput or tuple(tf.Tensor)  Parameters   pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.call()
for details.   head_mask (tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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.   bool_masked_pos (tf.Tenso


TFSwinModel

r of shape (batch_size, num_patches), optional) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).   Returns
transformers.models.swin.modeling_tf_swin.TFSwinModelOutput or tuple(tf.Tensor)

A transformers.models.swin.modeling_tf_swin.TFSwinModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (SwinConfig) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor of shape (batch_size, hidden_size), optional, returned when add_pooling_layer=True is passed) — Average pooling of the last layer hidden-state.
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


TFSwinModel

 (one for the output of the embeddings + one for the output of each stage) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each stage) 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.
reshaped_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 stage) of shape
(batch_size, hidden_size, height, width).
Hidden-states of the model at the output of each layer plus the initial embedding output


TFSwinModel

s reshaped to
include the spatial dimensions.
  The TFSwinModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoImageProcessor, TFSwinModel
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> model = TFSwinModel.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> inputs = image_processor(image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 49,


TFSwinForMaskedImageModeling

class transformers.TFSwinForMaskedImageModeling  < source > ( *args **kwargs  )   Parameters   config (SwinConfig) — 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.    Swin Model with a decoder on top for masked image modeling, as proposed in SimMIM.
This model is a Tensorflow
tf.keras.layers.Layer sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.  call  < source > ( pixel_values: tf.Tensor | None = None bool_masked_pos: tf.Tensor | None = None head_mask: tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = N


TFSwinForMaskedImageModeling

one return_dict: Optional[bool] = None training: bool = False  ) → transformers.models.swin.modeling_tf_swin.TFSwinMaskedImageModelingOutput or tuple(tf.Tensor)  Parameters   pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.call()
for details.   head_mask (tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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 h


TFSwinForMaskedImageModeling

idden 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.   bool_masked_pos (tf.Tensor of shape (batch_size, num_patches)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).   Returns
transformers.models.swin.modeling_tf_swin.TFSwinMaskedImageModelingOutput or tuple(tf.Tensor)

A transformers.models.swin.modeling_tf_swin.TFSwinMaskedImageModelingOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (SwinConfig) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when bool_masked_pos is provided) — Masked image modeling (MLM) loss.
reconstruction (tf.Tensor of shape


TFSwinForMaskedImageModeling

(batch_size, num_channels, height, width)) — Reconstructed pixel values.
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 stage) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each stage) 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.
reshaped_hidden_states (tuple(tf.Tensor), optional, re


TFSwinForMaskedImageModeling

turned 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 stage) of shape
(batch_size, hidden_size, height, width).
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
  The TFSwinForMaskedImageModeling forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Examples:   Copied >>> from transformers import AutoImageProcessor, TFSwinForMaskedImageModeling
>>> import tensorflow as tf
>>> from P


TFSwinForMaskedImageModeling

IL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> model = TFSwinForMaskedImageModeling.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
>>> pixel_values = image_processor(images=image, return_tensors="tf").pixel_values
>>> # create random boolean mask of shape (batch_size, num_patches)
>>> bool_masked_pos = tf.random.uniform((1, num_patches)) >= 0.5
>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
>>> list(reconstructed_pixel_values.shape)
[1, 3, 224, 2


TFSwinForImageClassification

class transformers.TFSwinForImageClassification  < source > ( *args **kwargs  )   Parameters   config (SwinConfig) — 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.    Swin Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet. This model is a Tensorflow
tf.keras.layers.Layer sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.  call  < source > ( pixel_values: tf.Tensor | None = None head_mask: tf.Tensor | None = None labels: tf.Tensor | None = None output_attentions: Opt


TFSwinForImageClassification

ional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False  ) → transformers.models.swin.modeling_tf_swin.TFSwinImageClassifierOutput or tuple(tf.Tensor)  Parameters   pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.call()
for details.   head_mask (tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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_st


TFSwinForImageClassification

ates (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 (tf.Tensor of shape (batch_size,), optional) —
Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).   Returns
transformers.models.swin.modeling_tf_swin.TFSwinImageClassifierOutput or tuple(tf.Tensor)

A transformers.models.swin.modeling_tf_swin.TFSwinImageClassifierOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elem


TFSwinForImageClassification

ents depending on the
configuration (SwinConfig) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each stage) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of


TFSwinForImageClassification

tf.Tensor (one for each stage) 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.
reshaped_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 stage) of shape
(batch_size, hidden_size, height, width).
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
  The TFSwinForImageClassification 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


TFSwinForImageClassification

 instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoImageProcessor, TFSwinForImageClassification
>>> import tensorflow as tf
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> model = TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
>>> inputs = image_processor(image, return_tensors="tf")
>>> logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = int(tf.math.argmax(logits, axis=-1))
>>> print(model.config.id2label[predicted_label])
tabby, tabby


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


Overview

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. The authors’ code can be found here


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 as
facebook/blenderbot_small_90M, have a different architecture and consequently should be used with
BlenderbotSmall.


