Although it currently has only around 2000 ones, with a lot not having a rotten tomatoes score, you can look at my notebook and change the code around a bit to have more!
Hello, want to update everyone that we added 50 new datasets to the BBND bringing us to 841 total. The Big Bad NLP Database offers links to free public/academic datasets for various tasks in NLP.
Some of the highlights include the IndoNLU benchmark and several datasets that were introduced at the EMNLP conference.
Today were announcing MinDiff! This is the first technique in our TensorFlow Model Remediation Library aimed at tackling fairness concerns in machine learning models. See how its used [goo.gle][twitter]
Two weeks ago, I used open data from Pennsylvania to investigate two claims of election fraud. Today, Pennsylvania took down the data. [self-promotion][reddit]/r/datasets
Two weeks ago I posted a dataset by the Pennsylvania Department of State about mail ballots, and used it to investigate two separate claims of election fraud:
[D] How do companies like Huggingface or Rasa make money?[reddit]/r/MachineLearning
Maybe slightly off-topic, but hear me out.
There are a few examples of companies in the machine learning industry that are open-sourcing a lot of their tech-stack and I assume, have the goal of making a return on that technology investment.
What is the business model of these companies and how do they generate revenue in the early and late stages?
It's fascinating because in other industries, rarely do you find examples that are throwing their tech out there and still able to fund their development, pay a salary, raise funding rounds etc.
Is there any public database for financial transactions, or at least a synthetic generated data set?[reddit]/r/datasets
Looking for financial transactions such as credit card payments, deposits and withdraws from banks or payments services. The most needed fields would be customer profile (age, gender, occupation, etc.) and transaction information (date, amount, location, detail ...).
[R] NeurIPS 2020 (Spotlight) Self-Supervised Relational Reasoning for Representation Learning[reddit]/r/MachineLearning
Hello everyone. I would like to share paper/code of our latest work entitled "Self-Supervised Relational Reasoning for Representation Learning" that has been accepted at NeurIPS 2020.
There are three key technical differences with contrastive methods like SimCLR: (i) the replacement of the reprojection head with a relation module, (ii) the use of a Binary Cross Entropy loss (BCE) instead of a contrastive loss, and (iii) the use of multiple augmentations instead of just two.
In the GitHub repository we have also released some pretrained models, minimalistic code of the method, a step-by-step notebook, and code to reproduce the experiments.
Abstract: In self-supervised learning, a system is tasked with achieving a surrogate objective by defining alternative targets on a set of unlabeled data. The aim is to build useful representations that can be used in downstream tasks, without costly manual annotation. In this work, we propose a novel self-supervised formulation of relational reasoning that allows a learner to bootstrap a signal from information implicit in unlabeled data. Training a relation head to discriminate how entities relate to themselves (intra-reasoning) and other entities (inter-reasoning), results in rich and descriptive representations in the underlying neural network backbone, which can be used in downstream tasks such as classification and image retrieval. We evaluate the proposed method following a rigorous experimental procedure, using standard datasets, protocols, and backbones. Self-supervised relational reasoning outperforms the best competitor in all conditions by an average 14% in accuracy, and the most recent state-of-the-art model by 3%. We link the effectiveness of the method to the maximization of a Bernoulli log-likelihood, which can be considered as a proxy for maximizing the mutual information, resulting in a more efficient objective with respect to the commonly used contrastive losses.