Qwen2ForSequenceClassification文本分类实战和经验分享

本文主要使用Qwen2ForSequenceClassification实现文本分类任务。

文章首发于我的知乎：https://zhuanlan.zhihu.com/p/17468021019

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
    • 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）
  • 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

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）

二、文本分类-从BERT到LLM

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。

和BERTForSequenceClassification一样。

BertForSequenceClassification是一个已经实现好的用来进行文本分类的类，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Bert，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
pythonself.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 regressionloss_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,) + outputsreturn outputs  # (loss), logits, (hidden_states), (attentions)

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_labelsself.model = Qwen2Model(config)self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)# Initialize weights and apply final processingself.post_init()def get_input_embeddings(self):return self.model.embed_tokensdef 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_dicttransformer_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 = -1else:if input_ids is not None:# if no pad token found, use modulo instead of reverse indexing for ONNX compatibilitysequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1sequence_lengths = sequence_lengths % input_ids.shape[-1]sequence_lengths = sequence_lengths.to(logits.device)else:sequence_lengths = -1pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]loss = Noneif 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 outputreturn 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, AutoTokenizermodel_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]
Downloading [configuration.json]: 100%|██████████| 2.00/2.00 [00:00<00:00, 2.30B/s]
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]
Downloading [merges.txt]: 100%|██████████| 1.59M/1.59M [00:00<00:00, 3.01MB/s]
Downloading [model-00001-of-00002.safetensors]: 100%|██████████| 3.70G/3.70G [00:10<00:00, 373MB/s] 
Downloading [model-00002-of-00002.safetensors]: 100%|██████████| 2.05G/2.05G [00:06<00:00, 332MB/s] 
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：

1. 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。

2. 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。

3. 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 randomseed = 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 jsonx = '''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_datasetdef 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, DataCollatorwithPaddingmodel_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 paddingreturn 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).logitsres = 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_preddef compute_metrics(eval_pred):predictions, label = eval_predpredictions = 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_predy_true, y_pred = label, predictionsaccuracy = 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, TaskTyperank = 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_idmodel = 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

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BERTAccuracy: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:0.93383533375322precision 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。

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:0.8420807262647815precision 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:0.8852820009557909precision 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

相关资料

• 比较详细的LLM分类头微调经验
  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
  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。