In mathematics, the Wasserstein or Kantorovich–Rubinstein metric or distance is a distance function defined between probability distributions on a given metric space M {\displaystyle M} M.
Intuitively, if each distribution is viewed as a unit amount of "dirt" piled on M {\displaystyle M} M, the metric is the minimum "cost" of turning one pile into the other, which is assumed to be the amount of dirt that needs to be moved times the mean distance it has to be moved. Because of this analogy, the metric is known in computer science as the earth mover's distance.
We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90%* quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of cases. The cost of training Vicuna-13B is around $300. The code and weights, along with an online demo, are publicly available for non-commercial use.
TLDR — Extractive question answering is an important task for providing a good user experience in many applications. The popular Retriever-Reader framework for QA using BERT can be difficult to scale…
In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning — from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics. The process of learning, recognizing, and extracting these topics across a collection of documents is called topic modeling.
In this post, we will explore topic modeling through 4 of the most popular techniques today: LSA, pLSA, LDA, and the newer, deep learning-based lda2vec.
You want to discern how many clusters we have (or, if you prefer, how many gaussians components generated the data), and you don’t have information about the “ground truth”. A real case, where data do not have the nicety of behaving good as the simulated ones.
Definition of NLP coherence scores, in particular intrinsic UMass measure and PMI.
Human judgment not being correlated to perplexity (or likelihood of unseen documents) is the motivation for more work trying to model the human judgment. This is by itself a hard task as human judgment is not clearly defined; for example, two experts can disagree on the usefulness of a topic.
One can classify the methods addressing this problem into two categories. \textit{Intrinsic} methods that do not use any external source or task from the dataset, whereas \textit{extrinsic} methods use the discovered topics for external tasks, such as information retrieval [Wei06], or use external statistics to evaluate topics.
Facebook Research open sourced a great project recently – fastText, a fast (no surprise) and effective method to learn word representations and perform text classification. I was curious about comparing these embeddings to other commonly used embeddings, so word2vec seemed like the obvious choice, especially considering fastText embeddings are an extension of word2vec.
The main aim of SenticNet is to make the conceptual and affective information conveyed by natural language (meant for human consumption) more easily-accessible to machines.
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data
The data is uniformly distributed on Riemannian manifold;
The Riemannian metric is locally constant (or can be approximated as such);
The manifold is locally connected.
From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.
MACE (Multi-Annotator Competence Estimation) is an implementation of an item-response model that let's you evaluate redundant annotations of categorical data. It provides competence estimates of the individual annotators and the most likely answer to each item.
If we have 10 annotators answer a question, and five answer with 'yes' and five with 'no' (a surprisingly frequent event), we would normaly have to flip a coin to decide what the right answer is. If we knew, however, that one of the people who answered 'yes' is an expert on the question, while one of the others just alwas selects 'no', we would take this information into account to weight their answers. MACE does exactly that. It tries to find out which annotators are more trustworthy and upweighs their answers. All you need to provide is a CSV file with one item per line.
In tests, MACE's trust estimates correlated highly wth the annotators' true competence, and it achieved accuracies of over 0.9 on several test sets. MACE can take annotated items into account, if they are available. This helps to guide the training and improves accuracy.
Build document-based question-answering systems using LangChain, Pinecone, LLMs like GPT-4, and semantic search for precise, context-aware AI solutions.
In this article, I am going to show you how to choose the number of principal components when using principal component analysis for dimensionality reduction.
In the first section, I am going to give you a short answer for those of you who are in a hurry and want to get something working. Later, I am going to provide a more extended explanation for those of you who are interested in understanding PCA.
As data engineers, you might have heard the terms functional data pipeline, factory pattern, singleton pattern, etc. One can quickly look up the implementation, but it can be tricky to understand what they are precisely and when to (& when not to) use them. Blindly following a pattern can help in some cases, but not knowing the caveats of a design will lead to hard-to-maintain and brittle code! While writing clean and easy-to-read code takes years of experience, you can accelerate that by understanding the nuances and reasoning behind each pattern. Imagine being able to design an implementation that provides the best extensibility and maintainability! Your colleagues (& future self) will be extremely grateful, your feature delivery speed will increase, and your boss will highly value your opinion. In this post, we will go over the specific code design patterns used for data pipelines, when and why to use them, and when not to use them, and we will also go over a few python specific techniques to help you write better pipelines. By the end of this post, you will be able to identify patterns in your data pipelines and apply the appropriate code design patterns. You will also be able to take advantage of pythonic features to write bug-free, maintainable code that is a joy to work on!
