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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmtokenizer | .md | "__classify__") to a vocabulary.
- The `lang2id` attribute maps the languages supported by the model with their IDs if provided (automatically set
for pretrained vocabularies).
- The `id2lang` attributes does reverse mapping if provided (automatically set for pretrained vocabularies).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Vocabulary file. | 416_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmtokenizer | .md | this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Vocabulary file.
merges_file (`str`):
Merges file.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
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.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip> | 416_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmtokenizer | .md | The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for | 416_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmtokenizer | .md | The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"</s>"`): | 416_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmtokenizer | .md | cls_token (`str`, *optional*, defaults to `"</s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"<special1>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict. | 416_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmtokenizer | .md | modeling. This is the token which the model will try to predict.
additional_special_tokens (`List[str]`, *optional*, defaults to `['<special0>', '<special1>', '<special2>', '<special3>', '<special4>', '<special5>', '<special6>', '<special7>', '<special8>', '<special9>']`):
List of additional special tokens.
lang2id (`Dict[str, int]`, *optional*):
Dictionary mapping languages string identifiers to their IDs.
id2lang (`Dict[int, str]`, *optional*):
Dictionary mapping language IDs to their string identifiers. | 416_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmtokenizer | .md | id2lang (`Dict[int, str]`, *optional*):
Dictionary mapping language IDs to their string identifiers.
do_lowercase_and_remove_accent (`bool`, *optional*, defaults to `True`):
Whether to lowercase and remove accents when tokenizing.
Methods: build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary | 416_6_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlm-specific-outputs | .md | models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
Base class for outputs of question answering models using a `SquadHead`.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses. | 416_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlm-specific-outputs | .md | Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.
start_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Log probabilities for the top config.start_n_top start token possibilities (beam-search). | 416_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlm-specific-outputs | .md | Log probabilities for the top config.start_n_top start token possibilities (beam-search).
start_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Indices for the top config.start_n_top start token possibilities (beam-search).
end_top_log_probs (`torch.FloatTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided): | 416_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlm-specific-outputs | .md | Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities
(beam-search).
end_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Indices for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search). | 416_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlm-specific-outputs | .md | Indices for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search).
cls_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Log probabilities for the `is_impossible` label of the answers.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | 416_7_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlm-specific-outputs | .md | Tuple of `torch.FloatTensor` (one for the output of the embeddings + 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 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, | 416_7_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlm-specific-outputs | .md | 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.
<frameworkcontent>
<pt> | 416_7_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmmodel | .md | The bare XLM 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](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 416_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmmodel | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`XLMConfig`]): 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 | 416_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmmodel | .md | Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 416_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmwithlmheadmodel | .md | The XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
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](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 416_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmwithlmheadmodel | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`XLMConfig`]): 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 | 416_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmwithlmheadmodel | .md | Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 416_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforsequenceclassification | .md | XLM Model 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](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 416_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforsequenceclassification | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`XLMConfig`]): 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 | 416_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforsequenceclassification | .md | Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 416_10_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmformultiplechoice | .md | XLM 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](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 416_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmformultiplechoice | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`XLMConfig`]): 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 | 416_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmformultiplechoice | .md | Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 416_11_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmfortokenclassification | .md | XLM 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](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 416_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmfortokenclassification | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`XLMConfig`]): 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 | 416_12_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmfortokenclassification | .md | Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 416_12_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforquestionansweringsimple | .md | XLM 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.) | 416_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforquestionansweringsimple | .md | 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](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`XLMConfig`]): Model configuration class with all the parameters of the model. | 416_13_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforquestionansweringsimple | .md | and behavior.
Parameters:
config ([`XLMConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 416_13_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforquestionanswering | .md | XLM Model with a beam-search 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.) | 416_14_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforquestionanswering | .md | 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](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`XLMConfig`]): Model configuration class with all the parameters of the model. | 416_14_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#xlmforquestionanswering | .md | and behavior.
