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湖南it网站建设mxtia,网站建设是什么时间段申请域名,网站策划教程,mysql asp网站开发标题引言NLP基础概念什么是自然语言处理#xff1f;NLP的核心任务传统NLP方法词袋模型#xff08;Bag of Words#xff09;TF-IDF#xff08;词频-逆文档频率#xff09;词嵌入#xff1a;词向量的演进Word2VecTransformer架构详解Transformer的革命性自注意力机制位置编…标题引言NLP基础概念什么是自然语言处理NLP的核心任务传统NLP方法词袋模型Bag of WordsTF-IDF词频-逆文档频率词嵌入词向量的演进Word2VecTransformer架构详解Transformer的革命性自注意力机制位置编码完整的Transformer编码器层实战项目文本分类模型预训练语言模型BERT风格的预训练实战项目情感分析系统NLP的未来发展1. 多模态学习2. 少样本和零样本学习总结延伸学习建议引言自然语言处理Natural Language Processing, NLP是人工智能领域中专注于计算机与人类语言交互的分支。随着深度学习技术的发展NLP取得了突破性进展特别是在2017年Transformer架构提出之后。本文将深入探讨NLP的核心概念、技术发展以及Transformer架构的革命性影响。NLP基础概念什么是自然语言处理NLP是让计算机理解、解释和生成人类语言的技术。它包含两个主要方面自然语言理解NLU使计算机能够理解文本含义自然语言生成NLG使计算机能够生成类人文本NLP的核心任务文本分类将文本分配到预定义类别命名实体识别NER识别文本中的实体关系抽取识别实体之间的关系情感分析判断文本的情感倾向机器翻译将文本从一种语言翻译成另一种问答系统根据问题提供答案文本摘要生成文本的简短摘要传统NLP方法词袋模型Bag of Words词袋模型是最简单的文本表示方法忽略了词序信息importnumpyasnpfromcollectionsimportCounterclassBagOfWords:def__init__(self):self.vocabulary{}self.vocabulary_size0deffit(self,documents):构建词汇表word_countsCounter()fordocindocuments:wordsdoc.lower().split()word_counts.update(words)# 只保留最常见的词most_commonword_counts.most_common(5000)# 保留5000个最常见词self.vocabulary{word:idxforidx,(word,_)inenumerate(most_common)}self.vocabulary_sizelen(self.vocabulary)deftransform(self,documents):将文档转换为词袋向量vectors[]fordocindocuments:wordsdoc.lower().split()vectornp.zeros(self.vocabulary_size)word_countCounter(words)forword,countinword_count.items():ifwordinself.vocabulary:idxself.vocabulary[word]vector[idx]count vectors.append(vector)returnnp.array(vectors)# 示例使用documents[I love machine learning,Machine learning is fascinating,I enjoy deep learning,Deep learning is a subset of machine learning]bowBagOfWords()bow.fit(documents)vectorsbow.transform(documents)print(词汇表大小:,bow.vocabulary_size)print(文档向量形状:,vectors.shape)TF-IDF词频-逆文档频率TF-IDF改进了词袋模型考虑了词在文档中的重要性importmathclassTFIDF:def__init__(self):self.vocabulary{}self.idf{}self.vocabulary_size0self.document_count0deffit(self,documents):计算IDF值self.document_countlen(documents)word_document_counts{}all_wordsset()fordocindocuments:wordsset(doc.lower().split())all_words.update(words)forwordinwords:word_document_counts[word]word_document_counts.get(word,0)1# 构建词汇表和IDFself.vocabulary{word:idxforidx,wordinenumerate(all_words)}self.vocabulary_sizelen(self.vocabulary)# 计算IDFforword,doc_countinword_document_counts.items():self.idf[word]math.log(self.document_count/(1doc_count))deftransform(self,documents):将文档转换为TF-IDF向量vectors[]fordocindocuments:wordsdoc.lower().split()word_countCounter(words)total_wordslen(words)vectornp.zeros(self.vocabulary_size)forword,countinword_count.items():ifwordinself.vocabulary:# 计算TFtfcount/total_words# 计算TF-IDFidxself.vocabulary[word]vector[idx]tf*self.idf.get(word,0)vectors.append(vector)returnnp.array(vectors)# 