LeonYi 2025-01-12 22:24 浙江
作者：LeonYi
链接：https://www.zhihu.com/question/632473480/answer/75664255663
使用Qwen2ForSequenceClassification实现文本分类任务。
一、实验结果和结论
这几个月，在大模型分类场景做了很多实验，攒了一点小小经验。
1、短文本
1）query情感分类，一般不如BERT
ps：结论和，https://segmentfault.com/a/1190000044485544#item-13，基本一致
2、长文本
1）通话ASR转译长文本，BERT截断512不如LLM
LLM没有截断（如果都阶段512，可能效果差不多）
没有对比，BERT进行文本滑动窗口的版本
2）Base v.s. Instruct
数据量小时，Base微调不如Instruct(Instruct模型有对齐税，但是微调数据量小时，效果还是比Base没见过指令微调样本的好)
3）SFT v.s. LoRA
数据量小时（总样本10K以下，每个标签需要视情况而定），SFT微调不如LoRA（SFT调参成本也更大）
3、分类场景的提升方案
1）生成式微调独有
混合同领域相似数据类型不同业务数据，可以提升若干点
数据分布不能差异太大，特别是文本长度，否则混入这种数据反而会让效果下滑（一个平均长度1.2K，一个平均长度5k）
混入比例（接近2：1，不同场景需自行尝试）
混入顺序（我使用的是随机采样，没验证是否分开先后训练顺序是否有影响）
优化提示词（提示词中，增加各类别标签的精要描述；短文本可尝试few-shot）
2）分类头微调 + 生成式微调
数据量大时（10K以上，平均每个标签样本充足），尝试微调Base，而不是微调Instruct
数据增强：尝试无标注数据上跑的伪标签样本（提示词抽的标签 + 微调后的模型抽的标签）
数据量大时，尝试SFT
LoRA微调时，加入LLM的embedding层（未验证过）
尝试蒸馏更大模型到小模型（痛点：大模型难调参，训练成本更高，部署上线还是得小模型）
尝试LoRA的变体
尝试调参（试过optuna自动搜索，效果也不太好；一般就调lr, epoch, rank）
3）重量级
Base模型，领域数据增量预训练后，再进行指令微调
方案待验证 （若验证成功，其好处是训出来的基座在各个领域任务上的微调都能提点）
这边尝试了在Qwen2-7B-Instruct的领域数据指令微调后的模型，微调效果反而比直接微调Qwen2-7B-Instruct效果差些。由于不清楚该模型训练步骤细节，所以原因尚不明确）
第一优先级，还是搞数据。其次，才是尝试各种方案的加加减减。
4、注意点
学习率：训练的参数量越大，学习率适当要调小
标签噪声：样本标注错误，需要在错误分析时进行剔除和校正
分类业务规则：复杂场景，需要提前确定好完备的标注规则，避免返工（那些模型可以做，那么模型不能做）
待改进点：
文本动态Padding
分类头多标签版本效果未验证成功
二、文本分类-从BERT到LLM
Qwen2ForSequenceClassification和LlamaForSequenceClassification，以及BERTForSequenceClassification，都可以用来完成文本分类任务。
都可以通过HuggingFace transformers的AutoModelForSequenceClassification库，自动加载相应的模型类，进行序列分类任务。
Qwen2ForSequenceClassification和BERTForSequenceClassification，逻辑上是一致的。都是在模型的输出层，加上一个Linear层，用来完成分类任务。
之前在，BERT上做的所有改动，都可以迁移到LLM上。譬如，BERT-CRF、BERT-SUM。
2.1 BERTForSequenceClassification
BertForSequenceClassification是一个已经实现好的用来进行文本分类的类，继承自BertPreTrainedModel，一般用来进行文本分类任务。
通过num_labels传递分类的类别数，从构造函数可以看出这个类大致由3部分组成，1个是BertModel，1个是Dropout，1个是用于分类的线性分类器Linear。
class BertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config):
super(BertForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
python
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
self.init_weights()
Bert用于提取文本特征进行Embedding，Dropout防止过拟合，Linear是一个弱分类器，进行分类，如果需要用更复杂的网络结构进行分类可以参考它进行改写。
forward()函数里面已经定义了损失函数，训练时可以不用自己额外实现，返回值包括4个内容
def forward(...):
...
