In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Example Input: We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
Question: How many tweats does MMHS150k contains, 150000?
Example Output: $150,000$ tweets

Example Input: We assess our dataset using traditional and deep learning methods. Our simplest model is a linear SVM trained on word unigrams. SVMs have produced state-of-the-art results for many text classification tasks BIBREF13 . We also train a bidirectional Long Short-Term-Memory (BiLSTM) model, which we adapted from the sentiment analysis system of sentimentSystem,rasooli2018cross and altered to predict offensive labels instead. It consists of (1) an input embedding layer, (2) a bidirectional LSTM layer, (3) an average pooling layer of input features. The concatenation of the LSTM's and average pool layer is passed through a dense layer and the output is passed through a softmax function. We set two input channels for the input embedding layers: pre-trained FastText embeddings BIBREF14 , as well as updatable embeddings learned by the model during training. Finally, we also apply a Convolutional Neural Network (CNN) model based on the architecture of BIBREF15 , using the same multi-channel inputs as the above BiLSTM.
Question: What models are used in the experiment?
Example Output: linear SVM bidirectional Long Short-Term-Memory (BiLSTM) Convolutional Neural Network (CNN)

Example Input: We evaluated baselines and our model using accuracy as the metric on the ROCStories dataset, and summarized these results in Table 2 .
Question: Which metrics are they evaluating with?
Example Output:
accuracy