
 
GPT-NeoX-Japanese
	
Overview
	
We introduce GPT-NeoX-Japanese, which is an autoregressive language model for Japanese, trained on top of https://github.com/EleutherAI/gpt-neox.
Japanese is a unique language with its large vocabulary and a combination of hiragana, katakana, and kanji writing scripts.
To address this distinct structure of the Japanese language, we use a special sub-word tokenizer. We are very grateful to tanreinama for open-sourcing this incredibly helpful tokenizer.
Following the recommendations from Google’s research on PaLM, we have removed bias parameters from transformer blocks, achieving better model performance. Please refer this article in detail.
Development of the model was led by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori from ABEJA, Inc.. For more information on this model-building activity, please refer here (ja).
Generation
	
The generate() method can be used to generate text using GPT NeoX Japanese model.
	Copied
 from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseTokenizer
 model = GPTNeoXJapaneseForCausalLM.from_pretrained("abeja/gpt-neox-japanese-2.7b")
 tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
 prompt = "人とAIが協調するためには、"
 input_ids = tokenizer(prompt, return_tensors="pt").input_ids
 gen_tokens = model.generate(
...     input_ids,
...     do_sample=True,
...     temperature=0.9,
...     max_length=100,
... )
 gen_text = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)[0]
 print(gen_text)
人とAIが協調するためには、AIと人が共存し、AIを正しく理解する必要があります。
Documentation resources
	
Causal language modeling task guide
GPTNeoXJapaneseConfig
	
class transformers.GPTNeoXJapaneseConfig
<
source
>
(
vocab_size = 32000
hidden_size = 2560
num_hidden_layers = 32
num_attention_heads = 32
intermediate_multiple_size = 4
hidden_act = 'gelu'
rotary_pct = 1.0
rotary_emb_base = 10000
max_position_embeddings = 2048
initializer_range = 0.02
layer_norm_eps = 1e-05
use_cache = True
bos_token_id = 31996
eos_token_id = 31999
attention_dropout = 0.1
hidden_dropout = 0.0
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 32000) —
Vocabulary size of the GPTNeoXJapanese model. Defines the number of different tokens that can be
represented by the inputs_ids passed when calling GPTNeoXJapanese.
hidden_size (int, optional, defaults to 2560) —
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (int, optional, defaults to 32) —
Number of hidden layers in the Transformer encoder.
num_attention_heads (int, optional, defaults to 32) —
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_multiple_size (int, optional, defaults to 4) —
Dimension of the “intermediate” layer in the Transformer encoder is calculated by hidden_size *
intermediate_multiple_size.
hidden_act (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler.
rotary_pct (float, optional, defaults to 1.00) —
percentage of hidden dimensions to allocate to rotary embeddings
rotary_emb_base (int, optional, defaults to 10000) —
base for computing rotary embeddings frequency
max_position_embeddings (int, optional, defaults to 2048) —
The maximum sequence length that this model might ever be used with.
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-5) —
The epsilon used by the layer normalization layers.
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.
attention_dropout (float, optional, defaults to 0.1) —
The dropout ratio for the attention.
hidden_dropout (float, optional, defaults to 0.0) —
The dropout ratio for the hidden layer.
Example —
This is the configuration class to store the configuration of a GPTNeoXModelJapanese. It is used to instantiate
a GPTNeoX 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 GPTNeoXJapanese
abeja/gpt-neox-japanese-2.7b architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information. Default configs is set as 2.7B model
	Copied
 from transformers import GPTNeoXJapaneseConfig, GPTNeoXJapaneseModel
 # Initializing a GPTNeoXJapanese gpt-neox-japanese-2.7b style configuration
 configuration = GPTNeoXJapaneseConfig()
 # Initializing a model (with random weights) from the gpt-neox-japanese-2.7b style configuration
 model = GPTNeoXJapaneseModel(configuration)
 # Accessing the model configuration
 configuration = model.config
GPTNeoXJapaneseTokenizer
	
