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自学笔记}/源代码/tensorflow2.x/zh-NER-keras-master/process_data.py (100%) rename 机器学习/{殷康龙 => 自学笔记}/源代码/tensorflow2.x/zh-NER-keras-master/train.py (100%) rename 机器学习/{殷康龙 => 自学笔记}/源代码/tensorflow2.x/zh-NER-keras-master/val.py (100%) rename 机器学习/{殷康龙 => 自学笔记}/联邦学习/联邦学习.xmind (100%) diff --git a/人工智能/transformer/PEFT教程.md b/人工智能/transformer/PEFT教程.md new file mode 100644 index 00000000..812863b1 --- /dev/null +++ b/人工智能/transformer/PEFT教程.md @@ -0,0 +1,148 @@ + +## 说明 +参数高效微调（PEFT）方法在微调过程中冻结预训练模型的参数，并在其顶部添加少量可训练参数（adapters）。adapters被训练以学习特定任务的信息。这种方法已被证明非常节省内存，同时具有较低的计算使用量，同时产生与完全微调模型相当的结果。 + + +## 使用 + +### 安装 +``` +pip install peft + +``` +### 加载adapter + +要从huggingface的Transformers库中加载并使用PEFTadapter模型，请确保Hub仓库或本地目录包含一个adapter_config.json文件和adapter权重，如上例所示。然后，您可以使用AutoModelFor类加载PEFT adapter模型。例如，要为因果语言建模加载一个PEFT adapter模型： + +指定PEFT模型id +将其传递给AutoModelForCausalLM类 +``` +from transformers import AutoModelForCausalLM, AutoTokenizer + +peft_model_id = "ybelkada/opt-350m-lora" +model = AutoModelForCausalLM.from_pretrained(peft_model_id) +``` +您也可以通过load_adapter方法来加载 PEFT adapter。 + +``` +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_id = "facebook/opt-350m" +peft_model_id = "ybelkada/opt-350m-lora" + +model = AutoModelForCausalLM.from_pretrained(model_id) +model.load_adapter(peft_model_id) +``` + +基于8bit或4bit进行加载 +bitsandbytes集成支持8bit和4bit精度数据类型，这对于加载大模型非常有用，因为它可以节省内存（请参阅bitsandbytes指南以了解更多信息）。要有效地将模型分配到您的硬件，请在from_pretrained()中添加load_in_8bit或load_in_4bit参数，并将device_map="auto"设置为： + +``` +from transformers import AutoModelForCausalLM, AutoTokenizer + +peft_model_id = "ybelkada/opt-350m-lora" +model = AutoModelForCausalLM.from_pretrained(peft_model_id, device_map="auto", load_in_8bit=True) +``` + +### 添加adapter + +你可以使用~peft.PeftModel.add_adapter方法为一个已有adapter的模型添加一个新的adapter，只要新adapter的类型与当前adapter相同即可。例如，如果你有一个附加到模型上的LoRA adapter： + +``` +from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer +from peft import PeftConfig + +model_id = "facebook/opt-350m" +model = AutoModelForCausalLM.from_pretrained(model_id) + +lora_config = LoraConfig( + target_modules=["q_proj", "k_proj"], + init_lora_weights=False +) + +model.add_adapter(lora_config, adapter_name="adapter_1") +``` +添加一个新的adapter： + +``` +# attach new adapter with same config +model.add_adapter(lora_config, adapter_name="adapter_2") +``` +现在您可以使用~peft.PeftModel.set_adapter来设置要使用的adapter。 + +``` +# use adapter_1 +model.set_adapter("adapter_1") +output = model.generate(**inputs) +print(tokenizer.decode(output_disabled[0], skip_special_tokens=True)) + +# use adapter_2 +model.set_adapter("adapter_2") +output_enabled = model.generate(**inputs) +print(tokenizer.decode(output_enabled[0], skip_special_tokens=True)) +``` + +### 启用和禁用adapters +一旦您将adapter添加到模型中，您可以启用或禁用adapter模块。要启用adapter模块： + +``` +from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer +from peft import PeftConfig + +model_id = "facebook/opt-350m" +adapter_model_id = "ybelkada/opt-350m-lora" +tokenizer = AutoTokenizer.from_pretrained(model_id) +text = "Hello" +inputs = tokenizer(text, return_tensors="pt") + +model = AutoModelForCausalLM.from_pretrained(model_id) +peft_config = PeftConfig.from_pretrained(adapter_model_id) + +# to initiate with random weights +peft_config.init_lora_weights = False + +model.add_adapter(peft_config) +model.enable_adapters() +output = model.generate(**inputs) +``` +要禁用adapter模块： + +``` +model.disable_adapters() +output = model.generate(**inputs) +``` + +### 训练一个adapter + +训练一个 PEFT adapter +PEFT适配器受Trainer类支持，因此您可以为您的特定用例训练适配器。它只需要添加几行代码即可。例如，要训练一个LoRA adapter： + +如果你不熟悉如何使用Trainer微调模型，请查看微调预训练模型教程。 + +使用任务类型和超参数定义adapter配置（参见~peft.LoraConfig以了解超参数的详细信息）。 +``` +from peft import LoraConfig + +peft_config = LoraConfig( + lora_alpha=16, + lora_dropout=0.1, + r=64, + bias="none", + task_type="CAUSAL_LM", +) +``` +将adapter添加到模型中。 +``` +model.add_adapter(peft_config) +``` +现在可以将模型传递给Trainer了！ +``` +trainer = Trainer(model=model, ...) +trainer.train() +``` +要保存训练好的adapter并重新加载它： + +``` +model.save_pretrained(save_dir) +model = AutoModelForCausalLM.from_pretrained(save_dir) +``` \ No newline at end of file diff --git a/人工智能/transformer/evaluate教程.md b/人工智能/transformer/evaluate教程.md new file mode 100644 index 00000000..cc813fde --- /dev/null +++ b/人工智能/transformer/evaluate教程.md @@ -0,0 +1,128 @@ +https://blog.csdn.net/muyao987/article/details/125411129 +### 安装 +``` +pip install evaluate +``` + +检查是否安装成功 +``` +python -c "import evaluate; print(evaluate.load('accuracy').compute(references=[1], predictions=[1]))" + +``` + +### load + +加载评估指标 + +evaluate中的每个指标都是一个单独的Python模块，通过 evaluate.load()（点击查看文档） 函数快速加载，其中load函数的常用参数如下： + +* path：必选，str类型。可以是指标名（如 accuracy 或 社区的铁汁们贡献 的 muyaostudio/myeval），如果源码安装还可以是路径名（如./metrics/rouge 或 ./metrics/rogue/rouge.py）。我用的后者，因为直接传指标名会联网下载评价脚本，但单位的网不给力。 +* config_name：可选，str类型。指标的配置（如 GLUE 指标的每个子集都有一个配置） +* module_type：上文三种评价类型之一，默认 metric，可选 comparison 或 measurement +* cache_dir：可选，存储临时预测和引用的路径（默认为 ~/.cache/huggingface/evaluate/) + +``` +import evaluate + +# module_type 默认为 'metric' +accuracy = evaluate.load("accuracy") + +# module_type 显式指定 'metric','comparison','measurement',防止重名。 +word_length = evaluate.load("word_length", module_type="measurement") +``` + +### 列出 + +列出可用指标 +list_evaluation_modules 列出官方（和社区）里有哪些指标，还能看到点赞信息，一共三个参数： + +module_type：要列出的评估模块类型，None是全部，可选metric，comparison，measurement。 +include_community：是否包含社区模块，默认 True 。 +with_details：返回指标的完整详细Dict信息，而不是str类型的指标名。默认 False。 +``` +print(evaluate.list_evaluation_modules( + module_type="measurement", + include_community=True, + with_details=True) +) +``` +评估模块都有的属性 +所有评估模块都附带一系列有用的属性，这些属性有助于使用存储在 evaluate.EvaluationModuleInfo 对象中的模块，属性如下： + +description：指标介绍 +citation：latex参考文献 +features：输入格式和类型，比如predictions、references等 +inputs_description：输入参数描述文档 +homepage：指标官网 +license：指标许可证 +codebase_urls：指标基于的代码地址 +reference_urls：指标的参考地址 + + +### 计算指标 +计算指标值（一次性计算/增量计算） +方式一：一次性计算 +函数：EvaluationModule.compute()，传入list/array/tensor等类型的参数references和predictions。 +``` +>>> import evaluate +>>> metric_name = './evaluate/metrics/accuracy' +>>> accuracy = evaluate.load(metric_name) +>>> accuracy.compute(references=[0,1,0,1], predictions=[1,0,0,1]) + +{'accuracy': 0.5} # 输出结果 +``` +方式二：单增量的增量计算 +函数： EvaluationModule.add()，用于for循环一对一对地里添加ref和pred，添加完退出循环之后统一计算指标。 +``` +>>> for ref, pred in zip([0,0,0,1], [0,0,0,1]): +... accuracy.add(references=ref, predictions=pred) +... +>>> accuracy.compute() + +{'accuracy': 1.0} # 输出结果 +``` + +方式三：多增量的增量计算 +函数： EvaluationModule.add_batch()，用于for循环多对多对地里添加ref和pred（下面例子是一次添加3对），添加完退出循环之后统一计算指标。 +``` +>>> for refs, preds in zip([[0,1],[0,1],[0,1]], [[1,0],[0,1],[0,1]]): +... accuracy.add_batch(references=refs, predictions=preds) +... +>>> accuracy.compute() + +{'accuracy': 0.6666666666666666} # 输出结果 +``` + +### 保存评价结果 +函数：evaluate.save()，参数为 path_or_file， 用于存储文件的路径或文件。如果只提供文件夹，则结果文件将以 result-%Y%m%d-%H%M%S.json的格式保存；可传 dict 类型的关键字参数 **result，**params。 +``` +>>> result = accuracy.compute(references=[0,1,0,1], predictions=[1,0,0,1]) +>>> hyperparams = {"model": "bert-base-uncased"} +>>> evaluate.save("./results/", experiment="run 42", **result, **hyperparams) +``` + +### 自动评估指标 +自动评估 +有点像 Trainer 的封装，可以直接把 evaluate.evaluator() 用做自动评估，且能通过strategy参数的调整来计算置信区间和标准误差，有助于评估值的稳定性： +``` +from transformers import pipeline +from datasets import load_dataset +from evaluate import evaluator +import evaluate + +pipe = pipeline("text-classification", model="lvwerra/distilbert-imdb", device=0) +data = load_dataset("imdb", split="test").shuffle().select(range(1000)) +metric = evaluate.load("accuracy") +results = eval.compute(model_or_pipeline=pipe, data=data, metric=metric, + label_mapping={"NEGATIVE": 0, "POSITIVE": 1}, + strategy="bootstrap", n_resamples=200) + +print(results) +>>> {'accuracy': +... { +... 'confidence_interval': (0.906, 0.9406749892841922), +... 'standard_error': 0.00865213251082787, +... 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