NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. (by sebastianruder)
checklist
Beyond Accuracy: Behavioral Testing of NLP models with CheckList (by marcotcr)
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| NLP-progress | checklist | |
|---|---|---|
| 17 | 2 | |
| 22,296 | 1,983 | |
| - | - | |
| 3.2 | 2.9 | |
| 2 months ago | 4 months ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NLP-progress
Posts with mentions or reviews of NLP-progress.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-11.
- [Discussion] Checklist of seminal NLP papers
- NLP research status
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[D] How difficult/easy is to learn NLP once you have experience in a CV?
One thing is that NLP is a set of wildly different problems which share some aspects, but often use quite different techniques and assumptions about their datasets. So even if you would have NLP experience, if you'd need to start on a substantially different NLP task, you can't just apply what you know and succeed, you have to review "how things are done" for that problem domain. For a quick overview, sites like https://nlpprogress.com/ can be helpful to see what methods are used; and, perhaps even more importantly, how people are modeling the actual task.
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Upcoming App Announcement: Lemmatize, a Foreign Language Reader
A standard step in Chinese text processing is word segmentation, which deals with this problem.
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Is there as site tracking computer vision process?
NLP has a github project tracking NLP progress, https://github.com/sebastianruder/NLP-progress. I wanna know if there is one tracking computer vision progress.
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[P] NLP "tl;dr" Notes on Transformers
It would also be cool to have some charts with parameter density and even overall effectiveness (a tl;dr version of SOTA-trackers, maybe?) if that doesn't prove too infeasible.
- What are state-of-the-art methods for abstractive text summarization ?
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BreadPanes 81: "They/Them"
As I said It increase ambiguity and cognitive overheard, needlessly given that "it" exists. Moreover it also make it harder for artificial intelligence to understand human text https://github.com/sebastianruder/NLP-progress/blob/master/english/coreference_resolution.md
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[Request] Curated Advanced NLP Resources
I could not find it on the internet (including on GitHub, Kaggle, Medium, or Reddit.) And, I know about NLP Progress and The Super Duper NLP Repo.
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How do you guys find/ keep up to date with the latest NLP papers?
For someone who needs to be on top of the latest research - Twitter (distraction-prone, marketing-friendly, instantly-gratifying, quick), newsletters in ML + NLP (https://jack-clark.net/, ruder.io, offconvex.org, etc.) (distraction-free, generic, time-consuming), SOTA chasing (https://paperswithcode.com/, http://nlpprogress.com/) (distraction-free, generic + focused, code-friendly)
checklist
Posts with mentions or reviews of checklist.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-21.
- Behavioral Testing of NLP Models with CheckList
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What are some classification tasks where BERT-based models don't work well? In a similar vein, what are some generative tasks where fine-tuning GPT-2/LM does not work well?
Interesting. Does the model fail on specific nuanced examples or all sentences in general? For e.g, in the Checklist work: https://github.com/marcotcr/checklist, there are examples of some specific sentences, but overall the model works well in a lot of cases. Do you have a code repo/notebook somewhere for experimenting with emotion classification?
What are some alternatives?
When comparing NLP-progress and checklist you can also consider the following projects:
nlp_tasks - Natural Language Processing Tasks and References
wtpsplit - Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
SymSpell - SymSpell: 1 million times faster spelling correction & fuzzy search through Symmetric Delete spelling correction algorithm
awesome-hungarian-nlp - A curated list of NLP resources for Hungarian
nlprule - A fast, low-resource Natural Language Processing and Text Correction library written in Rust.
OPUS-MT-train - Training open neural machine translation models
tldr-transformers - The "tl;dr" on a few notable transformer papers (pre-2022).
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
cndict
Neural-Machine-Translated-communication-system - The model is designed to train a single and large neural network in order to predict correct translation by reading the given sentence.
NLP-progress vs nlp_tasks
NLP-progress vs wtpsplit
NLP-progress vs SymSpell
NLP-progress vs awesome-hungarian-nlp
NLP-progress vs nlprule
NLP-progress vs OPUS-MT-train
NLP-progress vs tldr-transformers
NLP-progress vs flair
NLP-progress vs cndict
NLP-progress vs Neural-Machine-Translated-communication-system