The pulearn Python package provide a collection of scikit-learn wrappers to several positive-unlabled learning (PU-learning) methods.
Features
Scikit-learn compliant wrappers to prominent PU-learning methods.
Fully tested on Linux, macOS and Windows systems.
Compatible with Python 3.5+.
The recent release of this open-source project, LlamaFS, addresses the challenges associated with traditional file management systems, particularly in the context of overstuffed download folders, inefficient file organization, and the limitations of knowledge-based organization. These issues arise due to the manual nature of file sorting, which often leads to inconsistent structures and difficulty finding specific files. The disorganization in the file system hampers productivity and makes it challenging to locate important files quickly.
D-Tale is an interactive web-based library that consists of a Flask backend and a React front-end serving as an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. Currently, this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex.
When a word appears in different contexts, its vector gets moved in different directions during updates. The final vector then represents some sort of weighted average over the various contexts. Averaging over vectors that point in different directions typically results in a vector that gets shorter with increasing number of different contexts in which the word appears. For words to be used in many different contexts, they must carry little meaning. Prime examples of such insignificant words are high-frequency stop words, which are indeed represented by short vectors despite their high term frequencies ...
This page is a distribution site for movie-review data for use in sentiment-analysis experiments. Available are collections of movie-review documents labeled with respect to their overall sentiment polarity (positive or negative) or subjective rating (e.g., "two and a half stars") and sentences labeled with respect to their subjectivity status (subjective or objective) or polarity. These data sets were introduced in the following papers:
In this article, we will explore how we can use Llama2 for Topic Modeling without the need to pass every single document to the model. Instead, we will leverage BERTopic, a modular topic modeling technique that can use any LLM for fine-tuning topic representations.
I recently created a demo for some prospective clients of mine, demonstrating how to use Large Language Models (LLMs) together with graph databases like Neo4J.
The two have a lot of interesting interactions, namely that you can now create knowledge graphs easier than ever before, by having AI find the graph entities and relationships from your unstructured data, rather than having to do all that manually.
On top of that, graph databases also have some advantages for Retrieval Augmented Generation (RAG) applications compared to vector search, which is currently the prevailing approach to RAG.
These measurements are indispensable for tracking the results of your chatbot, identifying any stumbling blocks and continuously improving its performance. But which metrics should you choose?
Perplexity is a useful metric to evaluate models in Natural Language Processing (NLP). This article will cover the two ways in which it is normally defined and the intuitions behind them. A language…
Recent explosion in the popularity of large language models like ChatGPT has led to their increased usage in classical NLP tasks like language classification. This involves providing a context…
We’ve done a lot of looking over our shoulders at OpenAI. Who will cross the next milestone? What will the next move be?
But the uncomfortable truth is, we aren’t positioned to win this arms race and neither is OpenAI. While we’ve been squabbling, a third faction has been quietly eating our lunch.
I’m talking, of course, about open source. Plainly put, they are lapping us. Things we consider “major open problems” are solved and in people’s hands today.
In today’s rapidly evolving tech landscape, the integration of advanced language models with robust data management systems is opening new horizons for data processing and analytics. One of the most…
Since 2021 we’ve been releasing the annual State of Data Engineering Report, a compilation of all the relevant categories that have a direct impact on data engineering infrastructure.
In 2024, we see 3 primary trends that influence the categories which will be covered in this report.
Comparing machine learning methods and selecting a final model is a common operation in applied machine learning.
Models are commonly evaluated using resampling methods like k-fold cross-validation from which mean skill scores are calculated and compared directly. Although simple, this approach can be misleading as it is hard to know whether the difference between mean skill scores is real or the result of a statistical fluke.
Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text.
LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them.
