enginex-mlu370-any2any/transformers/docs/source/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 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.

⚠️ 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.

-->
*This model was released on 2019-10-23 and added to Hugging Face Transformers on 2020-11-16.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
    </div>
</div>

# T5

[T5](https://huggingface.co/papers/1910.10683) is a encoder-decoder transformer available in a range of sizes from 60M to 11B parameters. It is designed to handle a wide range of NLP tasks by treating them all as text-to-text problems. This eliminates the need for task-specific architectures because T5 converts every NLP task into a text generation task.

To formulate every task as text generation, each task is prepended with a task-specific prefix (e.g., translate English to German: ..., summarize: ...). This enables T5 to handle tasks like translation, summarization, question answering, and more.

You can find all official T5 checkpoints under the [T5](https://huggingface.co/collections/google/t5-release-65005e7c520f8d7b4d037918) collection.

> [!TIP]
> Click on the T5 models in the right sidebar for more examples of how to apply T5 to different language tasks.

The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and how to translate with T5 from the command line.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="text2text-generation",
    model="google-t5/t5-base",
    dtype=torch.float16,
    device=0
)
pipeline("translate English to French: The weather is nice today.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "google-t5/t5-base"
    )
model = AutoModelForSeq2SeqLM.from_pretrained(
    "google-t5/t5-base",
    dtype=torch.float16,
    device_map="auto"
    )

input_ids = tokenizer("translate English to French: The weather is nice today.", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
<hfoption id="transformers CLI">

```bash
echo -e "translate English to French: The weather is nice today." | transformers run --task text2text-generation --model google-t5/t5-base --device 0
```

</hfoption>
</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.

```py
# pip install torchao
import torch
from transformers import TorchAoConfig, AutoModelForSeq2SeqLM, AutoTokenizer

quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "google/t5-v1_1-xl",
    dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("google/t5-v1_1-xl")
input_ids = tokenizer("translate English to French: The weather is nice today.", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Notes

- You can pad the encoder inputs on the left or right because T5 uses relative scalar embeddings.
- T5 models need a slightly higher learning rate than the default used in [`Trainer`]. Typically, values of `1e-4` and `3e-4` work well for most tasks.

## T5Config

[[autodoc]] T5Config

## T5Tokenizer

[[autodoc]] T5Tokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## T5TokenizerFast

[[autodoc]] T5TokenizerFast

## T5Model

[[autodoc]] T5Model
    - forward

## T5ForConditionalGeneration

[[autodoc]] T5ForConditionalGeneration
    - forward

## T5EncoderModel

[[autodoc]] T5EncoderModel
    - forward

## T5ForSequenceClassification

[[autodoc]] T5ForSequenceClassification
    - forward

## T5ForTokenClassification

[[autodoc]] T5ForTokenClassification
    - forward

## T5ForQuestionAnswering

[[autodoc]] T5ForQuestionAnswering
    - forward