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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis . Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [ 1 ]
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research (FAIR) lab. [3] [4] [5] [6] The model allows one to ...
The three embedding vectors are added together representing the initial token representation as a function of these three pieces of information. After embedding, the vector representation is normalized using a LayerNorm operation, outputting a 768-dimensional vector for each input token. After this, the representation vectors are passed forward ...
It was observed that the Llama 3 models showed that when a model is trained on data that is more than the "Chinchilla-optimal" amount, the performance continues to scale log-linearly. For example, the Chinchilla-optimal dataset for Llama 3 8B is 200 billion tokens, but performance continued to scale log-linearly to the 75-times larger dataset ...
The Transformers library is a Python package that contains open-source implementations of transformer models for text, image, and audio tasks. It is compatible with the PyTorch , TensorFlow and JAX deep learning libraries and includes implementations of models like BERT and GPT-2 . [ 16 ]
In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.