示例使用tfidfTFIDF()tfidf.fit(documents)tfidf_vectorstfidf.transform(documents)print(TF-IDF向量形状:,tfidf_vectors.shape)词嵌入词向量的演进Word2VecWord2Vec通过上下文学习词的分布式表示importnumpyasnpfromcollectionsimportdefaultdictclassWord2Vec:def__init__(self,vector_size100,window5,learning_rate0.025,epochs100):self.vector_sizevector_size self.windowwindow self.learning_ratelearning_rate self.epochsepochs self.word_vectors{}self.vocabset()defbuild_vocab(self,sentences):构建词汇表word_countsdefaultdict(int)forsentenceinsentences:forwordinsentence.split():word_counts[word.lower()]1# 过滤低频词self.vocab{wordforword,countinword_counts.items()ifcount5}# 初始化词向量forwordinself.vocab:self.word_vectors[word]np.random.uniform(-0.5,0.5,self.vector_size)defsigmoid(self,x):Sigmoid激活函数return1/(1np.exp(-np.clip(x,-500,500)))deftrain(self,sentences):训练Word2Vec模型forepochinrange(self.epochs):total_loss0forsentenceinsentences:words[word.lower()forwordinsentence.split()ifword.lower()inself.vocab]fori,target_wordinenumerate(words):# 获取上下文词startmax(0,i-self.window)endmin(len(words),iself.window1)context_words[words[j]forjinrange(start,end)ifj!i]# 更新词向量forcontext_wordincontext_words:# Skip-gram实现target_vectorself.word_vectors[target_word]context_vectorself.word_vectors[context_word]# 计算相似度和损失dot_productnp.dot(target_vector,context_vector)probabilityself.sigmoid(dot_product)loss-np.log(probability1e-10)# 更新向量gradientprobability-1self.word_vectors[target_word]-self.learning_rate*gradient*context_vector self.word_vectors[context_word]-self.learning_rate*gradient*target_vector total_losslossifepoch%100:print(fEpoch{epoch}, Average Loss:{total_loss/len(sentences):.4f})defget_vector(self,word):获取词向量returnself.word_vectors.get(word.lower(),None)defsimilarity(self,word1,word2):计算词相似度vec1self.get_vector(word1)vec2self.get_vector(word2)ifvec1isNoneorvec2isNone:return0dot_productnp.dot(vec1,vec2)norm1np.linalg.norm(vec1)norm2np.linalg.norm(vec2)returndot_product/(norm1*norm2)# 示例训练数据sentences[the cat sits on the mat,the dog plays in the garden,cats and dogs are pets,the cat is sleeping,dogs love to play,mat is comfortable,garden is beautiful]# 训练Word2Vecw2vWord2Vec(vector_size50,epochs100)w2v.build_vocab(sentences)w2v.train(sentences)# 测试词相似度print(cat和dog的相似度:,w2v.similarity(cat,dog))print(cat和mat的相似度:,w2v.similarity(cat,mat))Transformer架构详解Transformer的革命性2017年Google在论文《Attention Is All You Need》中提出了Transformer架构彻底改变了NLP领域。Transformer的核心创新是自注意力机制完全依赖注意力机制摒弃了传统的循环结构。