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https://github.com/huggingface/transformers.git
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d538293f62
* transformers-cli -> transformers * Chat command works with positional argument * update doc references to transformers-cli * doc headers * deepspeed --------- Co-authored-by: Joao Gante <joao@huggingface.co>
207 lines
8.6 KiB
Markdown
207 lines
8.6 KiB
Markdown
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">
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</div>
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</div>
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# T5
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[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.
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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.
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You can find all official T5 checkpoints under the [T5](https://huggingface.co/collections/google/t5-release-65005e7c520f8d7b4d037918) collection.
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> [!TIP]
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> Click on the T5 models in the right sidebar for more examples of how to apply T5 to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and how to translate with T5 from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="text2text-generation",
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model="google-t5/t5-base",
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torch_dtype=torch.float16,
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device=0
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)
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pipeline("translate English to French: The weather is nice today.")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"google-t5/t5-base"
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)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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"google-t5/t5-base",
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torch_dtype=torch.float16,
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device_map="auto"
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)
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input_ids = tokenizer("translate English to French: The weather is nice today.", return_tensors="pt").to("cuda")
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output = model.generate(**input_ids, cache_implementation="static")
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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</hfoption>
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<hfoption id="transformers CLI">
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```bash
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echo -e "translate English to French: The weather is nice today." | transformers run --task text2text-generation --model google-t5/t5-base --device 0
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```
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</hfoption>
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</hfoptions>
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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.
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The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
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```py
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# pip install torchao
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import torch
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from transformers import TorchAoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
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quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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"google/t5-v1_1-xl",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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quantization_config=quantization_config
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)
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tokenizer = AutoTokenizer.from_pretrained("google/t5-v1_1-xl")
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input_ids = tokenizer("translate English to French: The weather is nice today.", return_tensors="pt").to("cuda")
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output = model.generate(**input_ids, cache_implementation="static")
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## Notes
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- You can pad the encoder inputs on the left or right because T5 uses relative scalar embeddings.
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- 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.
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## T5Config
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[[autodoc]] T5Config
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## T5Tokenizer
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[[autodoc]] T5Tokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## T5TokenizerFast
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[[autodoc]] T5TokenizerFast
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<frameworkcontent>
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<pt>
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## T5Model
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[[autodoc]] T5Model
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- forward
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## T5ForConditionalGeneration
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[[autodoc]] T5ForConditionalGeneration
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- forward
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## T5EncoderModel
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[[autodoc]] T5EncoderModel
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- forward
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## T5ForSequenceClassification
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[[autodoc]] T5ForSequenceClassification
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- forward
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## T5ForTokenClassification
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[[autodoc]] T5ForTokenClassification
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- forward
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## T5ForQuestionAnswering
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[[autodoc]] T5ForQuestionAnswering
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- forward
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</pt>
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<tf>
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## TFT5Model
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[[autodoc]] TFT5Model
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- call
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## TFT5ForConditionalGeneration
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[[autodoc]] TFT5ForConditionalGeneration
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- call
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## TFT5EncoderModel
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[[autodoc]] TFT5EncoderModel
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- call
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</tf>
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<jax>
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## FlaxT5Model
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[[autodoc]] FlaxT5Model
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- __call__
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- encode
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- decode
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## FlaxT5ForConditionalGeneration
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[[autodoc]] FlaxT5ForConditionalGeneration
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- __call__
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- encode
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- decode
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## FlaxT5EncoderModel
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[[autodoc]] FlaxT5EncoderModel
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- __call__
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</jax>
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</frameworkcontent>
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