output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ �→ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 以前，我们使用 model.chat() （有关更多详细信息，请参阅先前 OpenAI( (续下页) 1.2. 快速开始 5 Qwen (接上页) api_key=openai_api_key, base_url=openai_api_base, ) chat_response = client.chat.completions.create( model="Qwen/Qwen1.5-7B-Chat", messages=[ {"role": "system", "content": {"role": "user", "content": "Tell me something about large language models."}, ] ) print("Chat response:", chat_response) 1.2.3 下一步 现在，您可以尽情探索 Qwen 模型的各种用途。若想了解更多，请随时查阅本文档中的其他内容。 1.3 使用 Transformers 实现 Chat

properly installed on your machine, then open your terminal and type python, you could see the response similar as specified below, Python 3.6.5 (v3.6.5:f59c0932b4, Mar 28 2018, 17:00:18) [MSC v.1900 the below command to install one by one. numpy pip install numpy you could see the following response, Collecting numpy Downloading https://files.pythonhosted.org/packages/cf/a4/d5387a7420454 4MB 2.8MB/s Keras 5 pandas pip install pandas We could see the following response: Collecting pandas Downloading https://files.pythonhosted.org/packages/cf/a4/d5387a742045

Spanish speaker’s response, “estoy ir mercado”, sufficiently conveys the information that the person is going to the market. A version of this example could be a native english speaker’s response, “I go market” a text sample such that the agreement between the text and the original label is intact. In the context of sentiment analysis, the transformation must preserve the original sentiment of the text. For a

can train the model is to predict a hidden word in a sentence, given the words surrounding it (the context). This is known as the Continuous Bag of Words (CBOW) task, where the model learns to predict a masked based on the surrounding words (context). Mathematically, we want to find a word , which maximizes the conditional probability , where the size of the sliding window of context is ( words on each side of dataset is preprocessed (lowercase, strip punctuation, normalization etc.) to create pairs of input context (neighboring words), and the label (masked word to be predicted). The word tokens are vectorized

型的文本。在这一步中， 我们确定词汇量的大小（我们称之为vocab_size，比如说，将其视为10,000）以及 哪些词属于它。在训练阶段的开始，我们创建两个矩阵 - Embedding矩阵和Context 矩阵。这两个矩阵在我们的词汇表中嵌入了每个单词（这vocab_size是他们的维度 之一）。第二个维度是我们希望每次嵌入的时间长度（embedding_size- 300是一个 常见值）。 现在我们有四个单词：输入单词not和输出/上下文单词:( thou实际邻 居）,aaron，和taco（负样本）。我们继续查找它们的嵌入 - 对于输 入词，我们查看Embedding矩阵。对于上下文单词，我们查看Context矩 阵（即使两个矩阵都在我们的词汇表中嵌入了每个单词）。 23 3.Word2Vec 训练流程 现在我们需要一种方法将这些分数转化为看起来像概率的东西 ： 使用sigmoid函数把概率转换为0和1。 ，`thou`，`aaron`和`taco`）。我们现在进行下一步（下一个正样本及 其相关的负样本）,并再次执行相同的过程。 当我们循环遍历整个数据集多次时，嵌入继续得到改进。然后我们可以停 止训练过程，丢弃`Context`矩阵，并使用`Embeddings`矩阵作为下一个任务 的预训练嵌入。 27 4.GloVe 03 Word2Vec 04 GloVe 02 词嵌入 05

48550/arXiv.1803.07728. 2 Doersch, Carl, et al. "Unsupervised Visual Representation Learning by Context Prediction." arXiv, 19 May. 2015, doi:10.48550/arXiv.1505.05192. Figure 6-4 (a): Detecting relative research papers, since they can help improve your baseline models even if they were presented in the context of different model architectures and hyperparameters. For example, the paper titled: ResNet Strikes Subclass Distillation It can also be useful to revisit some of the other learning techniques in the context of the problem at hand. For instance, in chapter 3, we found that distillation was a very handy technique

AWS Lambda 1: Understand user intent Amazon API Gateway AWS Lambda 3: Translate REST response into natural language Mobile Hub Custom Connector 2: Invoke a SaaS application or an existing

training cases for which its value is known The thing we want to predict is called the target or the response variable Usually, we need training data Feng Li (SDU) Overview September 6, 2023 23 / 57 Supervised

-----------------------------------------------------+ 在PyTorch中，每个数组都有一个设备（device），我们通常将其称为环境（context）。默认情况下，所有 变量和相关的计算都分配给CPU。有时环境可能是GPU。当我们跨多个服务器部署作业时，事情会变得更加 棘手。通过智能地将数组分配给环境，我们可以最大限度地减少在设备之间传输数据的时间。例如，当在带 permute(1, 0, 2) # 广播context，使其具有与X相同的num_steps context = state[-1].repeat(X.shape[0], 1, 1) X_and_context = torch.cat((X, context), 2) output, state = self.rnn(X_and_context, state) output = self unsqueeze(hidden_state[-1], dim=1) # context的形状为(batch_size,1,num_hiddens) context = self.attention( query, enc_outputs, enc_outputs, enc_valid_lens) # 在特征维度上连结 x = torch.cat((context, torch.unsqueeze(x, dim=1))

?3 ?@?4 + ?4 ?@?5 + ?5 Flaws ▪ Long sentence ▪ 100+ words ▪ too much parametes [w, b] ▪ no context information ▪ consistent tensor Naïve version I hate this boring movie ?@?1 + ?1 Pos/Neg ?@