
 
MEGA
	
Overview
	
The MEGA model was proposed in Mega: Moving Average Equipped Gated Attention by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
MEGA proposes a new approach to self-attention with each encoder layer having a multi-headed exponential moving average in addition to a single head of standard dot-product attention, giving the attention mechanism
stronger positional biases. This allows MEGA to perform competitively to Transformers on standard benchmarks including LRA
while also having significantly fewer parameters. MEGA’s compute efficiency allows it to scale to very long sequences, making it an
attractive option for long-document NLP tasks.
The abstract from the paper is the following:
The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences. In this paper, we introduce Mega, a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential) moving average to incorporate inductive bias of position-aware local dependencies into the position-agnostic attention mechanism. We further propose a variant of Mega that offers linear time and space complexity yet yields only minimal quality loss, by efficiently splitting the whole sequence into multiple chunks with fixed length. Extensive experiments on a wide range of sequence modeling benchmarks, including the Long Range Arena, neural machine translation, auto-regressive language modeling, and image and speech classification, show that Mega achieves significant improvements over other sequence models, including variants of Transformers and recent state space models. 
Tips:
MEGA can perform quite well with relatively few parameters. See Appendix D in the MEGA paper for examples of architectural specs which perform well in various settings. If using MEGA as a decoder, be sure to set bidirectional=False to avoid errors with default bidirectional. 
Mega-chunk is a variant of mega that reduces time and spaces complexity from quadratic to linear. Utilize chunking with MegaConfig.use_chunking and control chunk size with MegaConfig.chunk_size 
This model was contributed by mnaylor.
The original code can be found here.
Implementation Notes:
The original implementation of MEGA had an inconsistent expectation of attention masks for padding and causal self-attention between the softmax attention and Laplace/squared ReLU method. This implementation addresses that inconsistency.
The original implementation did not include token type embeddings; this implementation adds support for these, with the option controlled by MegaConfig.add_token_type_embeddings
MegaConfig
	
