Gemma2 모델은 Google의 Gemma2 팀이 작성한 Gemma2: Open Models Based on Gemini Technology and Research에서 제안되었습니다. 파라미터 크기가 각각 90억(9B)과 270억(27B)인 두 가지 Gemma2 모델이 출시되었습니다.
블로그 게시물의 초록은 다음과 같습니다:
이제 우리는 전 세계의 연구자와 개발자들에게 Gemma 2를 공식적으로 출시합니다. 90억(9B)과 270억(27B) 파라미터 크기로 제공되는 Gemma 2는 1세대보다 더 높은 성능과 추론 효율성을 제공하며, 상당한 안전성 향상을 포함하고 있습니다. 사실 270억 규모의 모델은 크기가 두 배 이상인 모델과 비교해도 경쟁력 있는 대안을 제공하며, 이는 작년 12월까지만 해도 독점 모델에서만 가능했던 성능을 제공합니다.
팁:
src/transformers/models/Gemma2/convert_Gemma2_weights_to_hf.py
를 사용하여 변환할 수 있습니다.past_key_values
로 forward 호출에 전달해야 합니다. 또한 past_key_values
에 이미 이전의 키와 값이 포함되어 있다면 cache_position
도 준비해야 합니다.이 모델은 Arthur Zucker, Pedro Cuenca, Tom Arsen이 기여했습니다.
( vocab_size = 256000 hidden_size = 2304 intermediate_size = 9216 num_hidden_layers = 26 num_attention_heads = 8 num_key_value_heads = 4 head_dim = 256 hidden_activation = 'gelu_pytorch_tanh' max_position_embeddings = 8192 initializer_range = 0.02 rms_norm_eps = 1e-06 use_cache = True pad_token_id = 0 eos_token_id = 1 bos_token_id = 2 tie_word_embeddings = True rope_theta = 10000.0 attention_bias = False attention_dropout = 0.0 query_pre_attn_scalar = 256 sliding_window = 4096 final_logit_softcapping = 30.0 attn_logit_softcapping = 50.0 cache_implementation = 'hybrid' **kwargs )
Parameters
int
, optional, defaults to 256000) —
Vocabulary size of the Gemma2 model. Defines the number of different tokens that can be represented by the
inputs_ids
passed when calling Gemma2Model int
, optional, defaults to 2304) —
Dimension of the hidden representations. int
, optional, defaults to 9216) —
Dimension of the MLP representations. int
, optional, defaults to 26) —
Number of hidden layers in the Transformer decoder. int
, optional, defaults to 8) —
Number of attention heads for each attention layer in the Transformer decoder. int
, optional, defaults to 4) —
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
num_key_value_heads=num_attention_heads
, the model will use Multi Head Attention (MHA), if
num_key_value_heads=1
the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout this
paper. If it is not specified, will default to
num_attention_heads
. int
, optional, defaults to 256) —
The attention head dimension. str
or function
, optional, defaults to "gelu_pytorch_tanh"
) —
The non-linear activation function (function or string) in the decoder. Will default to "gelu_pytorch_tanh"
if not specified. "gelu_pytorch_tanh"
uses an approximation of the "gelu"
activation function. int
, optional, defaults to 8192) —
The maximum sequence length that this model might ever be used with. float
, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. float
, optional, defaults to 1e-06) —
The epsilon used by the rms normalization layers. 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
. int
, optional, defaults to 0) —
Padding token id. int
, optional, defaults to 1) —
End of stream token id. int
, optional, defaults to 2) —
Beginning of stream token id. bool
, optional, defaults to True
) —
Whether to tie weight embeddings float
, optional, defaults to 10000.0) —
The base period of the RoPE embeddings. bool
, defaults to False
, optional, defaults to False
) —
Whether to use a bias in the query, key, value and output projection layers during self-attention. float
, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities. float
, optional, defaults to 256) — scaling factor used on the attention scores int
, optional, defaults to 4096) — in Gemma2, every other layer uses sliding window attention. This is the
size of the sliding window. float
, optional, defaults to 30.0) — scaling factor when applying tanh softcapping on the logits. float
, optional, defaults to 50.0) — scaling factor when applying tanh softcapping on the attention scores. str
, optional, defaults to "hybrid"
) — the cache type to be used with generate
. This is the configuration class to store the configuration of a Gemma2Model. It is used to instantiate an Gemma2 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 Gemma2-7B. e.g. google/gemma2-7b Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
>>> from transformers import Gemma2Model, Gemma2Config
>>> # Initializing a Gemma2 gemma2-7b style configuration
>>> configuration = Gemma2Config()
>>> # Initializing a model from the gemma2-7b style configuration
>>> model = Gemma2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
( config: Gemma2Config )
Parameters
The bare Gemma2 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.
Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a Gemma2DecoderLayer
( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[transformers.cache_utils.HybridCache] = None 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 cache_position: typing.Optional[torch.LongTensor] = None )
Parameters
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.
torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
If past_key_values
is used, optionally only the last input_ids
have to be input (see
past_key_values
).