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), logits, (hidden_states), (attentions)
2.2 Qwen2ForSequenceClassification
接下来。看看Qwen2ForSequenceClassification。
Qwen2ForSequenceClassification(
(model): Qwen2Model(
(embed_tokens): Embedding(151936, 1024, padding_idx=151643)
(layers): ModuleList(
(0-23): 24 x Qwen2DecoderLayer(
(self_attn): Qwen2SdpaAttention(
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(o_proj): Linear(in_features=1024, out_features=1024, bias=False)
(rotary_emb): Qwen2RotaryEmbedding()
)
(mlp): Qwen2MLP(
(gate_proj): Linear(in_features=1024, out_features=2816, bias=False)
(up_proj): Linear(in_features=1024, out_features=2816, bias=False)
(down_proj): Linear(in_features=2816, out_features=1024, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen2RMSNorm()
(post_attention_layernorm): Qwen2RMSNorm()
)
)
(norm): Qwen2RMSNorm()
)
(score): Linear(in_features=1024, out_features=3, bias=False)
)
Qwen2官方代码实现，内置三种模式：
single_label_classification 单标签分类
损失为CrossEntropyLoss
取单标签对应logit，算负对数似然
multi_label_classification 多标签分类
损失为BCEWithLogitsLoss
标签为muilti-hot, 预测logits计算sigmoid，实际取对应维度标签Logit，损失求和
regression 回归
损失为MSELoss
默认为单维度回归（回归可以作为奖励模型，预测打分）
这3种模式的输入标签不同。
class Qwen2ForSequenceClassification(Qwen2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Qwen2Model(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(logits.device)
else:
sequence_lengths = -1
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
三、LoRA微调 Qwen2ForSequenceClassification
在LoRA微调后，将合并LoRA权重，并存储模型。 因为，目前PEFT代码没有，把分类头Linear层的参数存储下来。只靠LoRA权重无法，复现训练的Qwen2ForSequenceClassification模型。
有需要可以小改下代码
这边在modelscope提供的环境，完成了代码的测试。
from modelscope import AutoModelForCausalLM, AutoTokenizer
model_name_or_path = "qwen/Qwen2.5-3B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
prompt = "不想学习怎么办？有兴趣，但是拖延症犯了"
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs, max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```shell
Downloading [config.json]: 100%|██████████| 661/661 [00:00<00:00, 998B/s]
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Downloading [generation_config.json]: 100%|██████████| 242/242 [00:00<00:00, 557B/s]
Downloading [LICENSE]: 100%|██████████| 7.21k/7.21k [00:00<00:00, 11.7kB/s]
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Downloading [model-00001-of-00002.safetensors]: 100%|██████████| 3.70G/3.70G [00:10<00:00, 373MB/s]
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Downloading [model.safetensors.index.json]: 100%|██████████| 34.7k/34.7k [00:00<00:00, 56.8kB/s]
Downloading [README.md]: 100%|██████████| 4.79k/4.79k [00:00<00:00, 10.3kB/s]
Downloading [tokenizer.json]: 100%|██████████| 6.71M/6.71M [00:00<00:00, 8.58MB/s]
Downloading [tokenizer_config.json]: 100%|██████████| 7.13k/7.13k [00:00<00:00, 13.8kB/s]
Downloading [vocab.json]: 100%|██████████| 2.65M/2.65M [00:00<00:00, 5.09MB/s]
/usr/local/lib/python3.10/site-packages/accelerate/utils/modeling.py:1405: UserWarning: Current model requires 234882816 bytes of buffer for offloaded layers, which seems does not fit any GPU's remaining memory. If you are experiencing a OOM later, please consider using offload_buffers=True.
