modeling#
- class CTRLPreTrainedModel(*args, **kwargs)[源代码]#
-
An abstract class for pretrained CTRL models. It provides CTRL related
model_config_file
,resource_files_names
,pretrained_resource_files_map
,pretrained_init_configuration
,base_model_prefix
for downloading and loading pretrained models. SeePretrainedModel
for more details.- config_class#
CTRLConfig
的别名
- class CTRLModel(config: CTRLConfig)[源代码]#
-
The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from
PretrainedModel
. Refer to the superclass documentation for the generic methods.This model is also a Paddle paddle.nn.Layer subclass. Use it as a regular Paddle Layer and refer to the Paddle documentation for all matter related to general usage and behavior.
- 参数:
config (
CTRLConfig
) --An instance of
CTRLConfig
.备注
A normal_initializer initializes weight matrices as normal distributions. See
CTRLPreTrainedModel._init_weights()
for how weights are initialized inCTRLModel
.
- set_input_embeddings(new_embeddings)[源代码]#
set new input embedding for model
- 参数:
value (Embedding) -- the new embedding of model
- 抛出:
NotImplementedError -- Model has not implement
set_input_embeddings
method
- forward(input_ids=None, cache=None, attention_mask=None, token_type_ids=None, position_ids=None, use_cache=False, output_attentions=False, output_hidden_states=False)[源代码]#
The CTRLModel forward method, overrides the
__call__()
special method.- 参数:
input_ids (Tensor) -- Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. Its data type should be
int64
and it has a shape of [batch_size, sequence_length].cache (Tuple[Tuple[Tensor]], optional) -- Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model. Can be used to speed up sequential decoding. The
input_ids
which have their past given to this model should not be passed as input ids as they have already been computed. Defaults toNone
.attention_mask (Tensor, optional) -- Mask used in multi-head attention to avoid performing attention on to some unwanted positions, usually the paddings or the subsequent positions. Its data type can be int, float and bool. When the data type is bool, the
masked
tokens haveFalse
values and the others haveTrue
values. When the data type is int, themasked
tokens have0
values and the others have1
values. When the data type is float, themasked
tokens have0.0
values and the others have1.0
values. It is a tensor with shape broadcasted to[batch_size, num_attention_heads, sequence_length, sequence_length]
. Defaults toNone
, which means nothing needed to be prevented attention to.token_type_ids (Tensor, optional) --
Segment token indices to indicate different portions of the inputs. Selected in the range
[0, type_vocab_size - 1]
. Iftype_vocab_size
is 2, which means the inputs have two portions. Indices can either be 0 or 1:0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
Its data type should be
int64
and it has a shape of [batch_size, sequence_length]. Defaults toNone
, which means we don't add segment embeddings.position_ids (Tensor, optional) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, max_position_embeddings - 1]
. Shape as [batch_size, num_tokens] and dtype as int64. Defaults toNone
.use_cache (bool, optional) -- Whether or not to use cache. Defaults to
False
. If set toTrue
, key value states will be returned and can be used to speed up decoding.output_attentions (bool, optional) -- Whether or not to return the attentions tensors of all attention layers. Defaults to
False
.output_hidden_states (bool, optional) -- Whether or not to return the output of all hidden layers. Defaults to
False
.
- 返回:
Returns tuple (
last_hidden_state
,caches
,hidden_states
,attentions
)With the fields:
last_hidden_state
(Tensor):Sequence of hidden-states at the last layer of the model. It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
caches
(tuple(tuple(Tensor), optional):returned when
use_cache=True
is passed. Tuple oftuple(Tensor)
of lengthnum_hidden_layers
, with each tuple having 2 tensors of shape [batch_size, num_heads, sequence_length, embed_size_per_head] and float32 dtype.
hidden_states
(tuple(Tensor), optional):returned when
output_hidden_states=True
is passed. Tuple ofTensor
(one for the output of the embeddings + one for the output of each layer). Each Tensor has a data type of float32 and its shape is [batch_size, sequence_length, hidden_size].
attentions
(tuple(Tensor), optional):returned when
output_attentions=True
is passed. Tuple ofTensor
(one for each layer) of shape. Each Tensor has a data type of float32 and its shape is [batch_size, num_heads, sequence_length, sequence_length].
- 返回类型:
tuple
示例
import paddle from paddlenlp.transformers import CTRLModel, CTRLTokenizer tokenizer = CTRLTokenizer.from_pretrained('ctrl') model = CTRLModel.from_pretrained('ctrl') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs)
- class CTRLLMHeadModel(config: CTRLConfig)[源代码]#
-
The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).
- 参数:
config (
CTRLConfig
) -- An instance ofCTRLConfig
.
