Eshan-Agarwal

Eshan Agarwal's starred repositories

system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

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low-level-design-primer

Dedicated Resources for the Low-Level System Design. Learn how to design and implement large-scale systems. Prep for the system design interview.

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datasets

TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

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Jigsaw-Unintended-Bias-in-Toxicity-Classification

At the end of 2017 the Civil Comments platform shut down and chose make their ~2m public comments from their platform available in a lasting open archive so that researchers could understand and improve civility in online conversations for years to come. Jigsaw sponsored this effort and extended annotation of this data by human raters for various toxic conversational attributes. In the data supplied for this competition, the text of the individual comment is found in the comment_text column. Each comment in Train has a toxicity label (target), and models should predict the target toxicity for the Test data. This attribute (and all others) are fractional values which represent the fraction of human raters who believed the attribute applied to the given comment. For evaluation, test set examples with target >= 0.5 will be considered to be in the positive class (toxic). The data also has several additional toxicity subtype attributes. Models do not need to predict these attributes for the competition, they are included as an additional avenue for research. Subtype attributes are: severe_toxicity obscene threat insult identity_attack sexual_explicit Additionally, a subset of comments have been labelled with a variety of identity attributes, representing the identities that are mentioned in the comment. The columns corresponding to identity attributes are listed below. Only identities with more than 500 examples in the test set (combined public and private) will be included in the evaluation calculation. These identities are shown in bold. male female transgender other_gender heterosexual homosexual_gay_or_lesbian bisexual other_sexual_orientation christian jewish muslim hindu buddhist atheist other_religion black white asian latino other_race_or_ethnicity physical_disability intellectual_or_learning_disability psychiatric_or_mental_illness other_disability Note that the data contains different comments that can have the exact same text. Different comments that have the same text may have been labeled with different targets or subgroups.

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Semantic-Text-Similarity

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memoryGame

This is a memory based card game.

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3-D-Animation-Cube-using-Html5-CSS3

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Quora_question_pair_similarity

Quora is a place to gain and share knowledge—about anything. It’s a platform to ask questions and connect with people who contribute unique insights and quality answers. This empowers people to learn from each other and to better understand the world. Over 100 million people visit Quora every month, so it's no surprise that many people ask similarly worded questions. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question, and make writers feel they need to answer multiple versions of the same question. Quora values canonical questions because they provide a better experience to active seekers and writers, and offer more value to both of these groups in the long term. > Credits: Kaggle __ Problem Statement __ Identify which questions asked on Quora are duplicates of questions that have already been asked. This could be useful to instantly provide answers to questions that have already been answered. We are tasked with predicting whether a pair of questions are duplicates or not.

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