warnings.warn(
面对兴趣与拖延之间的矛盾，确实会让人感到困扰。这里有一些建议或许能帮助你克服拖延，更好地坚持学习：
设定小目标：将大目标分解为一系列小目标。完成每一个小目标都是一次小小的胜利，这可以增加你的动力和成就感。
制定计划：为自己规划一个详细的学习计划，并尽量按照计划执行。记得为休息时间留出空间，保持良好的工作与休息平衡。
保持积极心态：对自己保持耐心和理解，不要因为一时的困难而放弃。记住，进步的过程就是成长的过程。
由于modelscope不支持LORA, 这边查看了本地路径
print(model.model_dir)
/mnt/workspace/.cache/modelscope/hub/qwen/Qwen2___5-3B-Instruct
查看文件
config.json
merges.txt
README.md
configuration.json
model-00001-of-00002.safetensors
tokenizer_config.json
generation_config.json
model-00002-of-00002.safetensors
tokenizer.json
LICENSE
model.safetensors.index.json
vocab.json
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
微调代码
### 初始化设定和随机种子
import os
os.environ["CUDAVISIBLE_DEVICES"] = "0"
import torch
import numpy as np
import pandas as pd
import random
seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
基于prompt用大模型构造了20个样本。
import json
x = '''这基金表现也太差了吧，买了半年了还亏着呢。
管理费收得比别的基金都高，感觉就是在给基金公司打工。
想查查具体投了啥，结果发现透明度低得要命，啥也看不清楚。
基金经理换来换去的，都不知道到底谁在管我的钱。
客服电话打过去半天才有人接，问个问题还得等上好几天才有回复。
市场稍微有点风吹草动，这基金就跌得比谁都快。
投资组合里全是同一行业的股票，风险大得让人睡不着觉。
长期持有也没见赚多少钱，还不如存银行定期。
分红政策一会儿一个样，根本没法做财务规划。
当初宣传时说得好听，实际操作起来完全不是那么回事。'''
x_samples = x.split("\n")
y = '''这基金真的稳啊，买了之后收益一直挺不错的，感觉很靠谱！
管理团队超级专业，每次市场波动都能及时调整策略，让人放心。
透明度很高，随时都能查到投资组合的情况，心里有数。
基金经理经验老道，看准了几个大机会，赚了不少。
客服态度特别好，有问题总能很快得到解答，服务真是没得说。
即使在市场不好的时候，这基金的表现也比大多数同类产品强。
分散投资做得很好，风险控制得很到位，睡个安稳觉没问题。
长期持有的话，回报率真的非常可观，值得信赖。
分红政策明确而且稳定，每年都能按时收到分红，计划财务很方便。
宣传时承诺的那些好处都实现了，真心觉得选对了这只基金。'''
y_samples = y.split("\n")
# 创建一个Python字典
x_data = [{"content": i, "label": 0, "标注类别": "正向"} for i in x_samples]
y_data = [{"content": i, "label": 1, "标注类别": "负向"} for i in y_samples]
def save_json(path, data):
# 将Python字典转换为JSON字符串
with open(path, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=4)
save_json('data/classify_train.json', x_data[:6]+y_data[:6])
save_json('data/classify_valid.json', x_data[6:8]+y_data[6:8])
save_json('data/classify_test.json', x_data[8:]+y_data[8:])
数据加载
import json
from tqdm import tqdm
from loguru import logger
from datasets import Dataset, load_dataset
def get_dataset_from_json(json_path, cols):
with open(json_path, "r") as file:
data = json.load(file)
df = pd.DataFrame(data)
dataset = Dataset.from_pandas(df[cols], split='train')
return dataset
# load_dataset加载json的dataset太慢了
cols = ['content', 'label', '标注类别']
train_ds = get_dataset_from_json('data/classify_train.json', cols)
logger.info(f"TrainData num: {len(train_ds)}")
valid_ds = get_dataset_from_json('data/classify_valid.json', cols)
logger.info(f"ValidData num: {len(valid_ds)}")
test_ds = get_dataset_from_json('data/classify_test.json', cols)
logger.info(f"TestData num: {len(test_ds)}")
print(train_ds[0])
{'content': '这基金表现也太差了吧，买了半年了还亏着呢。', 'label': 0, '标注类别': '正向'}
准备dataset(简单实现截断和padding, 无动态padding)
id2label = {0: "正向", 1: "负向"}
label2id = {v:k for k,v in id2label.items()}
from transformers import AutoTokenizer, DataCollatorWithPadding
# from modelscope import AutoTokenizer, DataCollatorwithPadding
model_name_or_path = "/mnt/workspace/.cache/modelscope/hub/qwen/Qwen2___5-3B-Instruct"
