这里dataset是直接读取文本在经过所以加载的Tokenizer处理后的数据,主要的含义如下: input_ids:字的编码 token_type_ids:标识是第一个句子还是第二个句子 attention_mask:标识是不是填充 步骤4:定义Bert模型 由于这里是文本分类任务,所以直接使用BertForSequenceClassification完成加载即可,这里需要制定对应的类别数量。 fro...
fromtorch.utils.dataimportDataset,DataLoader,TensorDataset importnumpyasnp importpandasaspd importrandom importre #划分为训练集和验证集 #stratify按照标签进行采样,训练集和验证部分同分布 x_train,x_test,train_label,test_label=train_test_split(news_text[:], news_label[:],test_size=0.2,stratify=news_l...
在Kaggle的文本分类竞赛中,可以使用Hugging Face的Transformers库中的PreTrainedTokenizer类来进行分词和编码。同时,还可以使用TextClassificationPipeline类来简化数据预处理流程。三、模型训练和调优在数据预处理完成后,就可以开始训练BERT模型了。首先,需要安装Hugging Face的Transformers库,可以使用以下命令进行安装: pip instal...
fig.text(-0.02, 0.5, 'Seconds', va='center', rotation='vertical', fontsize=18) plt.suptitle(f"ID: {os.path.basename(filename)} TARGET: {label}", fontsize=18) fig.tight_layout() plt.show() 下面是自定义数据集对象: class ClassificationDataset: def __init__(self, image_paths, targe...
class TrainDataset(Dataset): def __init__(self, cfg, df): self.cfg = cfg self.texts = df['full_text'].values self.labels = df[cfg.target_cols].values def __len__(self): return len(self.texts) def __getitem__(self, item): ...
from transformers import RobertaModel, RobertaTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding from peft import LoraConfig, get_peft_model from datasets import load_dataset peft_model_name = 'roberta-base-peft' ...
classDatasetRetriever(Dataset):def__init__(self,data,tokenizer,max_len,is_test=False):self.data=dataif'excerpt' in self.data.columns:self.excerpts=self.data.excerpt.values.tolist()else:self.excerpts=self.data.text.values.tolist()self.targets=self.data.target.values.tolist()self.tokenizer=tok...
classClassificationDataset:def__init__(self,image_paths,targets):self.image_paths=image_paths self.targets=targetsdef__len__(self):returnlen(self.image_paths)def__getitem__(self,item):image=np.load(self.image_paths[item]).astype(float)targets=self.targets[item]image=image/np.array([np.abs...
The aim of this repository is to show a baseline model for text classification by implementing a LSTM-based model coded in PyTorch. In order to provide a better understanding of the model, it will be used a Tweets dataset provided by Kaggle. - FernandoLp
(text_dataset,shuffle=False,batch_size=batch_size)classNet(torch.nn.Module):def__init__(self):super(Net, self).__init__()self.l1 = torch.nn.Linear(784,512)self.l2 = torch.nn.Linear(512,256)self.l3 = torch.nn.Linear(256,128)self.l4 = torch.nn.Linear(128,64)self.l5 = torch...