我将按照本教程学习有关培训师API的知识。https://huggingface.co/transformers/training.html
我复制了如下代码：
from datasets import load_dataset
import numpy as np
from datasets import load_metric
metric = load_metric("accuracy")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
print('Download dataset ...')
raw_datasets = load_dataset("imdb")
from transformers import AutoTokenizer
print('Tokenize text ...')
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
print('Prepare data ...')
small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(500))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(500))
full_train_dataset = tokenized_datasets["train"]
full_eval_dataset = tokenized_datasets["test"]
print('Define model ...')
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
print('Define trainer ...')
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments("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,
)
print('Fine-tune train ...')
trainer.evaluate()
然而，它没有报告任何关于训练指标的信息，而是以下消息：
Download dataset ...
Reusing dataset imdb (/Users/congminmin/.cache/huggingface/datasets/imdb/plain_text/1.0.0/4ea52f2e58a08dbc12c2bd52d0d92b30b88c00230b4522801b3636782f625c5b)
Tokenize text ...
100%|██████████| 25/25 [00:06<00:00, 4.01ba/s]
100%|██████████| 25/25 [00:06<00:00, 3.99ba/s]
100%|██████████| 50/50 [00:13<00:00, 3.73ba/s]
Prepare data ...
Define model ...
Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Define trainer ...
Fine-tune train ...
100%|██████████| 63/63 [08:35<00:00, 8.19s/it]
Process finished with exit code 0
教程没有更新吗？我是否应该进行一些配置更改以报告度量？
我认为您需要告诉培训师在TrainingArguments 中使用evaluation_strategy和eval_steps评估性能的频率
evaluate函数返回度量，而不打印它们。
metrics=trainer.evaluate()
print(metrics)
工作？此外，消息说你使用的是基本伯特模型，它不是为句子分类预先训练的，而是基本语言模型。因此，它没有任务的初始化权重，应该进行训练
为什么要执行trainer.evaluate()？这只是在验证集上运行验证。如果你想微调或训练，你需要做：
trainer.train()
关键是标签，bert模型需要标签字段是"标签"；，所以您必须重命名列。
# check fields
print(next(iter(small_train_dataset)).keys())
# rename field
small_train_dataset = small_train_dataset.rename_column("label", "labels")
small_eval_dataset = small_eval_dataset.rename_column("label", "labels")
来自文档
重命名列"；标签"；至"；标签"；(因为模型期望参数被命名为标签(
您应该将evaluation_strategy='epoch'或evaluation_strategy='steps'添加到培训师参数中。默认情况下，培训期间不进行评估。