🤗 Transformers

要執行 🤗 Transformers 示例，請確保已安裝以下庫

pip install datasets transformers torch evaluate nltk rouge_score

Trainer

evaluate 中的指標可以輕鬆地與 Trainer 整合。Trainer 接受一個 compute_metrics 關鍵字引數，該引數傳遞一個用於計算指標的函式。可以在 TrainerArguments 中使用 evaluation_strategy 指定評估間隔，基於此，模型會相應地進行評估，並將預測和標籤傳遞給 compute_metrics。

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
import numpy as np
import evaluate

# Prepare and tokenize dataset
dataset = load_dataset("yelp_review_full")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")

def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(200))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(200))

# Setup evaluation 
metric = evaluate.load("accuracy")

def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

# Load pretrained model and evaluate model after each epoch
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=small_train_dataset,
    eval_dataset=small_eval_dataset,
    compute_metrics=compute_metrics,
)

trainer.train()

Seq2SeqTrainer

我們可以使用 Seq2SeqTrainer 來處理序列到序列任務，例如翻譯或摘要。對於這類生成任務，通常會評估像 ROUGE 或 BLEU 這樣的指標。然而，這些指標要求我們用模型生成一些文字，而不是像分類任務那樣只進行一次前向傳播。透過設定 predict_with_generate=True，Seq2SeqTrainer 允許使用生成方法，這將為評估集中的每個樣本生成文字。這意味著我們在 compute_metric 函式中評估生成的文字。我們只需要先對預測和標籤進行解碼。

import nltk
from datasets import load_dataset
import evaluate
import numpy as np
from transformers import AutoTokenizer, DataCollatorForSeq2Seq
from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer

# Prepare and tokenize dataset
billsum = load_dataset("billsum", split="ca_test").shuffle(seed=42).select(range(200))
billsum = billsum.train_test_split(test_size=0.2)
tokenizer = AutoTokenizer.from_pretrained("t5-small")
prefix = "summarize: "

def preprocess_function(examples):
    inputs = [prefix + doc for doc in examples["text"]]
    model_inputs = tokenizer(inputs, max_length=1024, truncation=True)

    labels = tokenizer(text_target=examples["summary"], max_length=128, truncation=True)

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

tokenized_billsum = billsum.map(preprocess_function, batched=True)

# Setup evaluation
nltk.download("punkt_tab", quiet=True)
metric = evaluate.load("rouge")

def compute_metrics(eval_preds):
    preds, labels = eval_preds

    # decode preds and labels
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    # rougeLSum expects newline after each sentence
    decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
    decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]

    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
    return result

# Load pretrained model and evaluate model after each epoch
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)

training_args = Seq2SeqTrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=4,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=2,
    fp16=True,
    predict_with_generate=True
)

trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_billsum["train"],
    eval_dataset=tokenized_billsum["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics
)

trainer.train()

只要 evaluate 中的指標與任務和預測相容，您就可以將其與 Trainer 和 Seq2SeqTrainer 一起使用。如果您不想訓練模型，而只想評估一個現有模型，您可以在上述指令碼中用 trainer.evaluate() 替換 trainer.train()。

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