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Text Classification, regression 2013 [122] [123] B. E. Sakar et al. Spoken Arabic Digits Spoken Arabic digits from 44 male and 44 female. Time-series of mel-frequency cepstrum coefficients. 8,800 Text Classification 2010 [124] [125] M. Bedda et al. ISOLET Dataset Spoken letter names. Features extracted from sounds. 7797 Text Classification 1994 ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Another example of an adversarial evaluation dataset is Swag and its successor, HellaSwag, collections of problems in which one of multiple options must be selected to complete a text passage. The incorrect completions were generated by sampling from a language model and filtering with a set of classifiers.
In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...
Specifically, consider a language model that given a previous text , predicts the next word . The network encodes the text into a vector v c {\displaystyle v_{c}} , and predicts the probability distribution of the next word as S o f t m a x ( v c W ) {\displaystyle \mathrm {Softmax} (v_{c}W)} for an embedding matrix W {\displaystyle W} .
The idea of using the attention mechanism for self-attention, instead of in an encoder-decoder (cross-attention), was also proposed during this period, such as in differentiable neural computers [29] and neural Turing machines. [30] It was termed intra-attention [31] where an LSTM is augmented with a memory network as it encodes an input sequence.
The BoW representation of a text removes all word ordering. For example, the BoW representation of "man bites dog" and "dog bites man" are the same, so any algorithm that operates with a BoW representation of text must treat them in the same way. Despite this lack of syntax or grammar, BoW representation is fast and may be sufficient for simple ...
A 380M-parameter model for machine translation uses two long short-term memories (LSTM). [21] Its architecture consists of two parts. The encoder is an LSTM that takes in a sequence of tokens and turns it into a vector. The decoder is another LSTM that converts the vector into a sequence