ProgressNow Colorado is the state’s largest and most effective multi-issue progressive communications organization in Colorado. The following is a guest post by Pankaj Parashar.Pankaj wrote to me about some pretty cool styled progress elements he created. I asked if he’d be interested in fleshing out the idea into an article about styling them in general. Like Harvey and Florence before it, Dorian's biggest danger comes when it stalls over land.
progress
- @kendoui
Packages 25
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Kendo UI TypeScript package gulp tasks
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SASS resources for the default Kendo UI theme
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Bootstrap v4 theme for Kendo UI
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Kendo UI platform-independent Charts library
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Kendo UI data query helpers
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Kendo UI typescript package exporting functions for Date manipulations
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Kendo UI Drawing
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Kendo UI File Saving Helper
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Kendo UI OOXML formatting library
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Kendo UI Popup common package
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This package is part of the [Kendo UI for jQuery](http://www.telerik.com/kendo-ui) suite.
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Progress® Telerik® Report Viewer for Angular
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Buttons Package for Angular
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Kendo UI Charts for Angular
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Date Inputs Package for Angular
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Dialog Package for Angular
Progress Energy
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Dropdowns Package for Angular
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Kendo UI for Angular Excel Export component
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Kendo UI Grid for Angular
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Inputs Package for Angular
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Localization Services - Kendo UI Angular
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Kendo UI Internationalization for Angular components
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Kendo UI Label for Angular
Progressbook
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Layout Package for Angular
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Localization messages - Kendo UI for Angular
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Table of contents
English
Vietnamese
Hindi
Progress Energy Carolinas
Chinese
For more tasks, datasets and results in Chinese, check out the Chinese NLP website.
French
Russian
Spanish
Portuguese
Korean
Nepali
Bengali
Persian
This document aims to track the progress in Natural Language Processing (NLP) and give an overviewof the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets.
It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech taggingas well as more recent ones such as reading comprehension and natural language inference. The main objectiveis to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for theirtask of interest, which serves as a stepping stone for further research. To this end, if there is a place where results for a task are already published and regularly maintained, such as a public leaderboard,the reader will be pointed there.
If you want to find this document again in the future, just go to nlpprogress.comor nlpsota.com in your browser.
Contributing
Guidelines
Results Results reported in published papers are preferred; an exception may be made for influential preprints.
Datasets Datasets should have been used for evaluation in at least one published paper besides the one that introduced the dataset.
Code We recommend to add a link to an implementation if available. You can add a Code column (see below) to the table if it does not exist.In the Code column, indicate an official implementation with Official.If an unofficial implementation is available, use Link (see below).If no implementation is available, you can leave the cell empty.
Adding a new result
If you would like to add a new result, you can just click on the small edit button in the top-rightcorner of the file for the respective task (see below).
Progress Residential
This allows you to edit the file in Markdown. Simply add a row to the corresponding table in thesame format. Make sure that the table stays sorted (with the best result on top). After you’ve made your change, make sure that the table still looks ok by clicking on the“Preview changes” tab at the top of the page. If everything looks good, go to the bottom of the page,where you see the below form.
Add a name for your proposed change, an optional description, indicate that you would like to“Create a new branch for this commit and start a pull request”, and click on “Propose file change”.
Adding a new dataset or task
For adding a new dataset or task, you can also follow the steps above. Alternatively, you can fork the repository.In both cases, follow the steps below:
- If your task is completely new, create a new file and link to it in the table of contents above.
- If not, add your task or dataset to the respective section of the corresponding file (in alphabetical order).
- Briefly describe the dataset/task and include relevant references.
- Describe the evaluation setting and evaluation metric.
- Show how an annotated example of the dataset/task looks like.
- Add a download link if available.
- Copy the below table and fill in at least two results (including the state-of-the-art) for your dataset/task (change Score to the metric of your dataset). If your dataset/task has multiple metrics, add them to the right of
Score. - Submit your change as a pull request.
| Model | Score | Paper / Source | Code |
|---|
Progress Meaning
Wish list
These are tasks and datasets that are still missing:
- Bilingual dictionary induction
- Discourse parsing
- Keyphrase extraction
- Knowledge base population (KBP)
- More dialogue tasks
- Semi-supervised learning
- Frame-semantic parsing (FrameNet full-sentence analysis)
Exporting into a structured format
You can extract all the data into a structured, machine-readable JSON format with parsed tasks, descriptions and SOTA tables.
The instructions are in structured/README.md.
Instructions for building the site locally
Instructions for building the website locally using Jekyll can be found here.