NLP-progress
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NLP-progress | flair | |
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17 | 9 | |
22,296 | 13,558 | |
- | 1.0% | |
3.2 | 9.4 | |
2 months ago | 6 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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
- [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)
flair
- Flair: A simple framework for state-of-the-art Natural Language Processing
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Artificial Intelligence sentiment analysis of the Harry Potter movies. The greener the edge the happier the conversations, the bigger the edge the more they talk. Made by me.
The code of the module is available there for easy access: https://github.com/flairNLP/flair
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The Spacy NER model for Spanish is terrible
Had the same experience with the german model in spacy (but tbh, the quailty of my textdata was bad). A bert based approach with flair really improved my results. I think there is a spanish pretrained model also available
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How to create a dataset for training NER models when you only have entity data
We have a list of entities in text files separated with a new line. We intend to train the flair model to detect these entities in text, but NER models require the entity to be labeled in a paragraph with BOI format.
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Preparing data for training NER models
Training most of the Named Entity Recognition (NER) models for example Flair usually needs to format data in BOI tagging) scheme as shown below where each sentence is separated by blank line
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German POS Corpus for Commercial use
I had the same problem a couple years ago. I think Flair, form Zalando uses a different Corpus. However, it's not great and I am pretty sure they are infringing the license anyway...
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Advice for how to approach classifying apartment posts on facebook?
For example, my first approach to the pet sentences would be to label all sentences within a respective text corpus containing according information for either yes or no. You would then convert this to a tertiary tag set, something like ["pet allowed", "pet not allowed", "irrelevant"]. You could then try out a model based on SentenceBert, other sentence-level embeddings/language models or 1D CNNs for this. flairNLP (https://github.com/flairNLP/flair) is a small, little framework which provides comfortable high-level access to different common language models which integrates perfectly with pyTorch.
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SpaCy VS Transformers for NER
For NER, if you don't need the full toolkit of spacy, I'd highly recommend checking out Flair. It will likely run faster than transformer-based models (like en_core_web_trf) and it tends to be one of the best performing approaches to NER.
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[D] NLP Q: How to extract this part from a messy short text?
You then train the whole thing on sequences where each position has a label that is begin/inside/outside and thus you can calculate cross-entropy loss. So all in all it is basically: https://github.com/flairNLP/flair, https://huggingface.co/transformers/model_doc/distilbert.html#tfdistilbertforsequenceclassification or any huggingface model "for sequence classificaiton" or but just char based instead of word based. The CRF layer (as included in flair) is optional but may be useful.
What are some alternatives?
nlp_tasks - Natural Language Processing Tasks and References
spacy-models - 💫 Models for the spaCy Natural Language Processing (NLP) library
wtpsplit - Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
SymSpell - SymSpell: 1 million times faster spelling correction & fuzzy search through Symmetric Delete spelling correction algorithm
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
awesome-hungarian-nlp - A curated list of NLP resources for Hungarian
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
nlprule - A fast, low-resource Natural Language Processing and Text Correction library written in Rust.
gensim - Topic Modelling for Humans
OPUS-MT-train - Training open neural machine translation models
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)