modeling#
- class XLMModel(config: XLMConfig)[source]#
Bases:
XLMPretrainedModel
The bare XLM Model transformer outputting raw hidden-states.
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.
- Parameters:
config (
XLMConfig
) – An instance ofXLMConfig
.
- forward(input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None, cache=None, output_attentions=False, output_hidden_states=False)[source]#
The XLMModel forward method, overrides the
__call__()
special method.- Parameters:
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].langs (Tensor, optional) – A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are languages ids which can be obtained from the language names by using two conversion mappings provided in the configuration of the model (only provided for multilingual models). More precisely, the language name to language id mapping is in
model.config['lang2id']
(which is a dictionary string to int). Shape as [batch_size, sequence_length] and dtype as int64. 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.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, sequence_length] and dtype as int64. Defaults toNone
.lengths (Tensor, optional) – Length of each sentence that can be used to avoid performing attention on padding token indices. You can also use attention_mask for the same result (see above), kept here for compatibility. Indices selected in
[0, ..., sequence_length]
. Shape as [batch_size] and dtype as int64. Defaults toNone
.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
.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
.
- Returns:
Returns tuple (
last_hidden_state
,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].
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].
- Return type:
tuple
Example
import paddle from paddlenlp.transformers import XLMModel, XLMTokenizer tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024") model = XLMModel.from_pretrained("xlm-mlm-tlm-xnli15-1024") inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"] last_hidden_state = model(**inputs)[0]
- get_input_embeddings()[source]#
get input embedding of model
- Returns:
embedding of model
- Return type:
nn.Embedding
- set_input_embeddings(value)[source]#
set new input embedding for model
- Parameters:
value (Embedding) – the new embedding of model
- Raises:
NotImplementedError – Model has not implement
set_input_embeddings
method
- class XLMPretrainedModel(*args, **kwargs)[source]#
Bases:
PretrainedModel
An abstract class for pretrained XLM models. It provides XLM 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#
alias of
XLMConfig
- class XLMWithLMHeadModel(config: XLMConfig)[source]#
Bases:
XLMPretrainedModel
The XLM Model transformer with a masked language modeling head on top (linear layer with weights tied to the input embeddings).
- Parameters:
config (
XLMConfig
) – An instance ofXLMConfig
.
- forward(input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None, cache=None, labels=None)[source]#
The XLMWithLMHeadModel forward method, overrides the __call__() special method.
- Parameters:
input_ids (Tensor) – See
XLMModel
.langs (Tensor, optional) – See
XLMModel
.attention_mask (Tensor, optional) – See
XLMModel
.position_ids (Tensor, optional) – See
XLMModel
.lengths (Tensor, optional) – See
XLMModel
.labels (Tensor, optional) – The Labels for computing the masked language modeling loss. Indices are selected in
[-100, 0, ..., vocab_size-1]
All labels set to-100
are ignored (masked), the loss is only computed for labels in[0, ..., vocab_size-1]
Shape as [batch_size, sequence_length] and dtype as int64. Defaults toNone
.
- Returns:
Returns tuple
(loss, logits)
. 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].
- Return type:
tuple
Example
import paddle from paddlenlp.transformers import XLMWithLMHeadModel, XLMTokenizer tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-tlm-xnli15-1024') model = XLMWithLMHeadModel.from_pretrained('xlm-mlm-tlm-xnli15-1024') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"] inputs["labels"] = inputs["input_ids"] loss, logits = model(**inputs)
- class XLMForSequenceClassification(config: XLMConfig)[source]#
Bases:
XLMPretrainedModel
The XLMModel with a sequence classification head on top (linear layer).
XLMForSequenceClassification
uses the first token in order to do the classification.- Parameters:
config (
XLMConfig
) – An instance ofXLMConfig
.
- forward(input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None)[source]#
The XLMForSequenceClassification forward method, overrides the __call__() special method.
- Parameters:
- Returns:
A tensor of the input text classification logits. Shape as
[batch_size, num_classes]
and dtype as float32.- Return type:
logits (Tensor)
Example
import paddle from paddlenlp.transformers import XLMForSequenceClassification, XLMTokenizer tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024") model = XLMForSequenceClassification.from_pretrained("xlm-mlm-tlm-xnli15-1024", num_classes=2) inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"] logits = model(**inputs)
- class XLMForTokenClassification(config: XLMConfig)[source]#
Bases:
XLMPretrainedModel
XLMModel with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- Parameters:
config (
XLMConfig
) – An instance ofXLMConfig
.