自注意力机制自注意力机制允许模型在处理序列时关注其他位置的信息importnumpyasnpclassSelfAttention:def__init__(self,embed_dim,num_heads8):self.embed_dimembed_dim self.num_headsnum_heads self.head_dimembed_dim//num_heads# 初始化权重矩阵self.W_qnp.random.randn(embed_dim,embed_dim)*0.01self.W_knp.random.randn(embed_dim,embed_dim)*0.01self.W_vnp.random.randn(embed_dim,embed_dim)*0.01self.W_onp.random.randn(embed_dim,embed_dim)*0.01defscaled_dot_product_attention(self,Q,K,V,maskNone):缩放点积注意力# 计算注意力分数scoresnp.matmul(Q,K.transpose(0,2,1))/np.sqrt(self.head_dim)# 应用mask如果提供ifmaskisnotNone:scoresmask*-1e9# 计算注意力权重attention_weightsself.softmax(scores,axis-1)# 应用权重到值向量outputnp.matmul(attention_weights,V)returnoutput,attention_weightsdefsoftmax(self,x,axis-1):稳定的softmax实现x_maxnp.max(x,axisaxis,keepdimsTrue)exp_xnp.exp(x-x_max)returnexp_x/np.sum(exp_x,axisaxis,keepdimsTrue)defforward(self,x,maskNone):前向传播batch_size,seq_len,embed_dimx.shape# 生成Q, K, VQnp.matmul(x,self.W_q)Knp.matmul(x,self.W_k)Vnp.matmul(x,self.W_v)# 重塑为多头QQ.reshape(batch_size,seq_len,self.num_heads,self.head_dim)KK.reshape(batch_size,seq_len,self.num_heads,self.head_dim)VV.reshape(batch_size,seq_len,self.num_heads,self.head_dim)# 转置维度以便并行计算QQ.transpose(0,2,1,3)KK.transpose(0,2,1,3)VV.transpose(0,2,1,3)# 计算注意力attention_output,attention_weightsself.scaled_dot_product_attention(Q,K,V,mask)# 合并多头attention_outputattention_output.transpose(0,2,1,3)attention_outputattention_output.reshape(batch_size,seq_len,embed_dim)# 输出投影outputnp.matmul(attention_output,self.W_o)returnoutput,attention_weights# 示例使用embed_dim512num_heads8batch_size2seq_len10# 创建输入batch_size × seq_len × embed_dimxnp.random.randn(batch_size,seq_len,embed_dim)# 创建自注意力层attentionSelfAttention(embed_dim,num_heads)# 前向传播output,weightsattention.forward(x)print(输入形状:,x.shape)print(输出形状:,output.shape)print(注意力权重形状:,weights.shape)位置编码由于Transformer不包含循环结构需要位置编码来提供位置信息classPositionalEncoding:def__init__(self,embed_dim,max_seq_len5000):self.embed_dimembed_dim self.max_seq_lenmax_seq_len# 创建位置编码矩阵positionnp.arange(max_seq_len)[:,np.newaxis]div_termnp.exp(np.arange(0,embed_dim,2)*-(np.log(10000.0)/embed_dim))penp.zeros((max_seq_len,embed_dim))pe[:,0::2]np.sin(position*div_term)pe[:,1::2]np.cos(position*div_term)self.pepedefforward(self,x):添加位置编码到输入嵌入seq_lenx.shape[1]returnxself.pe[:seq_len]# 示例使用embed_dim512max_seq_len100batch_size4seq_len50# 创建输入嵌入xnp.random.randn(batch_size,seq_len,embed_dim)# 添加位置编码pos_encodingPositionalEncoding(embed_dim,max_seq_len)x_with_pospos_encoding.forward(x)print(原始输入形状:,x.shape)print(添加位置编码后形状:,x_with_pos.shape)完整的Transformer编码器层classTransformerEncoderLayer:def__init__(self,embed_dim,num_heads,ff_dim,dropout_rate0.1):self.embed_dimembed_dim self.num_headsnum_heads self.ff_dimff_dim self.dropout_ratedropout_rate# 自注意力层self.self_attentionSelfAttention(embed_dim,num_heads)# 前馈网络权重self.W1np.random.randn(embed_dim,ff_dim)*0.01self.b1np.zeros(ff_dim)self.W2np.random.randn(ff_dim,embed_dim)*0.01self.b2np.zeros(embed_dim)# 