mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 21:00:08 +06:00
210 lines
7.3 KiB
Markdown
210 lines
7.3 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
|
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
|
specific language governing permissions and limitations under the License.
|
|
|
|
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
|
rendered properly in your Markdown viewer.
|
|
|
|
-->
|
|
|
|
<div style="float: right;">
|
|
<div class="flex flex-wrap space-x-1">
|
|
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
|
|
<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
|
|
<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat">
|
|
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
|
|
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
|
|
</div>
|
|
</div>
|
|
|
|
# mBART
|
|
|
|
[mBART](https://huggingface.co/papers/2001.08210) is a multilingual machine translation model that pretrains the entire translation model (encoder-decoder) unlike previous methods that only focused on parts of the model. The model is trained on a denoising objective which reconstructs the corrupted text. This allows mBART to handle the source language and the target text to translate to.
|
|
|
|
[mBART-50](https://huggingface.co/paper/2008.00401) is pretrained on an additional 25 languages.
|
|
|
|
You can find all the original mBART checkpoints under the [AI at Meta](https://huggingface.co/facebook?search_models=mbart) organization.
|
|
|
|
> [!TIP]
|
|
> Click on the mBART models in the right sidebar for more examples of applying mBART to different language tasks.
|
|
|
|
> [!NOTE]
|
|
> The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`
|
|
|
|
The example below demonstrates how to translate text with [`Pipeline`] or the [`AutoModel`] class.
|
|
|
|
<hfoptions id="usage">
|
|
<hfoption id="Pipeline">
|
|
|
|
```py
|
|
import torch
|
|
from transformers import pipeline
|
|
|
|
pipeline = pipeline(
|
|
task="translation",
|
|
model="facebook/mbart-large-50-many-to-many-mmt",
|
|
device=0,
|
|
torch_dtype=torch.float16,
|
|
src_lang="en_XX",
|
|
tgt_lang="fr_XX",
|
|
)
|
|
print(pipeline("UN Chief Says There Is No Military Solution in Syria"))
|
|
```
|
|
|
|
</hfoption>
|
|
<hfoption id="AutoModel">
|
|
|
|
```py
|
|
import torch
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
|
|
|
|
article_en = "UN Chief Says There Is No Military Solution in Syria"
|
|
|
|
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
|
|
tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
|
|
|
|
tokenizer.src_lang = "en_XX"
|
|
encoded_hi = tokenizer(article_en, return_tensors="pt").to("cuda")
|
|
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"], cache_implementation="static")
|
|
print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True))
|
|
```
|
|
|
|
</hfoption>
|
|
</hfoptions>
|
|
|
|
## Notes
|
|
|
|
- You can check the full list of language codes via `tokenizer.lang_code_to_id.keys()`.
|
|
- mBART requires a special language id token in the source and target text during training. The source text format is `X [eos, src_lang_code]` where `X` is the source text. The target text format is `[tgt_lang_code] X [eos]`. The `bos` token is never used. The [`~PreTrainedTokenizerBase._call_`] encodes the source text format passed as the first argument or with the `text` keyword. The target text format is passed with the `text_label` keyword.
|
|
- Set the `decoder_start_token_id` to the target language id for mBART.
|
|
|
|
```py
|
|
import torch
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
|
|
|
|
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-en-ro", torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
|
|
tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX")
|
|
|
|
article = "UN Chief Says There Is No Military Solution in Syria"
|
|
inputs = tokenizer(article, return_tensors="pt")
|
|
|
|
translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
|
|
tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
|
|
```
|
|
|
|
- mBART-50 has a different text format. The language id token is used as the prefix for the source and target text. The text format is `[lang_code] X [eos]` where `lang_code` is the source language id for the source text and target language id for the target text. `X` is the source or target text respectively.
|
|
- Set the `eos_token_id` as the `decoder_start_token_id` for mBART-50. The target language id is used as the first generated token by passing `forced_bos_token_id` to [`~GenerationMixin.generate`].
|
|
|
|
```py
|
|
import torch
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
|
|
|
|
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
|
|
tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
|
|
|
|
article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
|
|
tokenizer.src_lang = "ar_AR"
|
|
|
|
encoded_ar = tokenizer(article_ar, return_tensors="pt")
|
|
generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
|
|
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
|
|
```
|
|
|
|
## MBartConfig
|
|
|
|
[[autodoc]] MBartConfig
|
|
|
|
## MBartTokenizer
|
|
|
|
[[autodoc]] MBartTokenizer
|
|
- build_inputs_with_special_tokens
|
|
|
|
## MBartTokenizerFast
|
|
|
|
[[autodoc]] MBartTokenizerFast
|
|
|
|
## MBart50Tokenizer
|
|
|
|
[[autodoc]] MBart50Tokenizer
|
|
|
|
## MBart50TokenizerFast
|
|
|
|
[[autodoc]] MBart50TokenizerFast
|
|
|
|
<frameworkcontent>
|
|
<pt>
|
|
|
|
## MBartModel
|
|
|
|
[[autodoc]] MBartModel
|
|
|
|
## MBartForConditionalGeneration
|
|
|
|
[[autodoc]] MBartForConditionalGeneration
|
|
|
|
## MBartForQuestionAnswering
|
|
|
|
[[autodoc]] MBartForQuestionAnswering
|
|
|
|
## MBartForSequenceClassification
|
|
|
|
[[autodoc]] MBartForSequenceClassification
|
|
|
|
## MBartForCausalLM
|
|
|
|
[[autodoc]] MBartForCausalLM
|
|
- forward
|
|
|
|
</pt>
|
|
<tf>
|
|
|
|
## TFMBartModel
|
|
|
|
[[autodoc]] TFMBartModel
|
|
- call
|
|
|
|
## TFMBartForConditionalGeneration
|
|
|
|
[[autodoc]] TFMBartForConditionalGeneration
|
|
- call
|
|
|
|
</tf>
|
|
<jax>
|
|
|
|
## FlaxMBartModel
|
|
|
|
[[autodoc]] FlaxMBartModel
|
|
- __call__
|
|
- encode
|
|
- decode
|
|
|
|
## FlaxMBartForConditionalGeneration
|
|
|
|
[[autodoc]] FlaxMBartForConditionalGeneration
|
|
- __call__
|
|
- encode
|
|
- decode
|
|
|
|
## FlaxMBartForSequenceClassification
|
|
|
|
[[autodoc]] FlaxMBartForSequenceClassification
|
|
- __call__
|
|
- encode
|
|
- decode
|
|
|
|
## FlaxMBartForQuestionAnswering
|
|
|
|
[[autodoc]] FlaxMBartForQuestionAnswering
|
|
- __call__
|
|
- encode
|
|
- decode
|
|
|
|
</jax>
|
|
</frameworkcontent> |