Parameters:
config ([`XLMConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<tf> | 416_14_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#tfxlmmodel | .md | No docstring available for TFXLMModel
Methods: call | 416_15_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#tfxlmwithlmheadmodel | .md | No docstring available for TFXLMWithLMHeadModel
Methods: call | 416_16_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#tfxlmforsequenceclassification | .md | No docstring available for TFXLMForSequenceClassification
Methods: call | 416_17_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#tfxlmformultiplechoice | .md | No docstring available for TFXLMForMultipleChoice
Methods: call | 416_18_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#tfxlmfortokenclassification | .md | No docstring available for TFXLMForTokenClassification
Methods: call | 416_19_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/xlm.md | https://huggingface.co/docs/transformers/en/model_doc/xlm/#tfxlmforquestionansweringsimple | .md | No docstring available for TFXLMForQuestionAnsweringSimple
Methods: call
</tf>
</frameworkcontent> | 416_20_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/ | .md | <!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | 417_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | 417_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#overview | .md | The LongT5 model was proposed in [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916)
by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. It's an
encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of
T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2)
Transient-Global attention. | 417_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#overview | .md | Transient-Global attention.
The abstract from the paper is the following:
*Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the
performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we
explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated | 417_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#overview | .md | explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated
attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training
(PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call {\em Transient Global}
(TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs. We are | 417_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#overview | .md | (TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs. We are
able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on
question answering tasks.*
This model was contributed by [stancld](https://huggingface.co/stancld).
The original code can be found [here](https://github.com/google-research/longt5). | 417_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | - [`LongT5ForConditionalGeneration`] is an extension of [`T5ForConditionalGeneration`] exchanging the traditional
encoder *self-attention* layer with efficient either *local* attention or *transient-global* (*tglobal*) attention.
- Unlike the T5 model, LongT5 does not use a task prefix. Furthermore, it uses a different pre-training objective
inspired by the pre-training of [`PegasusForConditionalGeneration`]. | 417_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | inspired by the pre-training of [`PegasusForConditionalGeneration`].
- LongT5 model is designed to work efficiently and very well on long-range *sequence-to-sequence* tasks where the
input sequence exceeds commonly used 512 tokens. It is capable of handling input sequences of a length up to 16,384 tokens.
- For *Local Attention*, the sparse sliding-window local attention operation allows a given token to attend only `r` | 417_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | - For *Local Attention*, the sparse sliding-window local attention operation allows a given token to attend only `r`
tokens to the left and right of it (with `r=127` by default). *Local Attention* does not introduce any new parameters
to the model. The complexity of the mechanism is linear in input sequence length `l`: `O(l*r)`.
- *Transient Global Attention* is an extension of the *Local Attention*. It, furthermore, allows each input token to | 417_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | - *Transient Global Attention* is an extension of the *Local Attention*. It, furthermore, allows each input token to
interact with all other tokens in the layer. This is achieved via splitting an input sequence into blocks of a fixed
length `k` (with a default `k=16`). Then, a global token for such a block is obtained via summing and normalizing the embeddings of every token
in the block. Thanks to this, the attention allows each token to attend to both nearby tokens like in Local attention, and | 417_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | in the block. Thanks to this, the attention allows each token to attend to both nearby tokens like in Local attention, and
also every global token like in the case of standard global attention (*transient* represents the fact the global tokens
are constructed dynamically within each attention operation). As a consequence, *TGlobal* attention introduces
a few new parameters -- global relative position biases and a layer normalization for global token's embedding. | 417_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | a few new parameters -- global relative position biases and a layer normalization for global token's embedding.
The complexity of this mechanism is `O(l(r + l/k))`.
- An example showing how to evaluate a fine-tuned LongT5 model on the [pubmed dataset](https://huggingface.co/datasets/scientific_papers) is below.