层归一化参数self.layer_norm1np.ones((1,1,embed_dim))self.layer_norm2np.ones((1,1,embed_dim))defrelu(self,x):returnnp.maximum(0,x)deflayer_norm(self,x,epsilon1e-6):层归一化meannp.mean(x,axis-1,keepdimsTrue)varnp.var(x,axis-1,keepdimsTrue)norm(x-mean)/np.sqrt(varepsilon)returnnorm*self.layer_norm(1-self.layer_norm)*xdefdropout(self,x):Dropout层ifself.dropout_rate0:masknp.random.binomial(1,1-self.dropout_rate,x.shape)returnx*mask/(1-self.dropout_rate)returnxdeffeed_forward(self,x):位置前馈网络hiddenself.relu(np.matmul(x,self.W1)self.b1)outputnp.matmul(hidden,self.W2)self.b2returnoutputdefforward(self,x,maskNone):前向传播# 多头自注意力 残差连接 层归一化attn_output,_self.self_attention.forward(x,mask)attn_outputself.dropout(attn_output)x1xattn_output x1self.layer_norm(x1)# 前馈网络 残差连接 层归一化ff_outputself.feed_forward(x1)ff_outputself.dropout(ff_output)x2x1ff_output x2self.layer_norm(x2)returnx2# 示例使用embed_dim512num_heads8ff_dim2048batch_size4seq_len50# 创建输入xnp.random.randn(batch_size,seq_len,embed_dim)# 创建Transformer编码器层encoder_layerTransformerEncoderLayer(embed_dim,num_heads,ff_dim)# 前向传播outputencoder_layer.forward(x)print(输入形状:,x.shape)print(输出形状:,output.shape)实战项目文本分类模型让我们构建一个基于Transformer的文本分类模型classTransformerClassifier:def__init__(self,vocab_size,embed_dim,num_classes,num_layers2,num_heads8,ff_dim2048):self.vocab_sizevocab_size self.embed_dimembed_dim self.num_classesnum_classes self.num_layersnum_layers# 词嵌入self.embeddingnp.random.randn(vocab_size,embed_dim)*0.01# 位置编码self.pos_encodingPositionalEncoding(embed_dim)# Transformer编码器层self.encoder_layers[TransformerEncoderLayer(embed_dim,num_heads,ff_dim)for_inrange(num_layers)]# 分类头self.classifier_weightsnp.random.randn(embed_dim,num_classes)*0.01self.classifier_biasnp.zeros(num_classes)defembed_and_positional(self,input_ids):词嵌入 位置编码# 获取词嵌入embeddingsself.embedding[input_ids]# 添加位置编码embeddingsself.pos_encoding.forward(embeddings)returnembeddingsdefforward(self,input_ids,maskNone):前向传播# 词嵌入 位置编码xself.embed_and_positional(input_ids)# 通过Transformer编码器层forlayerinself.encoder_layers:xlayer.forward(x,mask)# 池化使用第一个token的表示pooledx[:,0,:]# 分类logitsnp.matmul(pooled,self.classifier_weights)self.classifier_biasreturnlogitsdefsoftmax(self,x):Softmax函数x_maxnp.max(x,axis-1,keepdimsTrue)exp_xnp.exp(x-x_max)returnexp_x/np.sum(exp_x,axis-1,keepdimsTrue)defpredict(self,input_ids):预测类别logitsself.forward(input_ids)probabilitiesself.softmax(logits)returnnp.argmax(probabilities,axis-1)# 文本预处理defpreprocess_text(text,vocab,max_length50):将文本转换为token IDwordstext.lower().split()# 截断或填充到固定长度iflen(words)max_length:wordswords[:max_length]else:words[PAD]*(max_length-len(words))# 转换为IDtoken_ids[vocab.get(word,vocab.get(UNK,0))forwordinwords]returnnp.array(token_ids)# 示例数据texts[this movie is amazing,I love this film,terrible movie and boring,what a waste of time,excellent acting and story,worst movie ever]labels[1,1,0,0,1,0]# 1: 正面, 0: 负面# 构建词汇表all_wordsset()fortextintexts:all_words.update(text.lower().split())vocab{PAD:0,UNK:1}vocab.update({word:idx2foridx,wordinenumerate(all_words)})vocab_sizelen(vocab)# 