model_name = model_name_or_path.split("/")[-1]
print(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side='left')
tokenizer.add_special_tokens({'pad_token': '<|endoftext|>'})
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
MAX_LEN = 24
txt_colname = 'content'
def preprocess_function(examples):
# padding后处理效率不高，需要动态batch padding
return tokenizer(examples[txt_colname], max_length=MAX_LEN, padding=True, truncation=True)
tokenized_train = train_ds.map(preprocess_function, num_proc=64, batched=True)
tokenized_valid = valid_ds.map(preprocess_function, num_proc=64, batched=True)
sklearn评测代码
from sklearn.metrics import (
classification_report,
confusion_matrix,
accuracy_score,
f1_score,
precision_score,
recall_score
)
def evals(test_ds, model):
k_list = [x[txt_colname] for x in test_ds]
model.eval()
k_result = []
for idx, txt in tqdm(enumerate(k_list)):
model_inputs = tokenizer([txt], max_length=MAX_LEN, truncation=True, return_tensors="pt").to(model.device)
logits = model(**model_inputs).logits
res = int(torch.argmax(logits, axis=1).cpu())
k_result.append(id2label.get(res))
y_true = np.array(test_ds['label'])
y_pred = np.array([label2id.get(x) for x in k_result])
return y_true, y_pred
def compute_metrics(eval_pred):
predictions, label = eval_pred
predictions = np.argmax(predictions, axis=1)
return {"f1": f1_score(y_true=label, y_pred=predictions, average='weighted')}
def compute_valid_metrics(eval_pred):
predictions, label = eval_pred
y_true, y_pred = label, predictions
accuracy = accuracy_score(y_true, y_pred)
print(f'Accuracy: {accuracy}')
metric_types = ['micro', 'macro', 'weighted']
for metric_type in metric_types:
precision = precision_score(y_true, y_pred, average=metric_type)
recall = recall_score(y_true, y_pred, average=metric_type)
f1 = f1_score(y_true, y_pred, average=metric_type)
print(f'{metric_type} Precision: {precision}')
print(f'{metric_type} Recall: {recall}')
print(f'{metric_type} F1 Score: {f1}')
模型加载，使用Trainer进行训练
import torch
from transformers import AutoModelForSequenceClassification
from transformers import Trainer, TrainingArguments
from peft import get_peft_config, PeftModel, PeftConfig, get_peft_model, LoraConfig, TaskType
rank = 64
alpha = rank*2
training_args = TrainingArguments(
output_dir=f"./output/{model_name}/seqence_classify/",
learning_rate=5e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=4,
num_train_epochs=3,
weight_decay=0.01,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True
)
peft_config = LoraConfig(
task_type=TaskType.SEQ_CLS,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
inference_mode=False,
r=rank,
lora_alpha=alpha,
lora_dropout=0.1
)
model = AutoModelForSequenceClassification.from_pretrained(
model_name_or_path,
num_labels=len(id2label),
id2label=id2label,
label2id=label2id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
attn_implementation="flash attention2"
)
model.config.pad_token_id = tokenizer.pad_token_id
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_valid,
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics
)
