本文主要是介绍1 NLP分类之：FastText，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
0 数据
https://download.csdn.net/download/qq_28611929/88580520?spm=1001.2014.3001.5503
数据集合：0 NLP: 数据获取与EDA-CSDN博客
词嵌入向量文件： embedding_SougouNews.npz
词典文件：vocab.pkl
1 模型
基于fastText做词向量嵌入然后引入2-gram, 3-gram扩充，最后接入一个MLP即可；
fastText 是一个由 Facebook AI Research 实现的开源库，用于进行文本分类和词向量学习。它结合了传统的词袋模型和神经网络的优点，能够快速训练大规模的文本数据。
fastText 的主要特点包括：
1. 快速训练：fastText 使用了层次化 Softmax 和负采样等技术，大大加快了训练速度。
2. 子词嵌入：fastText 将单词表示为字符级别的 n-gram，并将其视为单词的子词。这样可以更好地处理未登录词和稀有词。
3. 文本分类：fastText 提供了一个简单而高效的文本分类接口，可以用于训练和预测多类别文本分类任务。
4. 多语言支持：fastText 支持多种语言，并且可以通过学习共享词向量来提高跨语言任务的性能。
需要注意的是，fastText 主要适用于文本分类任务，对于其他类型的自然语言处理任务（如命名实体识别、机器翻译等），可能需要使用其他模型或方法。
2 代码
nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
`nn.Embedding.from_pretrained` 是 PyTorch 中的一个函数，用于从预训练的词向量加载 Embedding 层的权重。
在使用 `nn.Embedding.from_pretrained` 时，你需要提供一个预训练的词向量矩阵作为参数，
freeze 参数： 指定是否冻结该层的权重。预训练的词向量可以是从其他模型（如 Word2Vec 或 GloVe）中得到的。
y = nn.Embedding.from_pretrained（x）x输入：词的索引
y返回： 词向量
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pickle as pkl
from tqdm import tqdm
import time
from torch.utils.data import Datasetfrom datetime import timedelta
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader
from collections import defaultdict
from torch.optim import AdamWUNK, PAD = '<UNK>', '<PAD>' # 未知字，padding符号
RANDOM_SEED = 2023file_path = "./data/online_shopping_10_cats.csv"
vocab_file = "./data/vocab.pkl"
emdedding_file = "./data/embedding_SougouNews.npz"
vocab = pkl.load(open(vocab_file, 'rb'))class MyDataSet(Dataset):def __init__(self, df, vocab,pad_size=None):self.data_info = dfself.data_info['review'] = self.data_info['review'].apply(lambda x:str(x).strip())self.data_info = self.data_info[['review','label']].valuesself.vocab = vocab self.pad_size = pad_sizeself.buckets = 250499 def biGramHash(self,sequence, t):t1 = sequence[t - 1] if t - 1 >= 0 else 0return (t1 * 14918087) % self.bucketsdef triGramHash(self,sequence, t):t1 = sequence[t - 1] if t - 1 >= 0 else 0t2 = sequence[t - 2] if t - 2 >= 0 else 0return (t2 * 14918087 * 18408749 + t1 * 14918087) % self.bucketsdef __getitem__(self, item):result = {}view, label = self.data_info[item]result['view'] = view.strip()result['label'] = torch.tensor(label,dtype=torch.long)token = [i for i in view.strip()]seq_len = len(token)# 填充if self.pad_size:if len(token) < self.pad_size:token.extend([PAD] * (self.pad_size - len(token)))else:token = token[:self.pad_size]seq_len = self.pad_sizeresult['seq_len'] = seq_len# 词表的转换words_line = []for word in token:words_line.append(self.vocab.get(word, self.vocab.get(UNK)))result['input_ids'] = torch.tensor(words_line, dtype=torch.long) # bigram = []trigram = []for i in range(self.pad_size):bigram.append(self.biGramHash(words_line, i))trigram.append(self.triGramHash(words_line, i))result['bigram'] = torch.tensor(bigram, dtype=torch.long)result['trigram'] = torch.tensor(trigram, dtype=torch.long)return resultdef __len__(self):return len(self.data_info)
df = pd.read_csv("./data/online_shopping_10_cats.csv")#myDataset[0]
df_train, df_test = train_test_split(df, test_size=0.1, random_state=RANDOM_SEED)
df_val, df_test = train_test_split(df_test, test_size=0.5, random_state=RANDOM_SEED)
df_train.shape, df_val.shape, df_test.shapedef create_data_loader(df,vocab,pad_size,batch_size=4):ds = MyDataSet(df,vocab,pad_size=pad_size)return DataLoader(ds,batch_size=batch_size)MAX_LEN = 256
BATCH_SIZE = 4
train_data_loader = create_data_loader(df_train,vocab,pad_size=MAX_LEN, batch_size=BATCH_SIZE)
val_data_loader = create_data_loader(df_val,vocab,pad_size=MAX_LEN, batch_size=BATCH_SIZE)