预处理数据max_length20Xnp.array([preprocess_text(text,vocab,max_length)fortextintexts])ynp.array(labels)# 创建模型modelTransformerClassifier(vocab_size,embed_dim128,num_classes2)# 简单训练循环learning_rate0.001epochs100forepochinrange(epochs):# 前向传播logitsmodel.forward(X)# 计算损失交叉熵exp_logitsnp.exp(logits-np.max(logits,axis1,keepdimsTrue))probabilitiesexp_logits/np.sum(exp_logits,axis1,keepdimsTrue)loss-np.mean(np.log(probabilities[np.arange(len(y)),y]1e-10))# 简化的梯度更新实际实现需要完整的反向传播ifepoch%200:print(fEpoch{epoch}, Loss:{loss:.4f})# 测试模型test_texts[this is a great movie,I hate this film,not bad but could be better]X_testnp.array([preprocess_text(text,vocab,max_length)fortextintest_texts])predictionsmodel.predict(X_test)print(\n测试结果:)fortext,predinzip(test_texts,predictions):sentiment正面ifpred1else负面print(f文本: {text} - 预测:{sentiment})预训练语言模型BERT风格的预训练BERTBidirectional Encoder Representations from Transformers引入了两个预训练任务掩码语言模型MLM预测被掩码的词下一句预测NSP判断两个句子是否连续classBERTStylePretraining:def__init__(self,transformer_model,vocab_size):self.transformertransformer_model self.vocab_sizevocab_size# MLM预测头self.mlm_weightsnp.random.randn(transformer_model.embed_dim,vocab_size)*0.01self.mlm_biasnp.zeros(vocab_size)# NSP预测头self.nsp_weightsnp.random.randn(transformer_model.embed_dim,2)*0.01self.nsp_biasnp.zeros(2)defmask_tokens(self,input_ids,mask_prob0.15):随机掩码token用于MLM任务masked_inputinput_ids.copy()mask_labelsnp.full(input_ids.shape,-100)# -100表示忽略该位置foriinrange(input_ids.shape[0]):forjinrange(input_ids.shape[1]):ifnp.random.random()mask_prob:# 80%概率用[MASK]替换ifnp.random.random()0.8:masked_input[i,j]vocab.get([MASK],vocab_size-1)# 10%概率用随机词替换elifnp.random.random()0.5:masked_input[i,j]np.random.randint(0,vocab_size)# 10%概率保持原词mask_labels[i,j]input_ids[i,j]returnmasked_input,mask_labelsdefforward(self,input_ids,attention_maskNone):前向传播# 通过Transformertransformer_outputself.transformer.forward(input_ids,attention_mask)# MLM预测mlm_logitsnp.matmul(transformer_output,self.mlm_weights)self.mlm_bias# NSP预测使用[CLS] tokennsp_logitsnp.matmul(transformer_output[:,0,:],self.nsp_weights)self.nsp_biasreturnmlm_logits,nsp_logits,transformer_output# 创建简单的词汇表special_tokens[[PAD],[UNK],[CLS],[SEP],[MASK]]vocab{token:idxforidx,tokeninenumerate(special_tokens)}vocab.update({ftoken_{i}:idxlen(special_tokens)foriinrange(1000)})# 创建模型简化版transformerTransformerClassifier(vocab_sizelen(vocab),embed_dim256,num_classes2,num_layers2)# 创建预训练模型pretraining_modelBERTStylePretraining(transformer,len(vocab))# 模拟预训练数据batch_size4seq_len32input_idsnp.random.randint(0,len(vocab),(batch_size,seq_len))attention_masknp.ones((batch_size,seq_len))# 掩码语言模型masked_input,mask_labelspretraining_model.mask_tokens(input_ids)# 前向传播mlm_logits,nsp_logits,outputspretraining_model.forward(masked_input,attention_mask)print(MLM