class transformers.MegaConfig
<
source
>
(
vocab_size = 30522
hidden_size = 128
num_hidden_layers = 4
intermediate_size = 256
ema_projection_size = 16
bidirectional = True
shared_representation_size = 64
use_chunking = False
chunk_size = -1
truncation = None
normalize_before_mega = True
normalization_type = 'scalenorm'
norm_affine = True
activation = 'silu'
attention_activation = 'softmax'
dropout_prob = 0.1
hidden_dropout_prob = 0.1
attention_probs_dropout_prob = 0.1
use_feature_dropout = False
use_normalized_ffn = True
nffn_hidden_size = 256
normalize_before_ffn = True
nffn_activation_dropout_prob = 0.1
max_positions = 2048
add_token_type_embeddings = False
type_vocab_size = 2
initializer_range = 0.02
ema_delta_alpha_range = 0.2
ema_beta_range = 0.02
ema_gamma_omega_range = 1.0
pad_token_id = 1
bos_token_id = 0
eos_token_id = 2
relative_positional_bias = 'rotary'
classifier_dropout = None
use_cache = True
add_lm_hidden_dense_layer = True
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 30522) —
Vocabulary size of the Mega model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling MegaModel.
hidden_size (int, optional, defaults to 128) —
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (int, optional, defaults to 4) —
Number of hidden layers in the Mega encoder.
intermediate_size (int, optional, defaults to 256) —
Dimensionality of the hidden size (self-attention value projection) within the Mega encoder
ema_projection_size (int, optional, defaults to 16) —
Dimensionality of the MegaMultiDimensionDampedEma
bidirectional (bool, optional, defaults to True) —
Whether the MegaMultiDimensionDampedEma used in Mega’s self-attention should work bidirectionally (True)
or unidirectionally (False). Bidirectional EMA is incompatible with causal decoding, so this should be
False if you intend to use the model as a decoder.
shared_representation_size (int, optional, defaults to 64) —
Dimensionality of the linear projection for shared representation of self-attention queries and keys
use_chunking (bool, optional, defaults to False) —
Whether to chunk inputs for linear self-attention complexity (described as Mega-chunk in the paper)
chunk_size (int, optional, defaults to -1) —
If use_chunking is set to True, determines the size of the chunks to apply to the input sequence. If
chunking is used, input sequences must be padded to a multiple of chunk_size
truncation (int, optional) —
If specified, the sequence length for which to truncate MegaMultiDimensionDampedEma
normalize_before_mega (bool, optional, defaults to True) —
Whether to normalize before (True) or after (False) passing through Mega encoder blocks
normalization_type (str, optional, defaults to "scalenorm") —
Type of normalization to use in Mega encoder blocks. Choose one of "scalenorm", "layernorm",
"rmsnorm", "batchnorm", or "syncbatchnorm" (GPU required for syncbatchnorm)
norm_affine (bool, optional, defaults to True) —
If True, applies a parameterized affine transformation to inputs during normalization
activation (str, optional, defaults to "silu") —
Activation function to apply within Mega encoder blocks. Choose one of "silu", "relu", "linear",
"gelu", or "gelu_accurate"
attention_activation (str, optional, defaults to "softmax") —
Activation function to apply for single-headed self-attention (a la Transformer). Choose one of
"softmax", "laplace", or "relu2"
dropout_prob (float, optional, defaults to 0.1) —
The dropout probability for EMA self-attention
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, optional, defaults to 0.1) —
The dropout ratio for the attention probabilities.
use_feature_dropout (bool, optional, defaults to False) —
Whether to use feature-based (True) or standard dropout (False)
use_normalized_ffn (bool, optional, defaults to True) —
Whether to use the normalized feed-forward sub-layer in Mega blocks (True) or pass Mega encoder output
as-is (False)
nffn_hidden_size (int, optional, defaults to 256) —
If using the normalized feed-forward network (NFFN) layer within Mega (use_normalized_ffn = True), this
is the hidden size of the NFFN
normalize_before_ffn (bool, optional, defaults to True) —
Whether to normalize before (True) or after (False) the feed-forward portion of NFFN
nffn_activation_dropout_prob (float, optional, defaults to 0.1) —
The dropout ratio for the NFFN component.
max_positions (int, optional, defaults to 2048) —
The maximum sequence length to use for positional representations. For "simple" relative positional bias,
this is a hard limit on input length; "rotary" relative positional bias will extrapolate to longer
sequences
add_token_type_embeddings (bool, optional, defaults to True) —
Whether to account for token types in embeddings. Left as optional to maintain compatibility with original
implementation while adding support for token types.
type_vocab_size (int, optional, defaults to 2) —
The vocabulary size of the token_type_ids passed when calling MegaModel. Only used if
add_token_type_embeddings = True
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
ema_delta_alpha_range (float, optional, defaults to 0.2) —
The standard deviation for initializing the delta (damping factor) and alpha (decay factor) parameters in
MegaMultiDimensionDampedEma.
ema_beta_range (float, optional, defaults to 0.02) —
The standard deviation for initializing the beta parameter (expansion matrix) in
MegaMultiDimensionDampedEma.
ema_gamma_omega_range (float, optional, defaults to 1.0) —
The standard deviation for initializing the gamma (projection matrix) and omega (residual weight)
parameters in MultiDimensionEMA.
relative_positional_bias (str, optional, defaults to "rotary") —
Type of relative positional encoding. Choose one of "rotary" or "simple". If "simple" is selected,
max_positions is used as a limit on input size, while "rotary" extrapolates beyond max_positions.
is_decoder (bool, optional, defaults to False) —
Whether the model is used as a decoder or not. If False, the model is used as an encoder.
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if config.is_decoder=True.
classifier_dropout (float, optional) —
The dropout ratio for the classification head.
add_lm_hidden_dense_layer (bool, optional, defaults to True) —
Whether to include a hidden layer for projection between encoder outputs and LM heads (True) or pass
hidden states directly to LM head (False). Remains optional for compatibility with original
implementation
This is the configuration class to store the configuration of a MegaModel. It is used to instantiate a Mega
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 Mega
mnaylor/mega-base-wikitext architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Examples:
	Copied
 from transformers import MegaConfig, MegaModel
 # Initializing a Mega configuration
 configuration = MegaConfig()
 # Initializing a model (with random weights) from the configuration
 model = MegaModel(configuration)
 # Accessing the model configuration
 configuration = model.config
MegaModel
	
