Deep Learning, NLP, NMT, AI, ML
News Ho Ho Ho! welcome to a special edition of the Hugging Face newsletter focused on new and upcoming commercial products. 👩🔬Introducing Supporter plans for individuals, with private models 👩🔬 Hugging Face is built for, and by the NLP community. We share our commitment to democratize NLP with hundreds of open source contributors, and model contributors all around
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Introduction Updating Python packages can be a hassle. There are many of them – it’s hard to keep track of all the newest versions, and even when you decide what to update, you still have to update each of them manually. To address this issue, pip-review was created. It lets you smoothly manage all available PyPi updates with simple commands. Originally a part of the pip-tools package, it now lives on as a standalone convenience wrapper around pip. In this […]
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Half a year ago I featured here (MT Weekly 45) a paper that questions the contribution of non-autoregressive models to computational efficiency. It showed that a model with a deep encoder (that can be parallelized) and a shallow decoder (that works sequentially) reaches the same speed with much better translation quality than NAR models. A pre-print by Facebook AI and CMU published on New Year’s Eve, Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade, presents a new fully non-autoregressive […]
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How to Optimize a Function with One Variable? Univariate function optimization involves finding the input to a function that results in the optimal output from an objective function. This is a common procedure in machine learning when fitting a model with one parameter or tuning a model that has a single hyperparameter. An efficient algorithm is required to solve optimization problems of this type that will find the best solution with the minimum number of evaluations of the objective function, […]
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A scalable semi-supervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains attributes of all nodes but labels of a few nodes… The classical label propagation (LP) method and the emerging graph convolutional network (GCN) are two popular semi-supervised solutions to this problem. The LP method is not effective in modeling node attributes and labels jointly or facing a slow convergence rate on large-scale graphs. GraphHop is proposed to its shortcoming. With proper […]
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In this paper we outline the development methodology for an automatic dog treat dispenser which combines machine learning and embedded hardware to identify and reward dog behaviors in real-time. Using machine learning techniques for training an image classification model we identify three behaviors of our canine companions: “sit”, “stand”, and “lie down” with up to 92% test accuracy and 39 frames per second… We evaluate a variety of neural network architectures, interpretability methods, model quantization and optimization techniques to develop […]
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Few-shot learning aims to train models on a limited number of labeled samples given in a support set in order to generalize to unseen samples from a query set. In the standard setup, the support set contains an equal amount of data points for each class… However, this assumption overlooks many practical considerations arising from the dynamic nature of the real world, such as class-imbalance. In this paper, we present a detailed study of few-shot class-imbalance along three axes: meta-dataset […]
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Iterative generative models, such as noise conditional score networks and denoising diffusion probabilistic models, produce high quality samples by gradually denoising an initial noise vector. However, their denoising process has many steps, making them 2-3 orders of magnitude slower than other generative models such as GANs and VAEs… In this paper, we establish a novel connection between knowledge distillation and image generation with a technique that distills a multi-step denoising process into a single step, resulting in a sampling speed […]
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This article was published as a part of the Data Science Blogathon. Introduction In this tutorial, you will learn how you can integrate common Natural Language Processing (NLP) functionalities into your application with minimal effort. We will be doing this using the ‘transformers‘ library provided by Hugging Face. 1. First, Install the transformers library. # Install the library !pip install transformers 2. Next, import the necessary functions. # Necessary imports from transformers import pipeline 3. Irrespective of the task that […]
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Introduction Python is a very high-level programming language, and it tends to stray away from anything remotely resembling internal data structure. Because of this, we usually don’t really need indices of a list to access its elements, however, sometimes we desperately need them. In this article, we will go over different approaches on how to access an index in Python’s for loop. How to Access Index in Python’s for Loop? The easiest, and most popular method to access the index […]
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