翻译将一段文本从一种语言转换为另一种语言。它可以建模为序列到序列任务：把输入序列映射为输出序列。翻译、摘要等任务都可以使用这一框架；翻译系统除文本到文本之外，也可以涉及语音或文本与语音之间的转换。
本指南演示两个步骤：在 OPUS Books 的英法子集上微调 T5，然后使用微调模型做推理。其他支持该任务的架构和检查点见官方翻译任务页。
开始前安装所需库：
pip install transformers datasets evaluate sacrebleu
官方文档建议登录 Hugging Face，方便随后上传并分享模型。在登录提示出现时输入自己的令牌；此文中的代码没有包含真实令牌。
>>> from huggingface_hub import notebook_login
>>> notebook_login()
加载 OPUS Books 数据集
使用 Datasets 加载英法子集：
>>> from datasets import load_dataset
>>> books = load_dataset("Helsinki-NLP/opus_books", "en-fr")
使用 train_test_split 划分训练集和测试集：
>>> books = books["train"].train_test_split(test_size=0.2)
查看一条记录。下面的数据是原文的展示示例：
>>> books["train"][0]
{'id': '90560',
'translation': {'en': 'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.',
'fr': 'Mais ce plateau élevé ne mesurait que quelques toises, et bientôt nous fûmes rentrés dans notre élément.'}}
translation 字段包含英语文本和对应的法语文本。
预处理
首先加载 T5 tokenizer，用于处理英语和法语句对：
>>> from transformers import AutoTokenizer
>>> checkpoint = "google-t5/t5-small"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
预处理函数需要完成三件事：
- 在输入前加上任务提示，让 T5 知道当前任务是翻译。能够执行多种 NLP 任务的模型，可能需要这种提示。
- 通过
text_target提供目标法语文本，让 tokenizer 正确处理目标序列；不设置它时，目标文本会按输入处理方式编码。 - 根据
max_length截断超过最大长度的序列。
>>> source_lang = "en"
>>> target_lang = "fr"
>>> prefix = "translate English to French: "
>>> def preprocess_function(examples):
... inputs = [prefix + example[source_lang] for example in examples["translation"]]
... targets = [example[target_lang] for example in examples["translation"]]
... model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True)
... return model_inputs
使用 Datasets 的 map 将预处理函数应用到整个数据集。设置 batched=True，一次处理多条记录，可以加快映射过程：
>>> tokenized_books = books.map(preprocess_function, batched=True)
再使用 DataCollatorForSeq2Seq 组成批次。在整理批次时，动态填充到该批次最长句子的长度，比把整个数据集都填充到统一最大长度更高效。
>>> from transformers import DataCollatorForSeq2Seq
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)
评估
训练过程中加入评估指标，有助于了解模型表现。可以通过 Evaluate 加载评估方法。此处使用 SacreBLEU；加载和计算指标的更多说明见 Evaluate 的快速入门。
>>> import evaluate
>>> metric = evaluate.load("sacrebleu")
定义函数，将预测和标签交给指标的 compute 方法。下面的实现会解码预测，把标签中的 -100 替换为填充 token ID，整理参考译文的形状，再计算 SacreBLEU 和生成长度：
>>> import numpy as np
>>> def postprocess_text(preds, labels):
... preds = [pred.strip() for pred in preds]
... labels = [[label.strip()] for label in labels]
... return preds, labels
>>> def compute_metrics(eval_preds):
... preds, labels = eval_preds
... if isinstance(preds, tuple):
... preds = preds[0]
... decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
... labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
... decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
... decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
... result = metric.compute(predictions=decoded_preds, references=decoded_labels)
... result = {"bleu": result["score"]}
... prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
... result["gen_len"] = np.mean(prediction_lens)
... result = {k: round(v, 4) for k, v in result.items()}
... return result
训练设置中会使用这个 compute_metrics 函数。
训练
如果不熟悉使用 Trainer 微调模型，可以先阅读官方训练入门。
使用 AutoModelForSeq2SeqLM 加载 T5：
>>> from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
>>> model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
接下来完成三个步骤：
- 在
Seq2SeqTrainingArguments中设置训练超参数。output_dir指定模型保存位置。示例启用push_to_hub=True，因此需要已登录及具备相应上传权限。每个 epoch 结束时，Trainer 会评估 SacreBLEU 并保存训练检查点。 - 将参数、模型、数据集、tokenizer、data collator 和指标函数传给
Seq2SeqTrainer。 - 调用
train开始微调。
下面是官方示例，使用 PyTorch。实际环境需要相应后端及 Trainer 的依赖，fp16 或 bf16 也需要硬件支持；本稿没有执行训练。
>>> training_args = Seq2SeqTrainingArguments(
... output_dir="my_awesome_opus_books_model",
... eval_strategy="epoch",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... weight_decay=0.01,
... save_total_limit=3,
... num_train_epochs=2,
... predict_with_generate=True,
... fp16=True, #change to bf16=True for XPU
... push_to_hub=True,
... )
>>> trainer = Seq2SeqTrainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_books["train"],
... eval_dataset=tokenized_books["test"],
... processing_class=tokenizer,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
训练完成后，官方示例通过 push_to_hub 分享模型：
>>> trainer.push_to_hub()
更完整的示例见官方PyTorch 翻译 Notebook。
推理
微调完成后，可以使用模型翻译新文本。对于 T5，应根据任务在输入前添加提示。英译法的提示如下：
>>> text = "translate English to French: Legumes share resources with nitrogen-fixing bacteria."
分词并返回 PyTorch 张量形式的 input_ids：
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_opus_books_model")
>>> inputs = tokenizer(text, return_tensors="pt").input_ids
使用 generate 生成译文。生成策略及参数的更多说明见 Text Generation API。
>>> from transformers import AutoModelForSeq2SeqLM
>>> model = AutoModelForSeq2SeqLM.from_pretrained("username/my_awesome_opus_books_model")
>>> outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
最后，将输出 token ID 解码回文本。下方保留原文输出，属于文档示例；采样会产生不同结果，而且原文译文含有用词、拼写及字符串展示问题，不能作为质量保证：
>>> tokenizer.decode(outputs[0], skip_special_tokens=True)
'Les lignées partagent des ressources avec des bactéries enfixant l'azote.'
来源：The HuggingFace Team，Translation，教程源文件。本文件已从英文翻译为中文，并补充运行环境、占位输出及未执行训练的说明；18 个代码/输出块逐字保留。本稿没有登录账户、下载模型、训练、上传或运行推理。
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