source stringclasses 470
values | url stringlengths 49 167 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#generation | .md | >>> translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id["en_XX"])
>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
"Returns the maximum value of a b c."
``` | 293_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#resources | .md | - [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | 293_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | This is the configuration class to store the configuration of a [`PLBartModel`]. It is used to instantiate an
PLBART 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 PLBART
[uclanlp/plbart-base]... | 293_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50005):
Vocabulary size of the PLBART model. Defines the number of different tokens that can be ... | 293_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | d_model (`int`, *optional*, defaults to 768):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 6):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 6):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 12):
Number o... | 293_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | decoder_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, default... | 293_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully co... | 293_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for cla... | 293_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`floa... | 293_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
scale_embedd... | 293_6_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (`int`, *optional*, defaults to 2):
The id of the token to force as the last generated token when `max_lengt... | 293_6_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | .md | >>> # Initializing a PLBART uclanlp/plbart-base style configuration
>>> configuration = PLBartConfig()
>>> # Initializing a model (with random weights) from the uclanlp/plbart-base style configuration
>>> model = PLBartModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | 293_6_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | .md | Construct an PLBART tokenizer.
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
<tokens> <eos>` for target language documents. ... | 293_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | .md | src_lang (`str`, *optional*):
A string representing the source language.
tgt_lang (`str`, *optional*):
A string representing the target language.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The start of sequence token.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
sep_token (`s... | 293_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | .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.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The cls ... | 293_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | .md | 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.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mas... | 293_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | .md | The token used for masking values. This is the token used when training this model with masking tasks. This
is only used in the `"base"` tokenizer type. For `"multi"` tokenizer, masking is never done for the
downstream tasks.
language_codes (`str`, *optional*, defaults to `"base"`):
What language codes to use. Should b... | 293_7_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | .md | sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling... | 293_7_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | .md | - `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, ... | 293_7_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | .md | >>> tokenizer = PLBartTokenizer.from_pretrained("uclanlp/plbart-python-en_XX", src_lang="python", tgt_lang="en_XX")
>>> example_python_phrase = "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])"
>>> expected_translation_english = "Returns the maximum value of a b c."
>>> inputs = tokenizer(example_python_phrase, t... | 293_7_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartmodel | .md | The bare PLBART 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... | 293_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartmodel | .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 ([`PLBartConfig`]):
Model configuration class wit... | 293_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartmodel | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 293_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforconditionalgeneration | .md | The PLBART Model with a language modeling head. Can be used for code-to-text, text-to-code and code-to-code.
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, pr... | 293_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforconditionalgeneration | .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 ([`PLBartConfig`]):
Model configuration class wit... | 293_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforconditionalgeneration | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 293_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforsequenceclassification | .md | PLBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for code
classification.
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 ... | 293_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforsequenceclassification | .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 ([`PLBartConfig`]):
Model configuration class wit... | 293_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforsequenceclassification | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 293_10_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforcausallm | .md | No docstring available for PLBartForCausalLM
Methods: forward | 293_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/ | .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 agr... | 294_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/ | .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 ... | 294_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5 | .md | <div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=t5">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-t5-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/t5-base">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face... | 294_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#overview | .md | The T5 model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by [Colin Raffel](https://huggingface.co/craffel), Noam Shazeer, [Adam Roberts](https://huggingface.co/adarob), Katherine Lee, Sharan Narang,
Michael Matena, Yanqi Zhou... | 294_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#overview | .md | 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 appro... | 294_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#overview | .md | 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,... | 294_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#overview | .md | 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 ... | 294_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#overview | .md | All checkpoints can be found on the [hub](https://huggingface.co/models?search=t5).
This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/google-research/text-to-text-transfer-transformer). | 294_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | .md | - 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: *... | 294_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | .md | for summarization: *summarize: ...*.
- The pretraining includes both supervised and self-supervised training. Supervised training is conducted on downstream tasks provided by the GLUE and SuperGLUE benchmarks (converting them into text-to-text tasks as explained above). | 294_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | .md | - Self-supervised training uses corrupted tokens, by randomly removing 15% of the tokens and replacing them with individual sentinel tokens (if several consecutive tokens are marked for removal, the whole group is replaced with a single sentinel token). The input of the encoder is the corrupted sentence, the input of t... | 294_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | .md | - T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.
