.. 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 specific language governing permissions and limitations under the License. T5 ----------------------------------------------------------------------------------------------------------------------- **DISCLAIMER:** This model is still a work in progress, if you see something strange, file a `Github Issue `__. Overview ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The T5 model was presented in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer `_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. The abstract from the paper is the following: *Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.* Tips: - T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task, e.g., for translation: *translate English to German: ...*, for summarization: *summarize: ...*. For more information about which prefix to use, it is easiest to look into Appendix D of the `paper `__. - For sequence-to-sequence generation, it is recommended to use :obj:`T5ForConditionalGeneration.generate()``. This method takes care of feeding the encoded input via cross-attention layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right. The original code can be found `here `__. Training ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher forcing. This means that for training we always need an input sequence and a target sequence. The input sequence is fed to the model using :obj:`input_ids``. The target sequence is shifted to the right, i.e., prepended by a start-sequence token and fed to the decoder using the :obj:`decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the :obj:`labels`. The PAD token is hereby used as the start-sequence token. T5 can be trained / fine-tuned both in a supervised and unsupervised fashion. - Unsupervised denoising training In this setup spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) and the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. Each sentinel token represents a unique mask token for this sentence and should start with :obj:``, :obj:``, ... up to :obj:``. As a default, 100 sentinel tokens are available in :class:`~transformers.T5Tokenizer`. For instance, the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be processed as follows: .. code-block:: input_ids = tokenizer('The walks in park', return_tensors='pt').input_ids labels = tokenizer(' cute dog the ', return_tensors='pt').input_ids # the forward function automatically creates the correct decoder_input_ids loss = model(input_ids=input_ids, labels=labels).loss - Supervised training In this setup the input sequence and output sequence are standard sequence-to-sequence input output mapping. In translation, for instance with the input sequence "The house is wonderful." and output sequence "Das Haus ist wunderbar.", the sentences should be processed as follows: .. code-block:: input_ids = tokenizer('translate English to German: The house is wonderful.', return_tensors='pt').input_ids labels = tokenizer('Das Haus ist wunderbar.', return_tensors='pt').input_ids # the forward function automatically creates the correct decoder_input_ids loss = model(input_ids=input_ids, labels=labels).loss T5Config ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.T5Config :members: T5Tokenizer ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.T5Tokenizer :members: build_inputs_with_special_tokens, get_special_tokens_mask, create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary T5TokenizerFast ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.T5TokenizerFast :members: T5Model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.T5Model :members: forward, parallelize, deparallelize T5ForConditionalGeneration ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.T5ForConditionalGeneration :members: forward, parallelize, deparallelize T5EncoderModel ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.T5EncoderModel :members: forward, parallelize, deparallelize TFT5Model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.TFT5Model :members: call TFT5ForConditionalGeneration ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.TFT5ForConditionalGeneration :members: call TFT5EncoderModel ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: transformers.TFT5EncoderModel :members: call