机器学习代考_Machine Learning代考_Neural Networks for Sequences

机器学习代考_Machine Learning代考_Fine-tuning BERT for NLP applications

After pre-training BERT in an unsupervised way, we can use it for various downtream tasks by performing supervised fine-tuning. (See Section $19.2$ for more background on such transfer learning methods.) Figure $15.35$ illustrates how we can modify a BERT model to perform different tasks, by simply adding one or more new output heads to the final hidden layer. See code.probml.ai/book1/bert_torch for some sample code.

In Figure 15.35(a), we show how we can tackle single sentence classification (e.g., sentiment analysis): we simply take the feature vector associated with the dummy CLS token and feed it into an MLP. Since each output attends to all inputs, this hidden vector will summarize the entire sentence. The MLP then learns to map this to the desired label space.

In Figure 15.35(b), we show how we can tackle sentence-pair classification (e.g., textual entailment, as discussed in Sertion 15.4.6): we just feed in the two input. sentences, formatted as in Equation (15.73), and then classify the CLS token.

In Figure 15.35(c), we show how we can tackle single sentence tagging, in which we associate a label or tag with each word, instead of just the entire sentence. A common application of this is part of speech tagging, in which we annotate each words a noun, verb, adjective, etc. Another application of this is noun phrase chunking, also called shallow parsing, in which we must annotate the span of each noun phrase. The span is encoded using the BIO notation, in which B is the beginning of an entity, I-x is for inside, and $\mathrm{O}$ is for outside any entity. For example, consider the following sentence:
$\begin{array}{lllllllllll}\text { B } & \text { I } & 0 & 0 & 0 & \text { B } & \text { I } & 0 & \text { B } & \text { I } & \text { I }\end{array}$
British Airways rose after announcing its withdrawl from the UAI deal
We see that there are 3 noun phrases, “British Airways”, “Its withdrawl” and “the UAI deal”. (We require that the B, I and $\mathrm{O}$ labels occur in order, so this a prior constraint that can be included in the model.)

We can also associate types with each noun phrase, for example distinguishing person, location, organization, and other. Thus the label space becomes {B-Per, I-Per, B-Loc, I-Loc, B-Org, I-Org, Outside $}$. This is called named entity recognition, and is a key step in information extraction. For example, consider the following sentence:

机器学习代考_Machine Learning代考_K nearest neighbor (KNN) classification

In this section, we discuss one of the simplest kind of classifier, known as the $\mathbf{K}$ nearest neighbor (KNN) classifier. The idea is as follows: to classify a new input $\boldsymbol{x}$, we find the $K$ closest examples to $\boldsymbol{x}$ in the training set, denoted $N_K(\boldsymbol{x}, \mathcal{D})$, and then look at their labels, to derive a distribution over the outputs for the local region around $\boldsymbol{x}$. More precisely, we compute
$$p(y=c \mid \boldsymbol{x}, \mathcal{D})=\frac{1}{K} \sum_{n \in N_K(\boldsymbol{x}, \mathcal{D})} \mathbb{I}\left(y_n=c\right)$$
We can then return this distribution, or the majority label.
The two main parameters in the model are the size of the neighborhood, $K$, and the distance metric $d\left(\boldsymbol{x}, \boldsymbol{x}^{\prime}\right)$. For the latter, it is common to use the Mahalanobis distance
$$d_{\mathbf{M}}(\boldsymbol{x}, \boldsymbol{\mu})=\sqrt{(\boldsymbol{x}-\boldsymbol{\mu})^{\mathrm{T}} \mathbf{M}(\boldsymbol{x}-\boldsymbol{\mu})}$$
where $\mathbf{M}$ is a positive definite matrix. If $\mathbf{M}=\mathbf{I}$, this reduces to Euclidean distance. We discuss how to learn the distance metric in Section 16.2.

Despite the simplicity of KNN classifiers, it can be shown that this approach becomes within a factor of 2 of the Bayes error (which measures the performance of the best possible classitier) if $N \rightarrow \infty[\mathrm{CH} 67$; CD14]. (Of course the convergence rate to this optimal performance may be poor in practice, for reasons we discuss in Section 16.1.2.)

机器学习代考

机器学习代考_Machine Learning代考_Fine-tuning BERT for NLP applications

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British Airways 在宣布退出 UAI 交易后上涨

机器学习代考_Machine Learning代考_K nearest neighbor (KNN) classification

$$p(y=c \mid \boldsymbol{x}, \mathcal{D})=\frac{1}{K} \sum_{n \in N_K(\boldsymbol{x}, \mathcal{D})} \mathbb{I}\left(y_n=c\right)$$

$$d_{\mathrm{M}}(\boldsymbol{x}, \boldsymbol{\mu})=\sqrt{(\boldsymbol{x}-\boldsymbol{\mu})^{\mathrm{T}} \mathrm{M}(\boldsymbol{x}-\boldsymbol{\mu})}$$

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