test_data_loader = create_data_loader(df_test,vocab,pad_size=MAX_LEN, batch_size=BATCH_SIZE)class Config(object):"""配置参数"""def __init__(self):self.model_name = 'FastText'self.embedding_pretrained = torch.tensor(np.load("./data/embedding_SougouNews.npz")["embeddings"].astype('float32')) # 预训练词向量self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 设备self.dropout = 0.5 # 随机失活self.require_improvement = 1000 # 若超过1000batch效果还没提升，则提前结束训练self.num_classes = 2 # 类别数self.n_vocab = 0 # 词表大小，在运行时赋值self.num_epochs = 20 # epoch数self.batch_size = 128 # mini-batch大小self.learning_rate = 1e-4 # 学习率self.embed = self.embedding_pretrained.size(1)\if self.embedding_pretrained is not None else 300 # 字向量维度self.hidden_size = 256 # 隐藏层大小self.n_gram_vocab = 250499 # ngram 词表大小class Model(nn.Module):def __init__(self, config):super(Model, self).__init__()if config.embedding_pretrained is not None:self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)else:self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)self.embedding_ngram2 = nn.Embedding(config.n_gram_vocab, config.embed)self.embedding_ngram3 = nn.Embedding(config.n_gram_vocab, config.embed)self.dropout = nn.Dropout(config.dropout)self.fc1 = nn.Linear(config.embed * 3, config.hidden_size)# self.dropout2 = nn.Dropout(config.dropout)self.fc2 = nn.Linear(config.hidden_size, config.num_classes)def forward(self, x):out_word = self.embedding(x['input_ids'])out_bigram = self.embedding_ngram2(x['bigram'])out_trigram = self.embedding_ngram3(x['trigram'])out = torch.cat((out_word, out_bigram, out_trigram), -1)out = out.mean(dim=1)out = self.dropout(out)out = self.fc1(out)out = F.relu(out)out = self.fc2(out)return outconfig = Config()
model = Model(config)device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)EPOCHS = 5 # 训练轮数
optimizer = AdamW(model.parameters(),lr=2e-4)
total_steps = len(train_data_loader) * EPOCHS
# schedule = get_linear_schedule_with_warmup(optimizer,num_warmup_steps=0,
# num_training_steps=total_steps)
loss_fn = nn.CrossEntropyLoss().to(device)def train_epoch(model,data_loader,loss_fn,device,n_exmaples,schedule=None):model = model.train()losses = []correct_predictions = 0for d in tqdm(data_loader):# input_ids = d['input_ids'].to(device)# attention_mask = d['attention_mask'].to(device)targets = d['label']#.to(device)outputs = model(d)_,preds = torch.max(outputs, dim=1)loss = loss_fn(outputs,targets)losses.append(loss.item())correct_predictions += torch.sum(preds==targets)loss.backward()nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)optimizer.step()#scheduler.step()optimizer.zero_grad()return correct_predictions.double() / n_examples, np.mean(losses)def eval_model(model, data_loader, loss_fn, device, n_examples):model = model.eval() # 验证预测模式losses = []correct_predictions = 0with torch.no_grad():for d in data_loader:targets = d['label']#.to(device)outputs = model(d)_, preds = torch.max(outputs, dim=1)loss = loss_fn(outputs, targets)correct_predictions += torch.sum(preds == targets)losses.append(loss.item())return correct_predictions.double() / n_examples, np.mean(losses)# train model
EPOCHS = 5
history = defaultdict(list) # 记录10轮loss和acc
best_accuracy = 0for epoch in range(EPOCHS):print(f'Epoch {epoch + 1}/{EPOCHS}')print('-' * 10)train_acc, train_loss = train_epoch(model,train_data_loader,loss_fn,optimizer,device,len(df_train))print(f'Train loss {train_loss} accuracy {train_acc}')val_acc, val_loss = eval_model(model,val_data_loader,loss_fn,device,len(df_val))print(f'Val loss {val_loss} accuracy {val_acc}')print()history['train_acc'].append(train_acc)history['train_loss'].append(train_loss)history['val_acc'].append(val_acc)history['val_loss'].append(val_loss)if val_acc > best_accuracy:torch.save(model.state_dict(), 'best_model_state.bin')best_accuracy = val_acc备注： CPU训练模型很慢啊！！！
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