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amazon.scienceanalyticsdeep learninggeospatiallast milelogisticsmachine learningoptimizationroutingtransportationurban
The 2021 Amazon Last Mile Routing Research Challenge was an innovative research initiative led by Amazon.com and supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics. Over a period of 4 months, participants were challenged to develop innovative machine learning-based methods to enhance classic optimization-based approaches to solve the travelling salesperson problem, by learning from historical routes executed by Amazon delivery drivers. The primary goal of the Amazon Last Mile Routing Research Challenge was to foster innovative applied research in r...
amazon.sciencecomputer visionmachine learning
The Amazon Bin Image Dataset contains over 500,000 images and metadata from bins of a pod in an operating Amazon Fulfillment Center. The bin images in this dataset are captured as robot units carry pods as part of normal Amazon Fulfillment Center operations.
amazon.sciencemachine learningnatural language processing
Amazon product questions and their answers, along with the public product information.
amazon.sciencemachine learningnatural language processing
Original StackExchange answers and their voice-friendly Reformulation.
amazon.sciencedeep learningmachine learningnatural language processingspeech recognition
Sentence classification datatasets with ASR Errors.
amazon.scienceconversation datamachine learningnatural language processing
This bucket contains the checkpoints used to reproduce the baseline results reported in the DialoGLUE benchmark hosted on EvalAI (https://evalai.cloudcv.org/web/challenges/challenge-page/708/overview). The associated scripts for using the checkpoints are located here: https://github.com/alexa/dialoglue. The associated paper describing the benchmark and checkpoints is here: https://arxiv.org/abs/2009.13570. The provided checkpoints include the CONVBERT model, a BERT-esque model trained on a large open-domain conversational dataset. It also includes the CONVBERT-DG and BERT-DG checkpoints descri...
amazon.scienceconversation datamachine learningnatural language processing
This dataset provides extra annotations on top of the publicly released Topical-Chat dataset(https://github.com/alexa/Topical-Chat) which will help in reproducing the results in our paper "Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems" (https://arxiv.org/abs/2005.12529?context=cs.CL). The dataset contains 5 files: train.json, valid_freq.json, valid_rare.json, test_freq.json and test_rare.json. Each of these files will have additional annotations on top of the original Topical-Chat dataset. These specific annotations are: dialogue act annotations a...
amazon.scienceinformation retrievaljsonnatural language processingtext analysis
A collection of sentences extracted from customer reviews labeled with their helpfulness score.
amazon.sciencemachine learningnatural language processing
This dataset provides labeled humor detection from product question answering systems. The dataset contains 3 csv files: Humorous.csv containing the humorous product questions, Non-humorous-unbiased.csv containing the non-humorous prodcut questions from the same products as the humorous one, and, Details →
amazon.sciencedialogmachine learningnatural language processing
Humor patterns used for quering Alexa traffic when creating the taxonomy described in the paper "“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents" by Shani C., Libov A., Tolmach S., Lewin-Eytan L., Maarek Y., and Shahaf D. (CHI LBW 2022). These patterns corrospond to the researchers' hypotheses regarding what humor types are likely to appear in Alexa traffic. These patterns were used for querying Alexa traffic to evaluate these hypotheses.
amazon.sciencemachine learningnatural language processing
This dataset provides product related questions and answers, including answers' quality labels, as as part of the paper 'IR Evaluation and Learning in the Presence of Forbidden Documents'.
amazon.sciencenatural language processing
See https://lowcontext-ner-gaz.s3.amazonaws.com/readme.html
amazon.sciencenatural language processing
Name Entity Recognition datasets containing short sentences and queries with low-context, including LOWNER, MSQ-NER, ORCAS-NER and Gazetteers (1.67 million entities). This release contains the multilingual versions of the datasets in Low Context Name Entity Recognition (NER) Datasets with Gazetteer.
amazon.sciencenatural language processingtext analysis
A collection of product reviews summaries automatically generated by PASS for 32 Amazon products from the FewSum dataset
amazon.sciencemachine learningnatural language processing
This dataset provides product related questions, including their textual content and gap, in hours, between purchase and posting time. Each question is also associated with related product details, including its id and title.
amazon.scienceinformation retrievalmachine learningnatural language processing
Voice-based refinements of product search
amazon.sciencemachine learningnatural language processing
This dataset provides how-to articles from wikihow.com and their summaries, written as a coherent paragraph. The dataset itself is available at wikisum.zip, and contains the article, the summary, the wikihow url, and an official fold (train, val, or test). In addition, human evaluation results are available at wikisum-human-eval...
amazon.sciencecomputer visiondeep learningmachine learning
Airborne Object Tracking (AOT) is a collection of 4,943 flight sequences of around 120 seconds each, collected at 10 Hz in diverse conditions. There are 5.9M+ images and 3.3M+ 2D annotations of airborne objects in the sequences. There are 3,306,350 frames without labels as they contain no airborne objects. For images with labels, there are on average 1.3 labels per image. All airborne objects in the dataset are labelled.
amazon.sciencecomputer visiondeep learninginformation retrievalmachine learningmachine translation
Amazon Berkeley Objects (ABO) is a collection of 147,702 product listings with multilingual metadata and 398,212 unique catalog images. 8,222 listings come with turntable photography (also referred as "spin" or "360º-View" images), as sequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique sequences. For 7,953 products, the collection also provides high-quality 3d models, as glTF 2.0 files.
amazon.sciencecomputer visionmachine learning
Fine-grained localized visual similarity and search for fashion.
amazon.sciencegraphtraffictransportation
Large-scale node-weighted conflict graphs for maximum weight independent set solvers