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Natural Language Processing

Natural Language Processing (NLP)

Venture into the fascinating world of NLP, a cutting-edge field of AI that empowers machines to comprehend and interact with human language. With NLP, unlock a myriad of transformative applications, including chatbots, language translation, sentiment analysis for social media, and voice assistants like Siri & Alexa.

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  • Hugging Face tasks (opens in a new tab) is the home for all Machine Learning tasks. Here you can find what you need to get started with a task: demos, use cases, models, datasets, and more.

  • OpenAI Embeddings (opens in a new tab): OpenAI's embeddings are text-based vector representations that gauge text string relatedness. They're widely used in natural language and code tasks, including semantic search, text classification, and question answering.


  • Efficient Estimation of Word Representations in Vector Space (opens in a new tab) (2013): Introduced two model architectures for word vector representations derived from large datasets, outperforming existing methods in word similarity tasks with enhanced accuracy and lower computational demands. These vectors excel in measuring syntactic and semantic word similarities.

  • GloVe: Global Vectors for Word Representation (opens in a new tab) (2014): Proposed GloVe, a model that learns word meanings from co-occurrence statistics. It uses a global co-occurrence matrix to derive word vectors, demonstrating superior performance in various word analogy and similarity tasks compared to other methods.

  • Deep contextualized word representations (opens in a new tab) (2018): Introduces novel word representations capturing both meaning and context in sentences. These representations stem from a deep bidirectional language model trained on extensive text. They excel in various NLP tasks, such as sentiment analysis and named entity recognition, surpassing other techniques in performance.

  • Sequence to Sequence Learning with Neural Networks (opens in a new tab) (2014): Introduces the sequence-to-sequence model, a neural network for tasks like machine translation and text summarization. Comprising an encoder and a decoder, it effectively processes input sequences and generates output sequences, outperforming other methods in a range of NLP tasks.

  • Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (opens in a new tab) (2020): Introduces a versatile fine-tuning method for retrieval-augmented generation (RAG) models, combining parametric and non-parametric memory for language generation. They employ a pre-trained neural retriever to fetch Wikipedia passages for input, achieving state-of-the-art results on knowledge-intensive NLP tasks. RAG models offer more precise, diverse, and factual language generation. (blog)

  • Improving Language Understanding by Generative Pre-Training (opens in a new tab) (2018): Introduced Generative Pre-trained Transformer (GPT), enhancing natural language understanding through unsupervised learning, addressing the scarcity of labeled data. It introduces "generative pre-training," training a large neural network on vast unlabeled text corpora, followed by fine-tuning on specific tasks with labeled data. The approach combines transformers and unsupervised pre-training, showing improved language model performance and task-related advancements. (blog) (code)

  • Language Models are Unsupervised Multitask Learners (opens in a new tab) (2019): Introduced GPT-2, enhancing natural language processing through unsupervised learning, showcasing a language model's ability to learn tasks without explicit supervision. Training on the WebText dataset, the model infers and performs tasks in natural language sequences, demonstrating superior performance in a zero-shot setting across various tasks compared to discriminatively trained models. (blog) (code)

  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (opens in a new tab) (2018): Presents BERT, a pre-trained deep bidirectional transformer model. Trained on extensive text data with a masked language modeling objective, it excels in natural NLP like question answering and sentiment analysis, surpassing alternative methods in performance and versatility.

  • ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (opens in a new tab) (2020): Introducing a more sample-efficient pre-training task, "replaced token detection," outperforming traditional masked language modeling like BERT. Instead of masking, it corrupts input by replacing tokens with alternatives from a small generator network. Demonstrates efficiency over Masked language modeling (MLM) by defining the task for all input tokens, yielding superior contextual representations.

  • Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (opens in a new tab) (2019): This study investigates transfer learning through a unified text-to-text transformer model, scrutinizing diverse architectural variants. It underscores the significance of transfer learning in NLP, outlining the experimental setup and benchmarks. The exploration includes the effects of pre-training objectives, model size, and data size, yielding state-of-the-art results.

  • PaLM 2 (opens in a new tab): A superior language model by Google, excels in advanced reasoning, translation, and code generation. The next-gen PaLM is smaller yet more efficient, featuring enhanced performance with faster inference and reduced serving costs. Its diverse multilingual pre-training includes human and programming languages, equations, scientific papers, and web content. With improved architecture and varied task training, PaLM 2 caters to text generation, language translation, creative content creation, and informative question answering. (blog)