```python
>>> import evaluate
>>> from datasets import load_dataset
>>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration | 417_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | >>> dataset = load_dataset("scientific_papers", "pubmed", split="validation")
>>> model = (
... LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
... .to("cuda")
... .half()
... )
>>> tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps") | 417_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | >>> def generate_answers(batch):
... inputs_dict = tokenizer(
... batch["article"], max_length=16384, padding="max_length", truncation=True, return_tensors="pt"
... )
... input_ids = inputs_dict.input_ids.to("cuda")
... attention_mask = inputs_dict.attention_mask.to("cuda")
... output_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=512, num_beams=2)
... batch["predicted_abstract"] = tokenizer.batch_decode(output_ids, skip_special_tokens=True) | 417_2_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | ... batch["predicted_abstract"] = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
... return batch | 417_2_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#usage-tips | .md | >>> result = dataset.map(generate_answer, batched=True, batch_size=2)
>>> rouge = evaluate.load("rouge")
>>> rouge.compute(predictions=result["predicted_abstract"], references=result["abstract"])
``` | 417_2_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#resources | .md | - [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | 417_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | This is the configuration class to store the configuration of a [`LongT5Model`] or a [`FlaxLongT5Model`]. It is
used to instantiate a LongT5 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 LongT5
[google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) architecture. | 417_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Arguments:
vocab_size (`int`, *optional*, defaults to 32128):
Vocabulary size of the LongT5 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LongT5Model`]. | 417_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | `inputs_ids` passed when calling [`LongT5Model`].
d_model (`int`, *optional*, defaults to 512):
Size of the encoder layers and the pooler layer.
d_kv (`int`, *optional*, defaults to 64):
Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
num_heads`.
d_ff (`int`, *optional*, defaults to 2048):
Size of the intermediate feed forward layer in each `LongT5Block`.
num_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder. | 417_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | num_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer encoder.
num_decoder_layers (`int`, *optional*):
Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
num_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
local_radius (`int`, *optional*, defaults to 127) | 417_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | local_radius (`int`, *optional*, defaults to 127)
Number of tokens to the left/right for each token to locally self-attend in a local attention mechanism.
global_block_size (`int`, *optional*, defaults to 16)
Lenght of blocks an input sequence is divided into for a global token representation. Used only for
`encoder_attention_type = "transient-global"`.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
The number of buckets to use for each attention layer. | 417_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | relative_attention_num_buckets (`int`, *optional*, defaults to 32):
The number of buckets to use for each attention layer.
relative_attention_max_distance (`int`, *optional*, defaults to 128):
The maximum distance of the longer sequences for the bucket separation.
dropout_rate (`float`, *optional*, defaults to 0.1):
The ratio for all dropout layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the layer normalization layers. | 417_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | layer_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the layer normalization layers.
initializer_factor (`float`, *optional*, defaults to 1):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. LongT5v1.1 uses the | 417_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. LongT5v1.1 uses the
`"gated-gelu"` feed forward projection. Original LongT5 implementation uses `"gated-gelu"`.
encoder_attention_type (`string`, *optional*, defaults to `"local"`):
Type of encoder attention to be used. Should be one of `"local"` or `"transient-global"`, which are
supported by LongT5 implementation.
use_cache (`bool`, *optional*, defaults to `True`): | 417_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5config | .md | supported by LongT5 implementation.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
<frameworkcontent>
<pt> | 417_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5model | .md | The bare LONGT5 Model transformer outputting raw hidden-states without any specific head on top.
The LongT5 model was proposed in [LongT5: Efficient Text-To-Text Transformer for Long
Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo
Ni, Yun-Hsuan Sung and Yinfei Yang. It's an encoder-decoder transformer pre-trained in a text-to-text denoising | 417_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5model | .md | Ni, Yun-Hsuan Sung and Yinfei Yang. It's an encoder-decoder transformer pre-trained in a text-to-text denoising
generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different
efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the | 417_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5model | .md | 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](https://pytorch.org/docs/stable/nn.html#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.
Parameters: | 417_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5model | .md | and behavior.
Parameters:
config ([`LongT5Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 417_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5forconditionalgeneration | .md | LONGT5 Model with a `language modeling` head on top.
The LongT5 model was proposed in [LongT5: Efficient Text-To-Text Transformer for Long
Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo
Ni, Yun-Hsuan Sung and Yinfei Yang. It's an encoder-decoder transformer pre-trained in a text-to-text denoising
generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different | 417_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5forconditionalgeneration | .md | generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different
efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.