Usage

Here is an example of model usage:
  Copied >>> 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))
["<s> That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?</s>"]


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 TFBlenderbotM


BlenderbotConfig

odel.   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


BlenderbotConfig

 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 initia


BlenderbotConfig

lizing 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 to
eos_token_id.    This is the configuration class to store the configurati


BlenderbotConfig

on 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 Blenderbot
facebook/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:   Copied >>> from transformers import BlenderbotConfig, BlenderbotModel
>>> # Initializing a Blenderbot facebook/blenderbot-3B style configuration
>>> configuration = BlenderbotConfig()
>>> # Initializing a model (with random weights) from the facebook/blenderbot-3B style configuration
>>> model = BlenderbotModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config


BlenderbotTokenizer

class transformers.BlenderbotTokenizer  < source > ( vocab_file merges_file errors = 'replace' bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' add_prefix_space = False **kwargs  )   Parameters   vocab_file (str) —
Path to the vocabulary file.   merges_file (str) —
Path to the merges file.   errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.   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.


BlenderbotTokenizer

 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,


BlenderbotTokenizer

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


BlenderbotTokenizer

, 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:   Copied >>> 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 t


BlenderbotTokenizer

okenizer 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   Returns
List[int]

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 = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' 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. See
bytes.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


BlenderbotTokenizerFast

 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>") —
T


BlenderbotTokenizerFast

he 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.


BlenderbotTokenizerFast

    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:   Copied >>> 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


BlenderbotTokenizerFast

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   Returns
List[int]

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 </


BlenderbotModel

See transformers.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 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 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  < sou


BlenderbotModel

rce > ( 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 tupl


BlenderbotModel

e(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() and
PreTrainedTokenizer.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() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
Blenderbot


BlenderbotModel

 uses the bos_token_id as the starting token for decoder_input_ids generation. If
past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_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


BlenderbotModel

 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)


BlenderbotModel

) 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 all
decoder_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 (to


BlenderbotModel

rch.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 convert
decoder_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 (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool


BlenderbotModel

, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (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


BlenderbotModel

=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 initia


BlenderbotModel

l 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) — Sequen


BlenderbotModel

ce 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


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:   Copied >>> 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  # Batch size 1
>>> 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 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 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.  forwar


BlenderbotForConditionalGeneration

d  < 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  ) → transforme


BlenderbotForConditionalGeneration

rs.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() and
PreTrainedTokenizer.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() and
PreTrainedTokenizer.call() for details.
What a


BlenderbotForConditionalGeneration

re decoder input IDs?
Blenderbot uses the bos_token_id as the starting token for decoder_input_ids generation. If
past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_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


BlenderbotForConditionalGeneration

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


BlenderbotForConditionalGeneration

_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 all
decoder_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 (tor


BlenderbotForConditionalGeneration

ch.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 convert
decoder_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 (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional)


BlenderbotForConditionalGeneration

—
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].   Returns
transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BlenderbotConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optiona


BlenderbotForConditionalGeneration

l, 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 pas


BlenderbotForConditionalGeneration

sed 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, seque


BlenderbotForConditionalGeneration

nce_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_attent


BlenderbotForConditionalGeneration

ions=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 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:   Copied >>> 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 c


BlenderbotForConditionalGeneration

arbs."
>>> 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.</s> <s>That's unfortunate. "
...     "Are they trying to lose weight or are they just trying to be healthier?</s> "
...     "<s> 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 ha


BlenderbotForCausalLM

class transformers.BlenderbotForCausalLM  < source > ( config  )     forward  < source > ( input_ids: 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_leng


BlenderbotForCausalLM

th)) —
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() and
PreTrainedTokenizer.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


BlenderbotForCausalLM

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)) an


BlenderbotForCausalLM

d 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
(m


BlenderbotForCausalLM

asked), 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.   Returns
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of
torch.FloatTensor (if re


BlenderbotForCausalLM

turn_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.FloatTenso


BlenderbotForCausalLM

r), 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 ke


BlenderbotForCausalLM

y,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
   Example:   Copied >>> 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_shap


TFBlenderbotModel

class transformers.TFBlenderbotModel  < source > ( *args **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 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


TFBlenderbotModel

: 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


TFBlenderbotModel

_mask]) or model([input_ids, attention_mask, token_type_ids]) a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids}) Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!  call  < source > ( input_ids: 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 de


TFBlenderbotModel

coder_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() and
PreTrainedTokenizer.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_sequen


TFBlenderbotModel

ce_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
Blenderbot uses the bos_token_id as the starting token for decoder_input_ids generation. If
past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_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, enco


TFBlenderbotModel

der_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, sequenc


TFBlenderbotModel

e_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 all
decoder_input_ids of shape (batch_size, sequence_length).   use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_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, i