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120 lines
4.3 KiB
Markdown
120 lines
4.3 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 2020-10-22 and contributed to Hugging Face Transformers on 2020-11-17.*
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# mT5
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[mT5](https://huggingface.co/papers/2010.11934) is a multilingual variant of [T5](./t5), training on 101 languages. It also incorporates a new "accidental translation" technique to prevent the model from incorrectly translating predictions into the wrong language.
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You can find all the original [mT5] checkpoints under the [mT5](https://huggingface.co/collections/google/mt5-release-65005f1a520f8d7b4d039509) collection.
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> [!TIP]
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> This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
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>
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> Click on the mT5 models in the right sidebar for more examples of how to apply mT5 to different language tasks.
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The example below demonstrates how to summarize text with [`Pipeline`], [`AutoModel`], and 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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"csebuetnlp/mT5_multilingual_XLSum"
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)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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"csebuetnlp/mT5_multilingual_XLSum",
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device_map="auto",
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)
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input_text = """Plants are remarkable organisms that produce their own food using a method called photosynthesis.
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This process involves converting sunlight, carbon dioxide, and water into glucose, which provides energy for growth.
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Plants play a crucial role in sustaining life on Earth by generating oxygen and serving as the foundation of most ecosystems."""
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input_ids = tokenizer(input_text, 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 [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.
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```python
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4"
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)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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"csebuetnlp/mT5_multilingual_XLSum",
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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(
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"csebuetnlp/mT5_multilingual_XLSum"
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)
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input_text = """Plants are remarkable organisms that produce their own food using a method called photosynthesis.
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This process involves converting sunlight, carbon dioxide, and water into glucose, which provides energy for growth.
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Plants play a crucial role in sustaining life on Earth by generating oxygen and serving as the foundation of most ecosystems."""
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input_ids = tokenizer(input_text, 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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- mT5 must be fine-tuned for downstream tasks because it was only pretrained on the [mc4](https://huggingface.co/datasets/mc4) dataset.
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## MT5Config
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[[autodoc]] MT5Config
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## MT5Model
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[[autodoc]] MT5Model
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## MT5ForConditionalGeneration
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[[autodoc]] MT5ForConditionalGeneration
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## MT5EncoderModel
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[[autodoc]] MT5EncoderModel
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## MT5ForSequenceClassification
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[[autodoc]] MT5ForSequenceClassification
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## MT5ForTokenClassification
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[[autodoc]] MT5ForTokenClassification
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## MT5ForQuestionAnswering
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[[autodoc]] MT5ForQuestionAnswering
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