- forward(input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None)[source]#
The XLMForTokenClassification forward method, overrides the __call__() special method.
- Parameters:
- Returns:
A tensor of the input token classification logits. Shape as
[batch_size, sequence_length, num_classes]
and dtype asfloat32
.- Return type:
logits (Tensor)
Example
import paddle from paddlenlp.transformers import XLMForTokenClassification, XLMTokenizer tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024") model = XLMForTokenClassification.from_pretrained("xlm-mlm-tlm-xnli15-1024", num_classes=2) inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"] logits = model(**inputs)
- class XLMForQuestionAnsweringSimple(config: XLMConfig)[source]#
Bases:
XLMPretrainedModel
XLMModel 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
andspan end logits
).- Parameters:
config (
XLMConfig
) – An instance ofXLMConfig
.
- forward(input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None)[source]#
The XLMForQuestionAnswering forward method, overrides the __call__() special method.
- Parameters:
- Returns:
Returns tuple (
start_logits
,end_logits
).With the fields:
start_logits
(Tensor):A tensor of the input token classification logits, indicates the start position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length].
end_logits
(Tensor):A tensor of the input token classification logits, indicates the end position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length].
- Return type:
tuple
Example
import paddle from paddlenlp.transformers import XLMForQuestionAnswering, XLMTokenizer tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024") model = XLMForQuestionAnswering.from_pretrained("xlm-mlm-tlm-xnli15-1024", num_classes=2) inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!", lang="en") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} inputs["langs"] = paddle.ones_like(inputs["input_ids"]) * tokenizer.lang2id["en"] outputs = model(**inputs) start_logits = outputs[0] end_logits = outputs[1]
- class XLMForMultipleChoice(config: XLMConfig)[source]#
Bases:
XLMPretrainedModel
XLMModel with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- Parameters:
config (
XLMConfig
) – An instance ofXLMConfig
.
- forward(input_ids=None, langs=None, attention_mask=None, position_ids=None, lengths=None)[source]#
The XLMForMultipleChoice forward method, overrides the __call__() special method. :param input_ids: See
XLMModel
and shape as [batch_size, num_choice, sequence_length]. :type input_ids: Tensor :param langs: SeeXLMModel
and shape as [batch_size, num_choice, sequence_length]. :type langs: Tensor, optional :param attention_mask: SeeXLMModel
and shape as [batch_size, num_choice, sequence_length]. :type attention_mask: Tensor, optional :param position_ids: SeeXLMModel
and shape as [batch_size, num_choice, sequence_length]. :type position_ids: Tensor, optional :param lengths: SeeXLMModel
and shape as [batch_size, num_choice]. :type lengths: Tensor, optional- Returns:
A tensor of the multiple choice classification logits. Shape as
[batch_size, num_choice]
and dtype asfloat32
.- Return type:
reshaped_logits (Tensor)
Example
import paddle from paddlenlp.transformers import XLMForMultipleChoice, XLMTokenizer from paddlenlp.data import Pad tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-tlm-xnli15-1024") model = XLMForMultipleChoice.from_pretrained("xlm-mlm-tlm-xnli15-1024", num_choices=2) data = [ { "question": "how do you turn on an ipad screen?", "answer1": "press the volume button.", "answer2": "press the lock button.", "label": 1, }, { "question": "how do you indent something?", "answer1": "leave a space before starting the writing", "answer2": "press the spacebar", "label": 0, }, ] text = [] text_pair = [] for d in data: text.append(d["question"]) text_pair.append(d["answer1"]) text.append(d["question"]) text_pair.append(d["answer2"]) inputs = tokenizer(text, text_pair, lang="en") input_ids = Pad(axis=0, pad_val=tokenizer.pad_token_id)(inputs["input_ids"]) input_ids = paddle.to_tensor(input_ids, dtype="int64") langs = paddle.ones_like(input_ids) * tokenizer.lang2id["en"] reshaped_logits = model( input_ids=input_ids, langs=langs, )