with tf.variable_scope('language_model', reuse=None, initializer=initializer): train_model = PTBModel(True, TRAIN_BATCH_SIZE, TRAIN_NUM_STEP) # 定义测试用的循环神经网络模型。它与train_model公用参数,但是没有dropout with tf.variable_scope('language_model', reuse=True, initializer=initializer): ...
4096])target_ids[:,:-trg_len]=-100withtorch.no_grad():outputs=model(input_ids,labels=target_ids)# loss is calculated using CrossEntropyLoss which averages over valid labels# N.B. the model only calculates loss over trg_len - 1 labels, because it ...
模型训练(text-to-text 任务) # 典型的训练循环 for epoch in range(num_epochs): for batch in dataloader: outputs = model(batch) loss = criterion(outputs, targets) # 交叉熵损失 perplexity = torch.exp(loss) # 计算困惑度 # 用于监控训练 print(f"Epoch {epoch}, Perplexity: {perplexity}") 机...
pp = 10^(log(prop)/word); 其中prop为所有sentence的概率的乘积,word为词的数目
Perplexity (P) is a commonly used measure in language modeling to evaluate how well a language model predicts a given sentence or sequence of words. It is calculated using the following formula: P = 2^(-l) where P is the perplexity, and l is the average log-likelihood of the test set...
model = RNNModel("LSTM", VOCAB_SIZE, EMBEDDING_SIZE, HIDDEN_SIZE, 2, dropout=0.5) if USE_CUDA: model = model.to(device) 1. 2. 3. 查看模型结构 model # 结果 RNNModel( (drop): Dropout(p=0.5, inplace=False) (encoder): Embedding(50002, 100) ...
Acoustic Sensitive Language Model Perplexity for Automatic Speech RecognitionChelba, Ciprian
)、trigram(每个词只与前2个词相关联)、N-gram(每个词与前n-1个词相关联)3.语言模型评估(Perplexity)perplexity越小越好。perplexity公式见下...1.Noisy Channel Model2.语言模型即判断一句话是不是人话。是计算上文noisy channel model模型中的P(text)。方法是用markov假设。即一个词出现 ...
在unigram model中, ,perplexity=955 在这里也看到了,几个模型的perplexity的值是不同的,这也就表明了三元模型一般是性能良好的。 [评价一个语言模型Evaluating Language Models:Perplexity] 皮皮blog Topic Coherence 一种可能更好的主题模型评价标准 [Optimizing semantic coherence in topic models.] ...
语言模型(Language Model,简称 LM)是一个用于建模自然语言(即人们日常使用的语言)的概率模型。简单来说,语言模型的任务是评估一个给定的词序列(即一个句子)在真实世界中出现的概率。这种模型在自然语言处理(NLP)的诸多应用中,如机器翻译、语音识别、文本生成等,都起到了关键性的作用。 TechLead 2023/10/21 1.1K0...