【HuggingFace Transformer库学习笔记】基础组件学习：Trainer_transformers中的trainer

基础组件学习——Trainer

导入包

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
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加载数据集

dataset = load_dataset("csv", data_files="./ChnSentiCorp_htl_all.csv", split="train")
dataset = dataset.filter(lambda x: x["review"] is not None)
dataset

Dataset({
    features: ['label', 'review'],
    num_rows: 7765
})
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划分数据集

datasets = dataset.train_test_split(test_size=0.1)
datasets

DatasetDict({
    train: Dataset({
        features: ['label', 'review'],
        num_rows: 6988
    })
    test: Dataset({
        features: ['label', 'review'],
        num_rows: 777
    })
})
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数据集预处理

import torch

tokenizer = AutoTokenizer.from_pretrained("hfl/rbt3")

def process_function(examples):
    tokenized_examples = tokenizer(examples["review"], max_length=128, truncation=True)
    tokenized_examples["labels"] = examples["label"]
    return tokenized_examples

tokenized_datasets = datasets.map(process_function, batched=True, remove_columns=datasets["train"].column_names)
tokenized_datasets

DatasetDict({
    train: Dataset({
        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],
        num_rows: 6988
    })
    test: Dataset({
        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],
        num_rows: 777
    })
})
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创建模型

model = AutoModelForSequenceClassification.from_pretrained("hfl/rbt3")
model.config

BertConfig {
  "_name_or_path": "hfl/rbt3",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "directionality": "bidi",
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 3,
  "output_past": true,
  "pad_token_id": 0,
  "pooler_fc_size": 768,
  "pooler_num_attention_heads": 12,
  "pooler_num_fc_layers": 3,
  "pooler_size_per_head": 128,
  "pooler_type": "first_token_transform",
  "position_embedding_type": "absolute",
  "transformers_version": "4.35.2",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 21128
}
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创建评估函数

import evaluate

acc_metric = evaluate.load("accuracy")
f1_metirc = evaluate.load("f1")
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def eval_metric(eval_predict):
    predictions, labels = eval_predict
    predictions = predictions.argmax(axis=-1)
    acc = acc_metric.compute(predictions=predictions, references=labels)
    f1 = f1_metirc.compute(predictions=predictions, references=labels)
    acc.update(f1)
    return acc
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创建TrainingArguments

train_args = TrainingArguments(output_dir="./checkpoints",      # 输出文件夹
                               per_device_train_batch_size=64,  # 训练时的batch_size
                               per_device_eval_batch_size=128,  # 验证时的batch_size
                               logging_steps=10,                # log 打印的频率
                               evaluation_strategy="epoch",     # 评估策略
                               save_strategy="epoch",           # 保存策略
                               save_total_limit=3,              # 最大保存数
                               learning_rate=2e-5,              # 学习率
                               weight_decay=0.01,               # weight_decay
                               metric_for_best_model="f1",      # 设定评估指标
                               load_best_model_at_end=True)     # 训练完成后加载最优模型                            
train_args

TrainingArguments(
_n_gpu=8,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=True,
do_predict=False,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=epoch,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=True,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=True,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./checkpoints/runs/Jan13_18-37-43_wg-100-52,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=10,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=f1,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_torch,
optim_args=None,
output_dir=./checkpoints,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=128,
per_device_train_batch_size=64,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard'],
resume_from_checkpoint=None,
run_name=./checkpoints,
save_on_each_node=False,
save_safetensors=True,
save_steps=500,
...
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.01,
)
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创建Trainer

from transformers import DataCollatorWithPadding
trainer = Trainer(model=model, 
                  args=train_args, 
                  train_dataset=tokenized_datasets["train"], 
                  eval_dataset=tokenized_datasets["test"], 
                  data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
                  compute_metrics=eval_metric)
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模型训练

trainer.train()
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模型评估

trainer.evaluate(tokenized_datasets["test"])

{'eval_loss': 0.3903481960296631,
 'eval_accuracy': 0.8185328185328186,
 'eval_f1': 0.872858431018936,
 'eval_runtime': 0.4418,
 'eval_samples_per_second': 1758.732,
 'eval_steps_per_second': 2.263,
 'epoch': 3.0}
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模型预测

trainer.predict(tokenized_datasets["test"])