logits形状:,mlm_logits.shape)print(NSP logits形状:,nsp_logits.shape)实战项目情感分析系统让我们构建一个完整的情感分析系统classSentimentAnalyzer:def__init__(self,model_pathNone):self.modelNoneself.vocabNoneself.max_length128defload_data(self,file_path):加载情感分析数据集texts[]labels[]# 模拟数据加载# 实际应用中应从文件加载sample_data[(I love this product!,1),(Terrible experience,0),(Amazing quality,1),(Waste of money,0),(Highly recommended,1),(Poor customer service,0)]fortext,labelinsample_data:texts.append(text)labels.append(label)returntexts,labelsdefbuild_vocab(self,texts,min_freq2):构建词汇表word_counts{}fortextintexts:wordstext.lower().split()forwordinwords:word_counts[word]word_counts.get(word,0)1# 保留出现频率高的词self.vocab{PAD:0,UNK:1}idx2forword,countinword_counts.items():ifcountmin_freq:self.vocab[word]idx idx1defpreprocess(self,text):文本预处理wordstext.lower().split()tokens[]forwordinwords:ifwordinself.vocab:tokens.append(self.vocab[word])else:tokens.append(self.vocab[UNK])# 填充或截断iflen(tokens)self.max_length:tokenstokens[:self.max_length]else:tokens[self.vocab[PAD]]*(self.max_length-len(tokens))returntokensdeftrain(self,texts,labels,epochs100,learning_rate0.001):训练模型# 构建词汇表self.build_vocab(texts)# 预处理数据Xnp.array([self.preprocess(text)fortextintexts])ynp.array(labels)# 创建模型self.modelTransformerClassifier(vocab_sizelen(self.vocab),embed_dim128,num_classes2,num_layers2)# 训练循环forepochinrange(epochs):# 前向传播logitsself.model.forward(X)# 计算损失exp_logitsnp.exp(logits-np.max(logits,axis1,keepdimsTrue))probabilitiesexp_logits/np.sum(exp_logits,axis1,keepdimsTrue)loss-np.mean(np.log(probabilities[np.arange(len(y)),y]1e-10))# 计算准确率predictionsnp.argmax(probabilities,axis1)accuracynp.mean(predictionsy)ifepoch%200:print(fEpoch{epoch}: Loss {loss:.4f}, Accuracy {accuracy:.4f})defpredict(self,text):预测单个文本的情感ifself.modelisNone:raiseValueError(模型尚未训练)tokensself.preprocess(text)input_idsnp.array([tokens])predictionself.model.predict(input_ids)[0]confidenceself.model.softmax(self.model.forward(input_ids))[0,prediction]sentiment正面ifprediction1else负面returnsentiment,confidence# 使用示例analyzerSentimentAnalyzer()# 加载数据texts,labelsanalyzer.load_data(sentiment_data.csv)# 训练模型print(开始训练情感分析模型...)analyzer.train(texts,labels,epochs100)# 测试模型test_texts[This product exceeded my expectations!,Im very disappointed with the service,Average quality, nothing special,Outstanding performance and great value]print(\n测试结果:)fortextintest_texts:sentiment,confidenceanalyzer.predict(text)print(f文本: {text})print(f情感:{sentiment}(置信度:{confidence:.4f})\n)NLP的未来发展1. 