- See the [training](#training), [inference](#inference) and [resources](#resources) sections below for all details regarding usage.
T5 comes in different sizes:
- [google-t5/t5-small](https://huggingface.co/goog... | 294_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | .md | - [google-t5/t5-large](https://huggingface.co/google-t5/t5-large)
- [google-t5/t5-3b](https://huggingface.co/google-t5/t5-3b)
- [google-t5/t5-11b](https://huggingface.co/google-t5/t5-11b).
Based on the original T5 model, Google has released some follow-up works:
- **T5v1.1**: T5v1.1 is an improved version of T5... | 294_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | .md | mixing in the supervised tasks. Refer to the documentation of T5v1.1 which can be found [here](t5v1.1).
- **mT5**: mT5 is a multilingual T5 model. It is pre-trained on the mC4 corpus, which includes 101 languages. Refer to
the documentation of mT5 which can be found [here](mt5).
- **byT5**: byT5 is a T5 model pre-t... | 294_3_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | .md | to the documentation of byT5 which can be found [here](byt5).
- **UL2**: UL2 is a T5 like model pretrained on various denoising objectives
- **Flan-T5**: Flan is a pretraining methods that is based on prompting. The Flan-T5 are T5 models trained on the Flan collection of
datasets which include: `taskmaster2`, `djay... | 294_3_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | .md | - **FLan-UL2** : the UL2 model finetuned using the "Flan" prompt tuning and dataset collection.
- **UMT5**: UmT5 is a multilingual T5 model trained on an improved and refreshed mC4 multilingual corpus, 29 trillion characters across 107 language, using a new sampling method, UniMax. Refer to
the documentation of mT5 ... | 294_3_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | 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 corresponding target sequence. The input
sequence is fed to the model using `input_ids`. The target sequence is shifted to th... | 294_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target
sequence is then appended by the EOS token and corresponds to the `labels`. The PAD token is hereby used as the
start-sequence token. T5 can be trained / fine-tuned both in a supervised and unsupervised fashi... | 294_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | language modeling head on top of the decoder.
- 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... | 294_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | `<extra_id_1>`, ... up to `<extra_id_99>`. As a default, 100 sentinel tokens are available in
[`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:
```python
>>> from transformers import T5Tokenizer, T5ForConditionalGe... | 294_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
>>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id... | 294_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | >>> # the forward function automatically creates the correct decoder_input_ids
>>> loss = model(input_ids=input_ids, labels=labels).loss
>>> loss.item()
3.7837
```
If you're interested in pre-training T5 on a new corpus, check out the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/tree/main/examples... | 294_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | directory.
- Supervised training
In this setup, the input sequence and output sequence are a standard sequence-to-sequence input-output mapping.
Suppose that we want to fine-tune the model for translation for example, and we have a training example: the input
sequence "The house is wonderful." and output sequence "... | 294_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
>>> labels = tokenizer("Das Haus ist wunderbar.", return_tensor... | 294_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | >>> # the forward function automatically creates the correct decoder_input_ids
>>> loss = model(input_ids=input_ids, labels=labels).loss
>>> loss.item()
0.2542
```
As you can see, only 2 inputs are required for the model in order to compute a loss: `input_ids` (which are the
`input_ids` of the encoded input sequence)... | 294_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | target sequence). The model will automatically create the `decoder_input_ids` based on the `labels`, by
shifting them one position to the right and prepending the `config.decoder_start_token_id`, which for T5 is
equal to 0 (i.e. the id of the pad token). Also note the task prefix: we prepend the input sequence with 'tr... | 294_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | during T5's pre-training.
However, the example above only shows a single training example. In practice, one trains deep learning models in
batches. This entails that we must pad/truncate examples to the same length. For encoder-decoder models, one
typically defines a `max_source_length` and `max_target_length`, which... | 294_4_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | input and output sequences respectively (otherwise they are truncated). These should be carefully set depending on
the task.