class transformers.MegaModel
<
source
>
(
config: MegaConfig
add_pooling_layer = True
)
Parameters 
config (MegaConfig) — 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 MEGA Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added after self-attention, following the architecture described in Mega: Moving Average
Equipped Gated Attention_ by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig,
Jonathan May, and Luke Zettlemoyer
To behave as a decoder the model needs to be initialized with the is_decoder argument of the configuration set to
True and bidirectional set to False. To be used in a Seq2Seq model, the model needs to initialized with both
is_decoder=True and bidirectional=False argument as well as add_cross_attention set to True; an
encoder_hidden_states is then expected as an input to the forward pass.
.. _Mega: Moving Average Equipped Gated Attention: https://arxiv.org/abs/2209.10655
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.Tensor] = None
past_key_values: typing.Optional[typing.List[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.BaseModelOutputWithPoolingAndCrossAttentions 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?
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.
This parameter can only be used when the model is initialized with add_token_type_embeddings parameter
set to True. All the value in this tensor should be always < config.type_vocab_size.
What are token type IDs?
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 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.
encoder_hidden_states  (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
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) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions 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 (MegaConfig) 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)) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if
config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values
input) to speed up sequential decoding.
The MegaModel 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, MegaModel
 import torch
 tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
 model = MegaModel.from_pretrained("mnaylor/mega-base-wikitext")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
MegaForCausalLM
	
class transformers.MegaForCausalLM
<
source
>
(
config: MegaConfig
)
Parameters 
config (MegaConfig) — 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.
MEGA Model with a language modeling head on top for CLM fine-tuning.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
encoder_hidden_states: typing.Optional[torch.FloatTensor] = None
encoder_attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
past_key_values: typing.Tuple[typing.Tuple[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.CausalLMOutputWithCrossAttentions 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?
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.
This parameter can only be used when the model is initialized with add_token_type_embeddings parameter
set to True. All the value in this tensor should be always < config.type_vocab_size.
What are token type IDs?
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 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.
encoder_hidden_states  (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
[-100, 0, ..., config.vocab_size] (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]
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
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) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
Returns
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (MegaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
The MegaForCausalLM 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, MegaForCausalLM, AutoConfig
 import torch
 tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
 config = AutoConfig.from_pretrained("mnaylor/mega-base-wikitext")
 config.is_decoder = True
 config.bidirectional = False
 model = MegaForCausalLM.from_pretrained(
...     "mnaylor/mega-base-wikitext", config=config, ignore_mismatched_sizes=True
... )
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs)
 prediction_logits = outputs.logits
MegaForMaskedLM
	
class transformers.MegaForMaskedLM
<
source
>
(
config: MegaConfig
)
Parameters 
config (MegaConfig) — 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.
MEGA Model with a language modeling head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
encoder_hidden_states: typing.Optional[torch.FloatTensor] = None
encoder_attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MaskedLMOutput 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?
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.
This parameter can only be used when the model is initialized with add_token_type_embeddings parameter
set to True. All the value in this tensor should be always < config.type_vocab_size.
What are token type IDs?
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 tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (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]
kwargs (Dict[str, any], optional, defaults to {}) —
Used to hide legacy arguments that have been deprecated.
Returns
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MaskedLMOutput 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 (MegaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked language modeling (MLM) 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).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The MegaForMaskedLM 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, MegaForMaskedLM
 import torch
 tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
 model = MegaForMaskedLM.from_pretrained("mnaylor/mega-base-wikitext")
 inputs = tokenizer("The capital of France is <mask>.", return_tensors="pt")
 with torch.no_grad():
...     logits = model(**inputs).logits
 # retrieve index of <mask>
 mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
 predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
 tokenizer.decode(predicted_token_id)
' Paris'
 labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
 # mask labels of non-<mask> tokens
 labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
 outputs = model(**inputs, labels=labels)
 round(outputs.loss.item(), 2)
0.1
MegaForSequenceClassification
	