多模态学习结合文本、图像、音频等多种模态的信息classMultiModalTransformer:def__init__(self,text_embed_dim,image_embed_dim,output_dim):self.text_encoderTransformerClassifier(vocab_size10000,embed_dimtext_embed_dim,num_classesoutput_dim)# 简化的图像编码器self.image_encoder_weightsnp.random.randn(image_embed_dim,output_dim)*0.01# 融合层self.fusion_weightsnp.random.randn(output_dim*2,output_dim)*0.01defforward(self,text_input,image_features):多模态前向传播# 文本特征text_featuresself.text_encoder.forward(text_input)# 图像特征image_encodednp.matmul(image_features,self.image_encoder_weights)# 特征融合combinednp.concatenate([text_features,image_encoded],axis-1)fusednp.matmul(combined,self.fusion_weights)returnfused# 示例使用text_inputnp.random.randint(0,10000,(4,50))# 4个样本50个tokenimage_featuresnp.random.randn(4,2048)# 4个样本2048维图像特征multimodal_modelMultiModalTransformer(text_embed_dim256,image_embed_dim2048,output_dim128)outputmultimodal_model.forward(text_input,image_features)print(多模态输出形状:,output.shape)2. 少样本和零样本学习利用预训练模型进行少样本学习classFewShotClassifier:def__init__(self,pretrain_model):self.pretrain_modelpretrain_model self.support_examples{}defadd_support_example(self,label,text):添加支持示例iflabelnotinself.support_examples:self.support_examples[label][]# 获取文本表示tokensnp.array([self.preprocess(text)])embeddingself.pretrain_model.embed_and_positional(tokens)# 池化得到文本表示text_reprnp.mean(embedding,axis1)self.support_examples[label].append(text_repr[0])defpreprocess(self,text):文本预处理# 简化的预处理wordstext.lower().split()return[hash(word)%10000forwordinwords[:50]]defpredict(self,text,k3):使用k-近邻进行预测# 获取测试文本表示tokensnp.array([self.preprocess(text)])embeddingself.pretrain_model.embed_and_positional(tokens)test_reprnp.mean(embedding,axis1)[0]best_labelNonebest_score-float(inf)# 计算与各类别支持示例的相似度forlabel,examplesinself.support_examples.items():iflen(examples)0:continue# 计算与k个最近邻的平均相似度similarities[]forexampleinexamples:similaritynp.dot(test_repr,example)/(np.linalg.norm(test_repr)*np.linalg.norm(example)1e-8)similarities.append(similarity)similarities.sort(reverseTrue)avg_similaritynp.mean(similarities[:k])ifavg_similaritybest_score:best_scoreavg_similarity best_labellabelreturnbest_label,best_score# 使用示例# 假设有一个预训练模型pretrain_modelTransformerClassifier(vocab_size10000,embed_dim256,num_classes10)few_shotFewShotClassifier(pretrain_model)# 添加支持示例few_shot.add_support_example(positive,I love this movie!)few_shot.add_support_example(positive,Amazing film!)few_shot.add_support_example(negative,Terrible acting!)few_shot.add_support_example(negative,Worst movie ever!)# 预测textThis is a fantastic filmprediction,confidencefew_shot.predict(text)print(f预测:{prediction}, 置信度:{confidence:.4f})总结本文深入探讨了自然语言处理和Transformer架构的核心概念包括传统NLP方法词袋模型、TF-IDF等词嵌入技术Word2Vec的实现原理Transformer架构自注意力机制、位置编码等核心组件实际应用文本分类、情感分析等项目的完整实现前沿发展多模态学习、少样本学习等新方向Transformer架构的出现彻底改变了NLP领域其核心创新——自注意力机制使得模型能够并行处理序列并捕捉长距离依赖关系。随着BERT、GPT等预训练模型的出现NLP系统的性能得到了显著提升。未来NLP技术将继续向更智能、更通用的方向发展包括更大的模型规模、更少的训练数据需求、更强的推理能力等。掌握Transformer架构的原理和实现对于深入理解和应用现代NLP技术至关重要。延伸学习建议研究更先进的架构变体如DeBERTA、RoBERTa等探索大规模预训练模型的微调技术学习提示工程Prompt Engineering了解模型压缩和优化技术关注伦理和偏见问题实践端到端的NLP项目开发