logger.info(f"start Trainingrank: {rank}")
trainer.train()
logger.info(f"Valid Set, rank: {rank}")
y_true, y_pred = evals(valid_ds, model)
metrics = compute_valid_metrics((y_pred, y_true))
logger.info(metrics)
logger.info(f"Test Set, rank: {rank}")
y_true, y_pred = evals(test_ds, model)
metrics = compute_valid_metrics((y_pred, y_true))
logger.info(metrics)
saved_model = model.merge_and_unload()
saved_model.save_pretrained('/model/qwen2-3b/seqcls')
将LoraConfig和get_peft_model去掉，就是SFT的代码。
model的结构
PeftModelForSequenceClassification(
(base_model): LoraModel(
(model): Qwen2ForSequenceClassification(
(model): Qwen2Model(
(embed_tokens): Embedding(151936, 2048)
(layers): ModuleList(
(0-35): 36 x Qwen2DecoderLayer(
(self_attn): Qwen2SdpaAttention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=True)
(k_proj): Linear(in_features=2048, out_features=256, bias=True)
(v_proj): Linear(in_features=2048, out_features=256, bias=True)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(rotary_emb): Qwen2RotaryEmbedding()
)
(mlp): Qwen2MLP(
(gate_proj): Linear(in_features=2048, out_features=11008, bias=False)
(up_proj): Linear(in_features=2048, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=2048, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
(post_attention_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)
)
)
(norm): Qwen2RMSNorm((2048,), eps=1e-06)
)
(score): Linear(in_features=2048, out_features=2, bias=False)
)
)
)
预测
txt = "退钱，什么辣鸡基金"
model_inputs = tokenizer([txt], max_length=MAX_LEN, truncation=True, return_tensors="pt").to(saved_model.device)
logits = saved_model(**model_inputs).logits
res = int(torch.argmax(logits, axis=1).cpu())
print(id2label[res])
负向
output输出类型
SequenceClassifierOutputWithPast(loss=None, logits=tensor([[-0.1387, 2.3438]], device='cuda:0', grad_fn=<IndexBackward0>), past_key_values=((tensor([[[[ -3.3750, 0.3164, 2.3125, ..., 56.5000, 26.0000, 87.0000],
[ -4.6875, 3.0312, 0.6875, ..., 57.7500, 24.3750, 86.0000],
[ -0.7109, 1.1094, -0.7383, ..., 56.7500, 24.8750, 86.5000],
...,
...,
[-0.2188, 0.2148, 0.4375, ..., -0.1016, 0.9336, -1.1016],
[ 1.3281, 0.3359, 1.3125, ..., -0.3906, 0.0312, -0.0391],
[ 0.8789, 0.5312, 1.4297, ..., 0.1797, -0.9609, -0.6445]]]],
device='cuda:0'))), hidden_states=None, attentions=None)
跑测结果
Some weights of Qwen2ForSequenceClassification were not initialized from the model checkpoint at /mnt/workspace/.cache/modelscope/hub/qwen/Qwen2___5-3B-Instruct and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Detected kernel version 4.19.91, 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.
2024-10-09 23:54:07.615 | INFO | __main__:<module>:53 - start Trainingrank: 64
trainable params: 119,738,368 || all params: 3,205,681,152 || trainable%: 3.7352
[6/6 00:08, Epoch 3/3]
Epoch Training Loss Validation Loss F1
1 No log 0.988281 0.333333
2 No log 0.527344 0.733333
3 No log 0.453125 1.000000
2024-10-09 23:54:17.371 | INFO | __main__:<module>:56 - Valid Set, rank: 64
4it [00:00, 8.03it/s]
2024-10-09 23:54:17.896 | INFO | __main__:<module>:59 - None