class transformers.MegaForSequenceClassification
<
source
>
(
config
)
Parameters 
config (MegaConfig) — 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.
MEGA Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.SequenceClassifierOutput 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?
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.
This parameter can only be used when the model is initialized with add_token_type_embeddings parameter
set to True. All the value in this tensor should be always < config.type_vocab_size.
What are token type IDs?
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 tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SequenceClassifierOutput 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 (MegaConfig) 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, 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 MegaForSequenceClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of single-label classification:
	Copied
 import torch
 from transformers import AutoTokenizer, MegaForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
 model = MegaForSequenceClassification.from_pretrained("mnaylor/mega-base-wikitext")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_class_id = logits.argmax().item()
 # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
 num_labels = len(model.config.id2label)
 model = MegaForSequenceClassification.from_pretrained("mnaylor/mega-base-wikitext", num_labels=num_labels)
 labels = torch.tensor([1])
 loss = model(**inputs, labels=labels).loss
Example of multi-label classification:
	Copied
 import torch
 from transformers import AutoTokenizer, MegaForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
 model = MegaForSequenceClassification.from_pretrained("mnaylor/mega-base-wikitext", problem_type="multi_label_classification")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
 # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
 num_labels = len(model.config.id2label)
 model = MegaForSequenceClassification.from_pretrained(
...     "mnaylor/mega-base-wikitext", num_labels=num_labels, problem_type="multi_label_classification"
... )
 labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
 loss = model(**inputs, labels=labels).loss
MegaForMultipleChoice
	
class transformers.MegaForMultipleChoice
<
source
>
(
config
)
Parameters 
config (MegaConfig) — 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.
MEGA Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, num_choices, 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 (torch.FloatTensor of shape (batch_size, num_choices, 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, num_choices, 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.
This parameter can only be used when the model is initialized with add_token_type_embeddings parameter
set to True. All the value in this tensor should be always < config.type_vocab_size.
What are token type IDs?
inputs_embeds (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices-1] where num_choices is the size of the second dimension of the input tensors. (See
input_ids above)
Returns
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MultipleChoiceModelOutput 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 (MegaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.
logits (torch.FloatTensor of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The MegaForMultipleChoice 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, MegaForMultipleChoice
 import torch
 tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
 model = MegaForMultipleChoice.from_pretrained("mnaylor/mega-base-wikitext")
 prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
 choice0 = "It is eaten with a fork and a knife."
 choice1 = "It is eaten while held in the hand."
 labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1
 encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
 outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1
 # the linear classifier still needs to be trained
 loss = outputs.loss
 logits = outputs.logits
MegaForTokenClassification
	
class transformers.MegaForTokenClassification
<
source
>
(
config
)
Parameters 
config (MegaConfig) — 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.
MEGA Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.TokenClassifierOutput 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?
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.
This parameter can only be used when the model is initialized with add_token_type_embeddings parameter
set to True. All the value in this tensor should be always < config.type_vocab_size.
What are token type IDs?
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 tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].
Returns
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.TokenClassifierOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (MegaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided)  — Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The MegaForTokenClassification 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, MegaForTokenClassification
 import torch
 tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
 model = MegaForTokenClassification.from_pretrained("mnaylor/mega-base-wikitext")
 inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )
 with torch.no_grad():
...     logits = model(**inputs).logits
 predicted_token_class_ids = logits.argmax(-1)
 # Note that tokens are classified rather then input words which means that
 # there might be more predicted token classes than words.
 # Multiple token classes might account for the same word
 predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
 labels = predicted_token_class_ids
 loss = model(**inputs, labels=labels).loss
MegaForQuestionAnswering
	
class transformers.MegaForQuestionAnswering
<
source
>
(
config
)
Parameters 
config (MegaConfig) — 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.
MEGA Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
start_positions: typing.Optional[torch.LongTensor] = None
end_positions: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, 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 (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.
This parameter can only be used when the model is initialized with add_token_type_embeddings parameter
set to True. All the value in this tensor should be always < config.type_vocab_size.
What are token type IDs?
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 tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
start_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (MegaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The MegaForQuestionAnswering 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, MegaForQuestionAnswering
 import torch
 tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
 model = MegaForQuestionAnswering.from_pretrained("mnaylor/mega-base-wikitext")
 question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
 inputs = tokenizer(question, text, return_tensors="pt")
 with torch.no_grad():
...     outputs = model(**inputs)
 answer_start_index = outputs.start_logits.argmax()
 answer_end_index = outputs.end_logits.argmax()
 predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
 # target is "nice puppet"
 target_start_index = torch.tensor([14])
 target_end_index = torch.tensor([15])
 outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
 loss = outputs.loss
←MBart and MBart-50
MegatronBERT→
MEGA
Overview
MegaConfig
MegaModel
MegaForCausalLM
MegaForMaskedLM
MegaForSequenceClassification
MegaForMultipleChoice
MegaForTokenClassification
MegaForQuestionAnswering