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.) | 417_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5forconditionalgeneration | .md | 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](https://pytorch.org/docs/stable/nn.html#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.
Parameters:
config ([`LongT5Config`]): Model configuration class with all the parameters of the model. | 417_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5forconditionalgeneration | .md | and behavior.
Parameters:
config ([`LongT5Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 417_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5encodermodel | .md | The bare LONGT5 Model transformer outputting encoder's raw hidden-states without any specific head on top.
The LongT5 model was proposed in [LongT5: Efficient Text-To-Text Transformer for Long
Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo
Ni, Yun-Hsuan Sung and Yinfei Yang. It's an encoder-decoder transformer pre-trained in a text-to-text denoising | 417_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5encodermodel | .md | Ni, Yun-Hsuan Sung and Yinfei Yang. It's an encoder-decoder transformer pre-trained in a text-to-text denoising
generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different
efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the | 417_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5encodermodel | .md | 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](https://pytorch.org/docs/stable/nn.html#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.
Parameters: | 417_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#longt5encodermodel | .md | and behavior.
Parameters:
config ([`LongT5Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
</pt>
<jax> | 417_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#flaxlongt5model | .md | No docstring available for FlaxLongT5Model
Methods: __call__
- encode
- decode | 417_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/longt5.md | https://huggingface.co/docs/transformers/en/model_doc/longt5/#flaxlongt5forconditionalgeneration | .md | No docstring available for FlaxLongT5ForConditionalGeneration
Methods: __call__
- encode
- decode
</jax>
</frameworkcontent> | 417_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/ | .md | <!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | 418_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | 418_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/#overview | .md | The CPM model was proposed in [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin,
Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen,
Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
The abstract from the paper is the following: | 418_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/#overview | .md | The abstract from the paper is the following:
*Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3,
with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even
zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus | 418_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/#overview | .md | zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus
of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the
Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best
of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained | 418_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/#overview | .md | of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained
language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation,
cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many
NLP tasks in the settings of few-shot (even zero-shot) learning.* | 418_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/#overview | .md | NLP tasks in the settings of few-shot (even zero-shot) learning.*
This model was contributed by [canwenxu](https://huggingface.co/canwenxu). The original implementation can be found
here: https://github.com/TsinghuaAI/CPM-Generate
<Tip>
CPM's architecture is the same as GPT-2, except for tokenization method. Refer to [GPT-2 documentation](gpt2) for
API reference information.
</Tip> | 418_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/#cpmtokenizer | .md | Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models. | 418_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/cpm.md | https://huggingface.co/docs/transformers/en/model_doc/cpm/#cpmtokenizerfast | .md | Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models. | 418_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/ | .md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | 419_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | 419_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/#overview | .md | The PatchTST model was proposed in [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong and Jayant Kalagnanam.
At a high level the model vectorizes time series into patches of a given size and encodes the resulting sequence of vectors via a Transformer that then outputs the prediction length forecast via an appropriate head. The model is illustrated in the following figure: | 419_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/#overview | .md | 
The abstract from the paper is the following: | 419_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/#overview | .md | *We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local | 419_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/#overview | .md | the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We | 419_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/#overview | .md | can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy.* | 419_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/#overview | .md | This model was contributed by [namctin](https://huggingface.co/namctin), [gsinthong](https://huggingface.co/gsinthong), [diepi](https://huggingface.co/diepi), [vijaye12](https://huggingface.co/vijaye12), [wmgifford](https://huggingface.co/wmgifford), and [kashif](https://huggingface.co/kashif). The original code can be found [here](https://github.com/yuqinie98/PatchTST). | 419_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/#usage-tips | .md | The model can also be used for time series classification and time series regression. See the respective [`PatchTSTForClassification`] and [`PatchTSTForRegression`] classes. | 419_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/patchtst.md | https://huggingface.co/docs/transformers/en/model_doc/patchtst/#resources | .md | - A blog post explaining PatchTST in depth can be found [here](https://huggingface.co/blog/patchtst). The blog can also be opened in Google Colab. | 419_3_0 |
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