- get_output_embeddings()[源代码]#
To be overwrited for models with output embeddings
- 返回:
the otuput embedding of model
- 返回类型:
Optional[Embedding]
- forward(input_ids=None, cache=None, attention_mask=None, token_type_ids=None, position_ids=None, labels=None, use_cache=False, output_attentions=False, output_hidden_states=False)[源代码]#
- 参数:
input_ids (Tensor) -- See
CTRLModel
.cache (Tensor, optional) -- See
CTRLModel
.attention_mask (Tensor, optional) -- See
CTRLModel
.token_type_ids (Tensor, optional) -- See
CTRLModel
.position_ids (Tensor, optional) -- See
CTRLModel
.labels (Tensor, optional) -- Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
labels = input_ids
Indices are selected in[-100, 0, ..., vocab_size]
All labels set to-100
are ignored (masked), the loss is only computed for labels in[0, ..., vocab_size]
. Shape is [batch_size, sequence_length] and dtype is int64.use_cache (bool, optional) -- See
CTRLModel
.output_attentions (bool, optional) -- See
CTRLModel
.output_hidden_states (bool, optional) -- See
CTRLModel
.
- 返回:
Returns tuple
(loss, logits, caches, hidden_states, attentions)
. With the fields:loss
(Tensor):returned when
labels
is provided. Language modeling loss (for next-token prediction). It's data type should be float32 and its shape is [1,].
logits
(Tensor):Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). It's data type should be float32 and its shape is [batch_size, sequence_length, vocab_size].
caches
(tuple(tuple(Tensor), optional):See
CTRLModel
.
hidden_states
(tuple(Tensor), optional):See
CTRLModel
.
attentions
(tuple(Tensor), optional):See
CTRLModel
.
- 返回类型:
tuple
示例
import paddle from paddlenlp.transformers import CTRLLMHeadModel, CTRLTokenizer tokenizer = CTRLTokenizer.from_pretrained('ctrl') model = CTRLLMHeadModel.from_pretrained('ctrl') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs, labels=inputs["input_ids"]) loss = output[0] logits = output[1]
- class CTRLForSequenceClassification(config: CTRLConfig)[源代码]#
-
The CTRL Model transformer with a sequence classification head on top (linear layer).
CTRLForSequenceClassification
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 apad_token_id
is defined in the configuration, it finds the last token that is not a padding token in each row. If nopad_token_id
is defined, it simply takes the last value in each row of the batch.- 参数:
config (
CTRLConfig
) -- An instance ofCTRLConfig
.
- forward(input_ids=None, cache=None, attention_mask=None, token_type_ids=None, position_ids=None, labels=None, use_cache=False, output_attentions=False, output_hidden_states=False)[源代码]#
- 参数:
input_ids (Tensor) -- See
CTRLModel
.cache (Tensor, optional) -- See
CTRLModel
.attention_mask (Tensor, optional) -- See
CTRLModel
.token_type_ids (Tensor, optional) -- See
CTRLModel
.position_ids (Tensor, optional) -- See
CTRLModel
.labels (Tensor, optional) -- Labels for computing the sequence classification/regression loss. Indices should be in
[0, ...,num_classes - 1]
. Ifnum_classes == 1
a regression loss is computed (Mean-Square loss), Ifnum_classes > 1
a classification loss is computed (Cross-Entropy). Shape is [batch_size,] and dtype is int64.use_cache (bool, optional) -- See
CTRLModel
.output_attentions (bool, optional) -- See
CTRLModel
.output_hidden_states (bool, optional) -- See
CTRLModel
.
- 返回:
Returns tuple
(loss, logits, caches, hidden_states, attentions)
. With the fields:loss
(Tensor):returned when
labels
is provided. Language modeling loss (for next-token prediction). It's data type should be float32 and its shape is [1,].
logits
(Tensor):Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). It's data type should be float32 and its shape is [batch_size, num_classes].
caches
(tuple(tuple(Tensor), optional):See
CTRLModel
.
hidden_states
(tuple(Tensor), optional):See
CTRLModel
.
attentions
(tuple(Tensor), optional):See
CTRLModel
.
- 返回类型:
tuple
示例
import paddle from paddlenlp.transformers import CTRLForSequenceClassification, CTRLTokenizer tokenizer = CTRLTokenizer.from_pretrained('ctrl') model = CTRLForSequenceClassification.from_pretrained('ctrl', pad_token_id=0) inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs, labels=paddle.to_tensor([1])) loss = output[0] logits = output[1]
- class SinusoidalPositionalEmbedding(num_embeddings, embedding_dim)[源代码]#
基类:
Embedding
This module produces sinusoidal positional embeddings of any length.
- CTRLForCausalLM#
CTRLLMHeadModel
的别名