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127 lines
4.2 KiB
Markdown
127 lines
4.2 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2019-10-23 and contributed to Hugging Face Transformers on 2020-11-16.*
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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="AutoModel">
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```python
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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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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(model.device)
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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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</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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```python
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# pip install torchao
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, TorchAoConfig
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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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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(model.device)
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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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- get_special_tokens_mask
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- save_vocabulary
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## T5TokenizerFast
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[[autodoc]] T5TokenizerFast
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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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