If you want to change padding behavior, you should read modeling_opt._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the paper for more
information on the default strategy.
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.n_positions - 1]
.
Cache
or tuple(tuple(torch.FloatTensor))
, optional) —
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values
returned by the model at a previous stage of decoding, when use_cache=True
or config.use_cache=True
.
Two formats are allowed:
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)
). This is also known as the legacy
cache format.The model will output the same cache format that is fed as input. If no past_key_values
are passed, the
legacy cache format will be returned.
If past_key_values
are used, the user can optionally input only the last input_ids
(those that don’t
have their past key value states given to this model) of shape (batch_size, 1)
instead of all input_ids
of shape (batch_size, sequence_length)
.
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. 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
). bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor
of shape (sequence_length)
, optional) —
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids
,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length. The Gemma2Model 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.
( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[transformers.cache_utils.HybridCache] = 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 cache_position: typing.Optional[torch.LongTensor] = None num_logits_to_keep: int = 0 **loss_kwargs ) → transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
Parameters
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.
torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
If past_key_values
is used, optionally only the last input_ids
have to be input (see
past_key_values
).
If you want to change padding behavior, you should read modeling_opt._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the paper for more
information on the default strategy.
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.n_positions - 1]
.
Cache
or tuple(tuple(torch.FloatTensor))
, optional) —
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values
returned by the model at a previous stage of decoding, when use_cache=True
or config.use_cache=True
.
Two formats are allowed:
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)
). This is also known as the legacy
cache format.The model will output the same cache format that is fed as input. If no past_key_values
are passed, the
legacy cache format will be returned.
If past_key_values
are used, the user can optionally input only the last input_ids
(those that don’t
have their past key value states given to this model) of shape (batch_size, 1)
instead of all input_ids
of shape (batch_size, sequence_length)
.
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. 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
). bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor
of shape (sequence_length)
, optional) —
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids
,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length. 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]
.
num_logits_to_keep (int
, optional):
Calculate logits for the last num_logits_to_keep
tokens. If 0
, calculate logits for all
input_ids
(special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
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 (Gemma2Config) 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 Gemma2ForCausalLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, GemmaForCausalLM
>>> model = GemmaForCausalLM.from_pretrained("google/gemma-2-9b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
>>> prompt = "What is your favorite condiment?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"What is your favorite condiment?"
( config )
Parameters
The Gemma2 Model transformer with a sequence classification head on top (linear layer).
Gemma2ForSequenceClassification uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
pad_token_id
is defined in the configuration, it finds the last token that is not a padding token in each row. If
no pad_token_id
is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when inputs_embeds
are passed instead of input_ids
, it does the same (take the last value in
each row of the batch).
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.
( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Union[transformers.cache_utils.Cache, typing.List[torch.FloatTensor], NoneType] = 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 )
Parameters
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.
torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
If past_key_values
is used, optionally only the last input_ids
have to be input (see
past_key_values
).
If you want to change padding behavior, you should read modeling_opt._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the paper for more
information on the default strategy.
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.n_positions - 1]
.
Cache
or tuple(tuple(torch.FloatTensor))
, optional) —
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values
returned by the model at a previous stage of decoding, when use_cache=True
or config.use_cache=True
.
Two formats are allowed:
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)
). This is also known as the legacy
cache format.The model will output the same cache format that is fed as input. If no past_key_values
are passed, the
legacy cache format will be returned.
If past_key_values
are used, the user can optionally input only the last input_ids
(those that don’t
have their past key value states given to this model) of shape (batch_size, 1)
instead of all input_ids
of shape (batch_size, sequence_length)
.
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. 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
). bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor
of shape (sequence_length)
, optional) —
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids
,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length. 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). The Gemma2ForSequenceClassification 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.
( config )
Parameters
The Gemma2 Model transformer 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.
( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = 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.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters
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.
torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
If past_key_values
is used, optionally only the last input_ids
have to be input (see
past_key_values
).
If you want to change padding behavior, you should read modeling_opt._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the paper for more
information on the default strategy.
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.n_positions - 1]
.
Cache
or tuple(tuple(torch.FloatTensor))
, optional) —
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values
returned by the model at a previous stage of decoding, when use_cache=True
or config.use_cache=True
.
Two formats are allowed:
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)
). This is also known as the legacy
cache format.The model will output the same cache format that is fed as input. If no past_key_values
are passed, the
legacy cache format will be returned.
If past_key_values
are used, the user can optionally input only the last input_ids
(those that don’t
have their past key value states given to this model) of shape (batch_size, 1)
instead of all input_ids
of shape (batch_size, sequence_length)
.
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. 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
). bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor
of shape (sequence_length)
, optional) —
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids
,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length. 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.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 (Gemma2Config) 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 Gemma2ForTokenClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, Gemma2ForTokenClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma2-7b")
>>> model = Gemma2ForTokenClassification.from_pretrained("google/gemma2-7b")
>>> 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