2024-10-09 23:54:17.897 | INFO | __main__:<module>:61 - Test Set, rank: 64
Accuracy: 1.0
micro Precision: 1.0
micro Recall: 1.0
micro F1 Score: 1.0
macro Precision: 1.0
macro Recall: 1.0
macro F1 Score: 1.0
weighted Precision: 1.0
weighted Recall: 1.0
weighted F1 Score: 1.0
4it [00:00, 13.58it/s]
2024-10-09 23:54:18.218 | INFO | __main__:<module>:64 - None
Accuracy: 0.75
micro Precision: 0.75
micro Recall: 0.75
micro F1 Score: 0.75
macro Precision: 0.8333333333333333
macro Recall: 0.75
macro F1 Score: 0.7333333333333334
weighted Precision: 0.8333333333333333
weighted Recall: 0.75
weighted F1 Score: 0.7333333333333334
四、自测结果
4.1 短文本
常用的对话中，客户的单轮query, 情感极性分类。
3分类，长度最大128字，训练样本量6K左右
query ，比不过基础的BERT
Accuracy:0.9334389857369255
microPrecision:0.9334389857369255
microRecall:0.9334389857369255
micro F1Score:0.9334389857369255
macro Precision:0.9292774942877138
macro Reca1l:0.9550788300142491
macro F1Score:0.9388312342456646
weightedPrecision:0.9418775412386249
weighted Recall:0.9334389857369255
weighted F1Score:0.93383533375322
precision recall fi-score support
0 1.00 0.88 0.93 334
1 0.94 0.99 0.97 101
2 0.85 0.99 0.92 196
accuracy 0.93
macro avg 0.93
weightedavg 0.94
使用Chinese-RoBerta-large-wwm，及其各种变体进行比较。7B、3B、1.5B和0.5B，均无优势。比不过Large、Base。一些裁减参数量就几十M的都能到85左右，所以看不出LLM的优势。
结论：
短文本场景，LLM的优势在于少样本、样本不均匀，以及基于prompt+fewshot用72B规模生成伪标签。
除非样本量上万，且价值比较大的场景，可以尝试14B以上模型，调参确认提点后，再进行蒸馏。
绝大部分短文本场景没有必要用到大模型，除非是生成场景，比如query扩写、query多轮改写。
4.2 长文本
这里用到的是ASR转译文本
训练集4918样本，平均长度740字，最大4631字，75% 918字
LoRA微调结果，一般2个epoch效果好些，rank要适当调参。
epoch=1， rank=96, alpha=2*rank
Accuracy:0.8415637860082305
micro Precision:0.8415637860082305
micro Recall:0.8415637860082305
micro F1 Score:0.8415637860082305
macro Precision:0.8075007129137883
macro Recall: 0.770659344467927
macroF1 Score:0.7726373117446225
weightedPrecision:0.8509932419375813
weighted Recall:0.8415637860082305
weighted F1Score:0.8420807262647815
precision recall f1-score support
0 0.95 0.83 0.89 163
1 0.76 0.77 0.77 66
2 0.78 0.89 0.83 63
3 0.81 0.81 0.81 42
4 0.80 0.93 0.86 30
5 0.48 0.56 0.51 18
6 1.00 0.43 0.60 7
7 0.88 0.95 0.92 97
epoch=3，rank=96, alpha=2*rank
Accuracy:0.8847736625514403
micro Precision:0.8847736625514403
micro Recall:0.8847736625514403
micro F1 Score:0.8847736625514403
macro Precision:0.8765027065399982
macroRecall:0.8400805218716799
macro F1 Score:.8527883278910355
weighted Precision:0.8903846924862034
weighted Recall:0.8847736625514403
weighted F1 Score:0.8852820009557909
precision recall fl-score support
0 0.94 0.89 0.91 163
1 0.77 0.85 0.81 66
2 0.81 0.88 0.83 42
3 0.79 0.90 0.86 63
4 1.00 0.93 0.97 30
5 0.92 0.61 0.73 18
6 0.83 0.71 0.77 7
7 0.96 0.94 0.95 97
五、相关资料
比较详细的LLM分类头微调经验 ，十分推荐看看
邱震宇：大模型在传统NLP任务的使用姿势探讨https://zhuanlan.zhihu.com/p/704983302
在灾难推文分析场景上比较用 LoRA 微调 Roberta、Llama 2 和 Mistral 的过程及表现https://segmentfault.com/a/1190000044485544
SFT分类头微调代码（其实就是去掉LoRA那几行代码）https://github.com/muyaostudio/qwen2_seq_cls
知乎上的一个代码
郝可爱：使用LlamaForSequenceClassification构建文本分类模型https://zhuanlan.zhihu.com/p/691459595
相关代码可以在github上找找，kaggle也推荐去，主要就是这2个地方。