class transformers.GPTNeoXJapaneseTokenizer
<
source
>
(
vocab_file
emoji_file
unk_token = '<|endoftext|>'
pad_token = '<|endoftext|>'
bos_token = '<|startoftext|>'
eos_token = '<|endoftext|>'
do_clean_text = False
**kwargs
)
Parameters 
vocab_file (str) —
File containing the vocabulary.
emoji_file (str) —
File containing the emoji.
unk_token (str, optional, defaults to "<|endoftext|>") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (str, optional, defaults to "<|endoftext|>") —
The token used for padding
bos_token (str, optional, defaults to "<|startoftext|>") —
The beginning of sequence token.
eos_token (str, optional, defaults to "<|endoftext|>") —
The end of sequence token.
do_clean_text (bool, optional, defaults to False) —
Whether or not to clean text for URL, EMAIL, TEL, Japanese DATE and Japanese PRICE.
This tokenizer inherits from PreTrainedTokenizer and is based on Japanese special Sub-Word-Encoding that is
used in this repository (https://github.com/tanreinama/Japanese-BPEEncoder_V2). Check the repository for details.
Japanese has a relatively large vocabulary and there is no separation between words. Furthermore, the language is a
combination of hiragana, katakana, and kanji, and variants such as “1” and “①” are often used. In order to cope
with these, this tokenizer has the following features
Subword-by-subword segmentation, which is intermediate between byte strings and morphological analysis.
BPEs are created for each Kanji, Hiragana, and Katakana character, and there are no BPEs that cross character
types, such as Kanji + Hiragana or Hiragana + Katakana.
All-byte encoding that does not require <unk>.
Independent of UTF codes such as 2-byte and 3-byte characters
Conversion of heterographs to the same token_id
Emoji and Emoticon are grouped into 12 types as special tags.
Example:
	Copied
 from transformers import GPTNeoXJapaneseTokenizer
 tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
 # You can confirm both 慶応 and 慶應 are encoded to 17749
 tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"]
[30014, 26883, 26638, 27228, 25, 26650, 31732, 31679, 27809, 26638, 17749, 31592, 17749, 31593, 321, 1281]
 # Both 慶応 and 慶應 are decoded to 慶応
 tokenizer.decode(tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"])
'吾輩は猫である🐯。実は慶応(慶応)大学出身'
convert_tokens_to_string
<
source
>
(
tokens
)
Converts a sequence of tokens (string) in a single string.
GPTNeoXJapaneseModel
	
class transformers.GPTNeoXJapaneseModel
<
source
>
(
config
)
Parameters 
config (~GPTNeoXJapaneseConfig) — 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 GPTNeoXJapanese 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
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
past_key_values: typing.Optional[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.BaseModelOutputWithPast 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.
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.
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.
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].
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 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.
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.BaseModelOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPast 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 (GPTNeoXJapaneseConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if
config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values
input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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 GPTNeoXJapaneseModel 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, GPTNeoXJapaneseModel
 import torch
 tokenizer = AutoTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
 model = GPTNeoXJapaneseModel.from_pretrained("abeja/gpt-neox-japanese-2.7b")
 inputs = tokenizer("日本語のGPT-neoxがHugging Faceで使えます😀", return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
GPTNeoXJapaneseForCausalLM
	
class transformers.GPTNeoXJapaneseForCausalLM
<
source
>
(
config
)
Parameters 
config (~GPTNeoXJapaneseConfig) — 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.
GPTNeoXJapanese Model with a language modeling head on top for Classifier Model fine-tuning.
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
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[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.CausalLMOutputWithPast 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.
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.
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.
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].
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 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.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head). The two additional tensors are
only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks 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 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 n [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).
Returns
transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithPast 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 (GPTNeoXJapaneseConfig) 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).
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))
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, 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 GPTNeoXJapaneseForCausalLM 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, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseConfig
 import torch
 tokenizer = AutoTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
 config = GPTNeoXJapaneseConfig.from_pretrained("abeja/gpt-neox-japanese-2.7b")
 config.is_decoder = True
 model = GPTNeoXJapaneseForCausalLM.from_pretrained("abeja/gpt-neox-japanese-2.7b", config=config)
 inputs = tokenizer("日本語のGPT-neoxがHugging Faceで使えます😀", return_tensors="pt")
 outputs = model(**inputs)
 prediction_logits = outputs.logits
←GPT NeoX
GPT-J→
GPT-NeoX-Japanese
Overview
Generation
Documentation resources
GPTNeoXJapaneseConfig
GPTNeoXJapaneseTokenizer
GPTNeoXJapaneseModel
GPTNeoXJapaneseForCausalLM
