# 机器翻译任务

> sacrebleu==1.5.1，本notebook需要指定sacrebleu的版本，详见\[github issue]\([SacreBLEU update · Issue #2737 · huggingface/datasets (github.com)](https://github.com/huggingface/datasets/issues/2737))

本次任务使用

数据集，

## 加载数据

```python
from datasets import load_dataset, load_metric

raw_datasets = load_dataset("wmt16", "ro-en")
metric = load_metric("sacrebleu")
```

数据长这样

> {'translation': {'en': 'Membership of Parliament: see Minutes', 'ro': 'Componenţa Parlamentului: a se vedea procesul-verbal'}}

可以看到一句en对应一句romanian

依然使用`metric.compute(predictions=fake_preds, references=fake_labels)`api计算得分。

## 预处理

1. tokenizer对数据进行tokenize
2. 将得到的tokens转化为模型需要对应的token id
3. 再转化为模型需要的格式

```python
from transformers import AutoTokenizer
# 需要安装`sentencepiece`： pip install sentencepiece

tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
```

以我们使用的mBART模型为例，我们需要正确设置source语言和target语言。如果您要翻译的是其他双语语料，请查看[这里](https://huggingface.co/facebook/mbart-large-cc25)。我们可以检查source和target语言的设置：

```python
if "mbart" in model_checkpoint:
    tokenizer.src_lang = "en-XX"
    tokenizer.tgt_lang = "ro-RO"
```

注意：为了给模型准备好翻译的targets，我们使用`as_target_tokenizer`来控制targets所对应的特殊token：

```
with tokenizer.as_target_tokenizer():
    print(tokenizer("Hello, this one sentence!"))
    model_input = tokenizer("Hello, this one sentence!")
    tokens = tokenizer.convert_ids_to_tokens(model_input['input_ids'])
    # 打印看一下special toke
    print('tokens: {}'.format(tokens))
{'input_ids': [10334, 1204, 3, 15, 8915, 27, 452, 59, 29579, 581, 23, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
tokens: ['▁Hel', 'lo', ',', '▁', 'this', '▁o', 'ne', '▁se', 'nten', 'ce', '!', '</s>']
```

如果您使用的是T5预训练模型的checkpoints，需要对特殊的前缀进行检查。T5使用特殊的前缀来告诉模型具体要做的任务，具体前缀例子如下：

```
if model_checkpoint in ["t5-small", "t5-base", "t5-larg", "t5-3b", "t5-11b"]:
    prefix = "translate English to Romanian: "
else:
    prefix = ""
```

现在我们可以把所有内容放在一起组成我们的预处理函数了。我们对样本进行预处理的时候，我们还会`truncation=True`这个参数来确保我们超长的句子被截断。默认情况下，对与比较短的句子我们会自动padding。

**组合起来：**

```
max_input_length = 128
max_target_length = 128
source_lang = "en"
target_lang = "ro"

def preprocess_function(examples):
    inputs = [prefix + ex[source_lang] for ex in examples["translation"]]
    targets = [ex[target_lang] for ex in examples["translation"]]
    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)

    # Setup the tokenizer for targets
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(targets, max_length=max_target_length, truncation=True)

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs
```

以上的预处理函数可以处理一个样本，也可以处理多个样本exapmles。如果是处理多个样本，则返回的是多个样本被预处理之后的结果list。

```
preprocess_function(raw_datasets['train'][:2])
```

```
{'input_ids': [[393, 4462, 14, 1137, 53, 216, 28636, 0], [24385, 14, 28636, 14, 4646, 4622, 53, 216, 28636, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[42140, 494, 1750, 53, 8, 59, 903, 3543, 9, 15202, 0], [36199, 6612, 9, 15202, 122, 568, 35788, 21549, 53, 8, 59, 903, 3543, 9, 15202, 0]]}
```

使用map函数对所有样本进行预处理。

```
tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)
```

## 微调transformer模型

既然数据已经准备好了，现在我们需要下载并加载我们的预训练模型，然后微调预训练模型。既然我们是做seq2seq任务，那么我们需要一个能解决这个任务的模型类。我们使用`AutoModelForSeq2SeqLM`这个类。和tokenizer相似，`from_pretrained`方法同样可以帮助我们下载并加载模型，同时也会对模型进行缓存，就不会重复下载模型啦。

```
from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer

model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
```

由于我们微调的任务是机器翻译，而**我们加载的是预训练的seq2seq**模型，所以**不会**像上个问答任务里加载模型的时候**扔掉了一些不匹配的神经网络参数。**

1. 为了能够得到一个`Seq2SeqTrainer`训练工具，我们还需要3个要素，其中最重要的是训练的设定/参数[**`Seq2SeqTrainingArguments`**](https://huggingface.co/transformers/main_classes/trainer.html#transformers.Seq2SeqTrainingArguments)。这个训练设定包含了能够定义训练过程的所有属性：

```python
batch_size = 16
args = Seq2SeqTrainingArguments(
    "test-translation",
    evaluation_strategy = "epoch", # 每个epcoh会做一次验证评估
    learning_rate=2e-5,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    weight_decay=0.01,
    save_total_limit=3, # 由于我们的数据集比较大，同时`Seq2SeqTrainer`会不断保存模型，所以我们需要告诉它至多保存`save_total_limit=3`个模型。
    num_train_epochs=1,
    predict_with_generate=True,
    fp16=False,
)
```

1. 最后我们需要一个数据收集器data collator，将我们处理好的输入喂给模型。

```
data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
```

1. 设置好`Seq2SeqTrainer`还剩最后一件事情，那就是我们需要定义好评估方法。我们使用`metric`来完成评估。将模型预测送入评估之前，我们也会做一些数据后处理：

```python
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)

    # Replace -100 in the labels as we can't decode them.
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    # Some simple post-processing
    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
```

最后将所有的参数/数据/模型传给`Seq2SeqTrainer`即可

```python
trainer = Seq2SeqTrainer(
    model,
    args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["validation"],
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics
)
```

调用`train`方法进行微调训练。

```python
trainer.train()
```


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