Downloading data files: 100%
1/1 [00:00<00:00, 43.99it/s]
Extracting data files: 100%
1/1 [00:00<00:00, 11.15it/s]
Generating train split:
7766/0 [00:00<00:00, 32712.12 examples/s]
Filter: 100%
7766/7766 [00:00<00:00, 66325.06 examples/s]
Dataset({
    features: ['label', 'review'],
    num_rows: 7765
})
DatasetDict({
    train: Dataset({
        features: ['label', 'review'],
        num_rows: 6988
    })
    test: Dataset({
        features: ['label', 'review'],
        num_rows: 777
    })
})
Map: 100%
6988/6988 [00:01<00:00, 6948.87 examples/s]
Map: 100%
777/777 [00:00<00:00, 7551.30 examples/s]
DatasetDict({
    train: Dataset({
        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],
        num_rows: 6988
    })
    test: Dataset({
        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],
        num_rows: 777
    })
})
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at project/transformers-code-master/model/rbt3 and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
BertConfig {
  "_name_or_path": "hfl/rbt3",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "directionality": "bidi",
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 3,
  "output_past": true,
  "pad_token_id": 0,
  "pooler_fc_size": 768,
  "pooler_num_attention_heads": 12,
  "pooler_num_fc_layers": 3,
  "pooler_size_per_head": 128,
  "pooler_type": "first_token_transform",
  "position_embedding_type": "absolute",
  "transformers_version": "4.35.2",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 21128
}
TrainingArguments(
_n_gpu=8,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=True,
do_predict=False,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=epoch,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=True,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=True,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./checkpoints/runs/Jan13_18-37-43_wg-100-52,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=10,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=f1,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_torch,
optim_args=None,
output_dir=./checkpoints,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=128,
per_device_train_batch_size=64,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard'],
resume_from_checkpoint=None,
run_name=./checkpoints,
save_on_each_node=False,
save_safetensors=True,
save_steps=500,
...
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.01,
)
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
Detected kernel version 3.10.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
hg/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '
 [42/42 00:26, Epoch 3/3]
Epoch	Training Loss	Validation Loss	Accuracy	F1
1	0.610000	0.516674	0.725869	0.830279
2	0.516900	0.414584	0.797941	0.861430
3	0.392800	0.390348	0.818533	0.872858
hg/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '
hg/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '
TrainOutput(global_step=42, training_loss=0.4852517715522221, metrics={'train_runtime': 47.1938, 'train_samples_per_second': 444.21, 'train_steps_per_second': 0.89, 'total_flos': 351909933963264.0, 'train_loss': 0.4852517715522221, 'epoch': 3.0})
hg/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '
{'eval_loss': 0.3903481960296631,
 'eval_accuracy': 0.8185328185328186,
 'eval_f1': 0.872858431018936,
 'eval_runtime': 0.4418,
 'eval_samples_per_second': 1758.732,
 'eval_steps_per_second': 2.263,
 'epoch': 3.0}
hg/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '
PredictionOutput(predictions=array([[ 0.6721468 , -0.03753865],
       [-1.1493708 ,  1.5108933 ],
       [ 0.9299763 , -0.3243772 ],
       ...,
       [-1.2351408 ,  1.3719875 ],
       [ 0.7072362 ,  0.33271554],
       [-1.2782698 ,  1.4008656 ]], dtype=float32), label_ids=array([0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
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       0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1,
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       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
       1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1,
       1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
       1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0,
       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,
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       1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
       0, 1, 0, 1, 1, 1, 1]), metrics={'test_loss': 0.3903481960296631, 'test_accuracy': 0.8185328185328186, 'test_f1': 0.872858431018936, 'test_runtime': 0.3025, 'test_samples_per_second': 2568.929, 'test_steps_per_second': 3.306})
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模型推理预测

sen = "我觉得这家酒店不怎么样"
id2_label = {0: "差评！", 1: "好评！"}
model.eval()
with torch.inference_mode():
    inputs = tokenizer(sen, return_tensors="pt")
    inputs = {k: v.cuda() for k, v in inputs.items()}
    logits = model(**inputs).logits
    pred = torch.argmax(logits, dim=-1)
    print(f"输入：{sen}\n模型预测结果:{id2_label.get(pred.item())}")
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使用pipeline整合

from transformers import pipeline

model.config.id2label = id2_label
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)
pipe(sen)

[{'label': '好评！', 'score': 0.956095278263092}]
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使用模型对推理内容进行解码时候的一些参数

查看训练情况

启动tensorboard查看模型运行情况

cd checkpoints/
ls 

heckpoint-14  checkpoint-28  checkpoint-42  runs
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# 启动
tensorboard --logdir runs

TensorFlow installation not found - running with reduced feature set.
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.15.1 at http://localhost:6006/ (Press CTRL+C to quit)
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进入网页可查看

如果使用的是vscode，可直接在vscode中启动，输入Ctrl + shift + p，然后再输入tensorboard（会提示安装），然后再点击在当前工作目录里启动

基础组件整体回顾

解决NLP任务的通用流程