In addition, we must make sure that padding token id's of the `labels` are not taken into account by the loss
function. In PyTorch and Tensorflow, this can be done by replacing them with -100, ... | 294_4_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | of the `CrossEntropyLoss`. In Flax, one can use the `decoder_attention_mask` to ignore padded tokens from
the loss (see the [Flax summarization script](https://github.com/huggingface/transformers/tree/main/examples/flax/summarization) for details). We also pass
`attention_mask` as additional input to the model, which m... | 294_4_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | ```python
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration
>>> import torch | 294_4_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> # the following 2 hyperparameters are task-specific
>>> max_source_length = 512
>>> max_target_length = 128
>>> # Suppose we have the following 2 training examples:
>>> in... | 294_4_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | >>> input_sequence_2 = "HuggingFace is a company"
>>> output_sequence_2 = "HuggingFace est une entreprise"
>>> # encode the inputs
>>> task_prefix = "translate English to French: "
>>> input_sequences = [input_sequence_1, input_sequence_2]
>>> encoding = tokenizer(
... [task_prefix + sequence for sequence in inpu... | 294_4_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | >>> input_ids, attention_mask = encoding.input_ids, encoding.attention_mask
>>> # encode the targets
>>> target_encoding = tokenizer(
... [output_sequence_1, output_sequence_2],
... padding="longest",
... max_length=max_target_length,
... truncation=True,
... return_tensors="pt",
... )
>>> labels =... | 294_4_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | >>> # forward pass
>>> loss = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels).loss
>>> loss.item()
0.188
```
Additional training tips:
- T5 models need a slightly higher learning rate than the default one set in the `Trainer` when using the AdamW
optimizer. Typically, 1e-4 and 3e-4 work wel... | 294_4_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | answering, question generation). Note that T5 was pre-trained using the AdaFactor optimizer.
According to [this forum post](https://discuss.huggingface.co/t/t5-finetuning-tips/684), task prefixes matter when
(1) doing multi-task training (2) your task is similar or related to one of the supervised tasks used in T5's
... | 294_4_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | pre-training mixture (see Appendix D of the [paper](https://arxiv.org/pdf/1910.10683.pdf) for the task prefixes
used).
If training on TPU, it is recommended to pad all examples of the dataset to the same length or make use of
*pad_to_multiple_of* to have a small number of predefined bucket sizes to fit all examples i... | 294_4_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | .md | batches to the longest example is not recommended on TPU as it triggers a recompilation for every batch shape that is
encountered during training thus significantly slowing down the training. only padding up to the longest example in a
batch) leads to very slow training on TPU. | 294_4_20 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | At inference time, it is recommended to use [`~generation.GenerationMixin.generate`]. This
method takes care of encoding the input and feeding the encoded hidden states via cross-attention layers to the decoder
and auto-regressively generates the decoder output. Check out [this blog post](https://huggingface.co/blog/ho... | 294_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | There's also [this blog post](https://huggingface.co/blog/encoder-decoder#encoder-decoder) which explains how
generation works in general in encoder-decoder models.
```python
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration | 294_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small") | 294_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | >>> input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
>>> outputs = model.generate(input_ids)
>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Das Haus ist wunderbar.
```
Note that T5 uses the `pad_token_id` as the `decoder_start_token_id`, s... | 294_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | [`~generation.GenerationMixin.generate`], make sure you start it with the `pad_token_id`.
The example above only shows a single example. You can also do batched inference, like so:
```python
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration | 294_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> task_prefix = "translate English to German: "
>>> # use different length sentences to test batching
>>> sentences = ["The house is wonderful.", "I like to work in NYC."]
>... | 294_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | >>> inputs = tokenizer([task_prefix + sentence for sentence in sentences], return_tensors="pt", padding=True)
>>> output_sequences = model.generate(
... input_ids=inputs["input_ids"],
... attention_mask=inputs["attention_mask"],
... do_sample=False, # disable sampling to test if batching affects output
..... | 294_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | >>> print(tokenizer.batch_decode(output_sequences, skip_special_tokens=True))
['Das Haus ist wunderbar.', 'Ich arbeite gerne in NYC.']
