t5.md

T5

T5 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 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.

import torch
from transformers import pipeline

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

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

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

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

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

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

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

The example below uses torchao to only quantize the weights to int4.

# 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",
    torch_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("cuda")

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

TFT5Model

[[autodoc]] TFT5Model - call

TFT5ForConditionalGeneration

[[autodoc]] TFT5ForConditionalGeneration - call

TFT5EncoderModel

[[autodoc]] TFT5EncoderModel - call

FlaxT5Model

[[autodoc]] FlaxT5Model - call - encode - decode

FlaxT5ForConditionalGeneration

[[autodoc]] FlaxT5ForConditionalGeneration - call - encode - decode

FlaxT5EncoderModel

[[autodoc]] FlaxT5EncoderModel - call