在CoLA中使用HuggingFace NLP库的GLUE,可以通过以下步骤实现:
pip install transformers
transformers
和datasets
:from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
from datasets import load_dataset, load_metric
load_dataset
函数从HuggingFace数据集中加载CoLA数据集:dataset = load_dataset("glue", "cola")
train
、validation
和test
拆分来准备训练、验证和测试数据:train_dataset = dataset["train"]
eval_dataset = dataset["validation"]
test_dataset = dataset["test"]
AutoModelForSequenceClassification
和AutoTokenizer
来加载预训练的模型和标记器:model_name = "bert-base-uncased"
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def preprocess_function(examples):
return tokenizer(examples["sentence"], truncation=True, padding=True)
train_dataset = train_dataset.map(preprocess_function, batched=True)
eval_dataset = eval_dataset.map(preprocess_function, batched=True)
test_dataset = test_dataset.map(preprocess_function, batched=True)
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
weight_decay=0.01,
load_best_model_at_end=True,
metric_for_best_model="matthews_correlation",
)
metric = load_metric("glue", "cola")
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=metric,
)
trainer.train()
eval_result = trainer.evaluate(eval_dataset)
print(eval_result)
sentence = "This is a test sentence."
inputs = tokenizer(sentence, truncation=True, padding=True, return_tensors="pt")
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=-1)
print(predictions)
以上是在CoLA中使用HuggingFace NLP库的GLUE的基本步骤。对于更详细的信息和更多参数选项,请参考HuggingFace文档和相关教程。
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