NLP夏令营任务三-大模型解决二分类

最新推荐文章于 2024-11-29 19:46:19 发布

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最新推荐文章于 2024-11-29 19:46:19 发布

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分类专栏： NLP夏令营文章标签：自然语言处理分类 python

本文链接：https://blog.youkuaiyun.com/BIT_mk/article/details/132013053

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3 篇文章

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import pandas as pd
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('testB.csv')
train_df.info()
res = []

for i in range(len(train_df)):
    paper_item = train_df.loc[i]
    tmp = {
    "instruction": "Please judge whether it is a medical field paper according to the given paper title and abstract, output 1 or 0, the following is the paper title, author and abstract -->",
    "input": f"title:{paper_item[1]},abstract:{paper_item[3]}",
    "output": str(paper_item[5])
  }
    res.append(tmp)

import json

with open('paper_label.json', mode='w', encoding='utf-8') as f:
    json.dump(res, f, ensure_ascii=False, indent=4)

import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Device:", device)

torch.cuda.empty_cache()

from peft import PeftModel
from transformers import AutoTokenizer, AutoModel, GenerationConfig, AutoModelForCausalLM

model_path = "THUDM/chatglm2-6b"
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# 加载 label lora权重

model = PeftModel.from_pretrained(model, 'huanhuan-chat/output/label_xfg').half()
model = model.eval()
response, history = model.chat(tokenizer, "你好", history=[])


def predict(text):
    response, history = model.chat(tokenizer, f"Please judge whether it is a medical field paper according to the given paper title and abstract, output 1 or 0, the following is the paper title, author and abstract -->{text}", history=[],
    temperature=0.01)
    return response

predict('title:Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks,author:Li, Chenqi; Lammie, Corey; Dong, Xuening; Amirsoleimani, Amirali; Azghadi, Mostafa Rahimi; Genov, Roman,abstract:During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and areaconstrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of theCNNcomponent of our system. We parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS Deep Learning (DL) accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to lowAnalog-to-Digital Converter (ADC)/Digital-to-Analog Converter (DAC) resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck RON/ROFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791Wof power while occupying an area of 31.255 mm(2) in a 22 nm FDSOI CMOS process.')

# 预测测试集

from tqdm import tqdm

label = []

for i in tqdm(range(len(test_df))):
    test_item = test_df.loc[i]
    test_input = f"title:{test_item[1]},author:{test_item[2]},abstract:{test_item[3]}"
    label.append(int(predict(test_input)))

test_df['label'] = label



submit = test_df[['uuid', 'Keywords', 'label']]

submit.to_csv('submit.csv', index=False)

1-2. 导入 pandas 库并读取 'train.csv' 和 'testB.csv' 这两个 CSV 文件。这些文件应该包含了训练和测试的数据。

查看训练数据的信息，包括每个特征的类型和非空值的数量等。
初始化一个空列表，用于存储转换后的训练数据。

5-14. 遍历训练数据，对每一行数据进行处理。首先获取这一行的数据，然后将数据转换为一个字典，字典的 "instruction" 键对应的值是一个固定的字符串，"input" 键对应的值是由标题和摘要组成的字符串，"output" 键对应的值是这篇文章是否属于医学领域（1 代表是，0 代表不是）。然后将这个字典添加到之前创建的列表中。

15-16. 导入 json 库，用于将数据转换为 JSON 格式。

17-19. 将处理后的数据转换为 JSON 格式并保存到 'paper_label.json' 文件中。