LSA itself is an unsupervised way of uncovering synonyms in a collection of documents.
To start, we take a look how Latent Semantic Analysis is used in Natural Language Processing to analyze relationships between a set of documents and the terms that they contain. Then we go steps further to analyze and classify sentiment. We will review Chi Squared for feature selection along the way.
In probability theory and statistics, the Jensen–Shannon divergence is a method of measuring the similarity between two probability distributions. It is also known as information radius (IRad)[1] or total divergence to the average.[2] It is based on the Kullback–Leibler divergence, with some notable (and useful) differences, including that it is symmetric and it always has a finite value. The square root of the Jensen–Shannon divergence is a metric often referred to as Jensen-Shannon distance.[3][4][5]
In natural language processing (NLP) field, it is hard to augmenting text due to high complexity of language. Not every word we can replace it by others such as a, an, the. Also, not every word has synonym. Even changing a word, the context will be totally difference. On the other hand, generating augmented image in computer vision area is relative easier. Even introducing noise or cropping out portion of image, model can still classify the image.
There are currently few datasets appropriate for training and evaluating models for non-goal-oriented dialogue systems (chatbots); and equally problematic, there is currently no standard procedure for evaluating such models beyond the classic Turing test.
The aim of our competition is therefore to establish a concrete scenario for testing chatbots that aim to engage humans, and become a standard evaluation tool in order to make such systems directly comparable.
I'm not sure what these vectors are, since BERT does not generate meaningful sentence vectors. It seems that this is is doing average pooling over the word tokens to get a sentence vector, but we never suggested that this will generate meaningful sentence representations. And even if they are decent representations when fed into a DNN trained for a downstream task, it doesn't mean that they will be meaningful in terms of cosine distance. (Since cosine distance is a linear space where all dimensions are weighted equally).
In this article, we’ll look at Weakly Supervised Learning (WSL), which provides a solution by leveraging “weak” annotations to learn the task. But before we dive deeper into the techniques, it is worth exploring the various types of WSL techniques and the sections we intend to cover in this article.
In recent years, the real-world impact of machine learning (ML) has grown in leaps and bounds. In large part, this is due to the advent of deep learning models, which allow practitioners to get state-of-the-art scores on benchmark datasets without any hand-engineered features. Given the availability of multiple open-source ML frameworks like TensorFlow and PyTorch, and an abundance of available state-of-the-art models, it can be argued that high-quality ML models are almost a commoditized resource now. There is a hidden catch, however: the reliance of these models on massive sets of hand-labeled training data.
These hand-labeled training sets are expensive and time-consuming to create — often requiring person-months or years to assemble, clean, and debug — especially when domain expertise is required. On top of this, tasks often change and evolve in the real world. For example, labeling guidelines, granularities, or downstream use cases often change, necessitating re-labeling (e.g., instead of classifying reviews only as positive or negative, introducing a neutral category). For all these reasons, practitioners have increasingly been turning to weaker forms of supervision, such as heuristically generating training data with external knowledge bases, patterns/rules, or other classifiers. Essentially, these are all ways of programmatically generating training data—or, more succinctly, programming training data.
We begin by reviewing areas of ML that are motivated by the problem of labeling training data, and then describe our research on modeling and integrating a diverse set of supervision sources. We also discuss our vision for building data management systems for the massively multi-task regime with tens or hundreds of weakly supervised dynamic tasks interacting in complex and varied ways. Check out the our research blog for detailed discussions of these topics and more!
Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational - GitHub - gventuri/pandas-ai: Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational
The ultimate guide to chatbot analytics. Find out what bot metrics and KPIs you should measure and discover easy ways to optimize your chatbot performance.
Large language models (LLMs) have proven to be valuable tools, but they often lack reliability. Many instances have surfaced where LLM-generated responses included false information. Specifically…
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S. Basu, A. Banerjee, und R. Mooney. Proceedings of the 2004 SIAM International Conference on Data Mining, Seite 333--344. Lake Buena Vista, FL, Society for Industrial and Applied Mathematics, (April 2004)
C. Baziotis, N. Pelekis, und C. Doulkeridis. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Seite 390-395. Association for Computational Linguistics, (2017)