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*This model was released on 2019-10-23 and added to Hugging Face Transformers on 2020-11-16 and contributed by [thomwolf](https://huggingface.co/thomwolf).*

# T5

[T5](https://huggingface.co/papers/1910.10683) explores transfer learning in NLP by converting all language problems into a text-to-text format. This unified framework systematically compares various pretraining objectives, architectures, and datasets across numerous language understanding tasks. Leveraging scale and a new dataset called the "Colossal Clean Crawled Corpus," T5 achieves top results on benchmarks such as summarization, question answering, and text classification. The model and associated dataset are publicly available for further research.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text2text-generation", model="google-t5/t5-base", dtype="auto",)
pipeline("translate English to French: Plants create energy through a process known as photosynthesis.")
```

</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="auto",)

inputs = tokenizer("translate English to French: Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## Usage tips

- Pad encoder inputs on the left or right. T5 uses relative scalar embeddings.
- T5 models need a slightly higher learning rate than the default used in [`Trainer`]. Use values of `1e-4` and `3e-4` 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