```
Because T5 has been trained with the span-mask denoising objective,
it can be used to predict the sentinel (masked-out) tokens during inference.
The predicted tokens will then be ... | 294_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | .md | >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
>>> sequence_ids = model.generate(input_ids)
>>> sequences = tokenizer.... | 294_5_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#performance | .md | If you'd like a faster training and inference performance, install [NVIDIA APEX](https://github.com/NVIDIA/apex#quick-start) for NVIDIA GPUs, or [ROCm APEX](https://github.com/ROCmSoftwarePlatform/apex) for AMD GPUs and then the model will automatically use `apex.normalization.FusedRMSNorm` instead of `T5LayerNorm`. Th... | 294_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with T5. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an exist... | 294_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | <PipelineTag pipeline="text-classification"/>
- A notebook for how to [finetune T5 for classification and multiple choice](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb).
- A notebook for how to [finetune T5 for sentiment span extraction](https://colab.research.goo... | 294_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | <PipelineTag pipeline="token-classification"/>
- A notebook for how to [finetune T5 for named entity recognition](https://colab.research.google.com/drive/1obr78FY_cBmWY5ODViCmzdY6O1KB65Vc?usp=sharing). 🌎
<PipelineTag pipeline="text-generation"/>
- A notebook for [Finetuning CodeT5 for generating docstrings from ... | 294_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | <PipelineTag pipeline="summarization"/>
- A notebook to [Finetune T5-base-dutch to perform Dutch abstractive summarization on a TPU](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/T5/Fine_tuning_Dutch_T5_base_on_CNN_Daily_Mail_for_summarization_(on_TPU_using_HuggingFace_Acceler... | 294_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | - A notebook for how to [finetune T5 for summarization in PyTorch and track experiments with WandB](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb#scrollTo=OKRpFvYhBauC). 🌎
- A blog post on [Distributed Training: Train BART/T5 for Summari... | 294_7_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | - [`T5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization.ipynb). | 294_7_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | - [`TFT5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb).
- [`FlaxT5ForConditionalGeneration`]... | 294_7_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | - [Summarization](https://huggingface.co/course/chapter7/5?fw=pt#summarization) chapter of the 🤗 Hugging Face course.
- [Summarization task guide](../tasks/summarization)
<PipelineTag pipeline="fill-mask"/> | 294_7_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | - [`FlaxT5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#t5-like-span-masked-language-modeling) for training T5 with a span-masked language model objective. The script also shows how to train a T5 tokenizer. [`FlaxT5... | 294_7_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | <PipelineTag pipeline="translation"/>
- [`T5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb). | 294_7_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | - [`TFT5ForConditionalGeneration`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/translation) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb).
- [Translation task guide](../tasks/tran... | 294_7_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | - [Translation task guide](../tasks/translation)
<PipelineTag pipeline="question-answering"/>
- A notebook on how to [finetune T5 for question answering with TensorFlow 2](https://colab.research.google.com/github/snapthat/TF-T5-text-to-text/blob/master/snapthatT5/notebooks/TF-T5-Datasets%20Training.ipynb). 🌎
- A n... | 294_7_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | .md | 🚀 **Deploy**
- A blog post on how to deploy [T5 11B for inference for less than $500](https://www.philschmid.de/deploy-t5-11b). | 294_7_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5config | .md | This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
instantiate a T5 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 T5
[google-t5/t5-s... | 294_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5config | .md | 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 T5 model. Defines the number of different tokens that can be... | 294_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5config | .md | 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. The `inner_dim` of the projection layer will
be defined as `num_heads * d_kv`.
d_ff (`int`, *optional*, defaults to 2048)... | 294_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5config | .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 e... | 294_8_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5config | .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):
Th... | 294_8_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5config | .md | The dropout ratio for classifier.
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_for... | 294_8_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5config | .md | Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
`"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). | 294_8_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5tokenizer | .md | Construct a T5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
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`):
[SentencePiece](htt... | 294_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5tokenizer | .md | contains the vocabulary necessary to instantiate a tokenizer.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
unk_token (`str... | 294_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5tokenizer | .md | 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.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
extra_ids (`int`, *optional*, defaults to 100):
Add a num... | 294_9_2 |
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