Iβve tried looking it up, and know it has something to do with the alphabet and using the letter sign, for another sign.
NLU 1.1.2 Release Notes
We are very happy to announce NLU 1.1.2 has been released with the integration of 30+ models and pipelines Bengali Named Entity Recognition, Hindi Word Embeddings,
and state-of-the-art transformer based OntoNotes models and pipelines from the incredible Spark NLP 2.7.3 Release in addition to a few bugfixes.
In addition to that, there is a new NLU Webinar video showcasing in detail
how to use NLU to analyze a crypto news dataset to extract keywords unsupervised and predict sentimential/emotional distributions of the dataset and much more!
Python's NLU library: 1,000+ models, 200+ Languages, State of the Art Accuracy, 1 Line of code - NLU NYC/DC NLP Meetup Webinar
Using just 1 line of Python code by leveraging the NLU library, which is powered by the award-winning Spark NLP.
This webinar covers, using live coding in real-time, how to deliver summarization, translation, unsupervised keyword extraction, emotion analysis, question answering, spell checking, named entity recognition, document classification, and other common NLP tasks. T his is all done with a single line of code, that works directly on Python strings or pandas data frames. Since NLU is based on Spark NLP, no code changes are required to scale processing to multi-core or cluster environment - integrating natively with Ray, Dask, or Spark data frames.
The recent releases for Spark NLP and NLU include pre-trained models for over 200 languages and language detection for 375 languages. This includes 20 languages families; non-Latin alphabets; languages that do not use spaces for word segmentation like Chinese, Japanese, and Korean; and languages written from right to left like Arabic, Farsi, Urdu, and Hebrew. We'll also cover some of the algorithms and models that are included. The code notebooks will be freely available online.
NLU 1.1.2 New Models and Pipelines
NLU 1.1.2 New Non-English Models
| Language | nlu.load() reference | Spark NLP Model reference | Type |
|---|---|---|---|
| Bengali | bn.ner | ner_jifs_glove_840B_300d |
Are there certain tenses shared amongst a number of Indo-European languages for example, but not common in other language families? Or certain ways of conjugating? Etc?
As per this Wikipedia article on classifiers, a classifier is "a word of affix that accompanies nouns and can be considered to 'classify' a noun depending on the type of its referent."
For example, in Korean we would use the word "gae (κ°)" to refer to "things" in general, but use "myung (λͺ )" to refer to people. This directly translates over into Chinese as well.
I've noticed that many European (or maybe just English?) languages don't rely on classifiers, and I'm curious what the history of this would be.
Even if East Asian languages, you don't really need the diverse set of classifier nouns that we have right now in order to get the message across. In Korean, it suffices to just say "gae" for everything. I also recall seeing a meme about people who are learning Chinese and how they'd use "ge (ε)" to refer to everything.
Too many people aren't aware of zipper merging and for some reason believe that merging over a mile and a half before the actual merge point and backing up traffic in one lane is morally superior than riding the other lane until the correct merge point. It is a huge hindrance on traffic, and people legitimately get pissed when someone rides the other lane all the way to the end.
Believe it or not, the person riding that lane to the end is doing the right thing!
My wife watched a small accident today because the person in the left lane absolutely would not let the person in the right lane over before the lane ended. Why the hell would you want to sit in a mile and a half long line instead of cutting it in half and smoothly merging without having to stop. Plus, if people know how zipper merging works and understand that you merge every other car, then traffic doesn't need to come to a standstill and becomes far more predictable.
In the old city I used to live in, they started putting up zipper merge signs that explained not to merge early and told you precisely when to merge. It took people a little bit to catch on but after a few days traffic was much better in that area and it no longer resulted in a mile long back-up. I genuinely think signs explaining what is right would go a long way.
NLU 1.1.2 Release Notes
We are very happy to announce NLU 1.1.2 has been released with the integration of 30+ models and pipelines Bengali Named Entity Recognition, Hindi Word Embeddings,
and state-of-the-art transformer based OntoNotes models and pipelines from the incredible Spark NLP 2.7.3 Release in addition to a few bugfixes.
In addition to that, there is a new NLU Webinar video showcasing in detail
how to use NLU to analyze a crypto news dataset to extract keywords unsupervised and predict sentimential/emotional distributions of the dataset and much more!
Python's NLU library: 1,000+ models, 200+ Languages, State of the Art Accuracy, 1 Line of code - NLU NYC/DC NLP Meetup Webinar
Using just 1 line of Python code by leveraging the NLU library, which is powered by the award-winning Spark NLP.
This webinar covers, using live coding in real-time, how to deliver summarization, translation, unsupervised keyword extraction, emotion analysis, question answering, spell checking, named entity recognition, document classification, and other common NLP tasks. T his is all done with a single line of code, that works directly on Python strings or pandas data frames. Since NLU is based on Spark NLP, no code changes are required to scale processing to multi-core or cluster environment - integrating natively with Ray, Dask, or Spark data frames.
The recent releases for Spark NLP and NLU include pre-trained models for over 200 languages and language detection for 375 languages. This includes 20 languages families; non-Latin alphabets; languages that do not use spaces for word segmentation like Chinese, Japanese, and Korean; and languages written from right to left like Arabic, Farsi, Urdu, and Hebrew. We'll also cover some of the algorithms and models that are included. The code notebooks will be freely available online.
NLU 1.1.2 New Models and Pipelines
NLU 1.1.2 New Non-English Models
| Language | nlu.load() reference | Spark NLP Model reference | Type |
|---|---|---|---|
| Bengali | bn.ner | ner_jifs_glove_840B_300d |
NLU 1.1.2 Release Notes
We are very happy to announce NLU 1.1.2 has been released with the integration of 30+ models and pipelines Bengali Named Entity Recognition, Hindi Word Embeddings,
and state-of-the-art transformer based OntoNotes models and pipelines from the incredible Spark NLP 2.7.3 Release in addition to a few bugfixes.
In addition to that, there is a new NLU Webinar video showcasing in detail
how to use NLU to analyze a crypto news dataset to extract keywords unsupervised and predict sentimential/emotional distributions of the dataset and much more!
Python's NLU library: 1,000+ models, 200+ Languages, State of the Art Accuracy, 1 Line of code - NLU NYC/DC NLP Meetup Webinar
Using just 1 line of Python code by leveraging the NLU library, which is powered by the award-winning Spark NLP.
This webinar covers, using live coding in real-time, how to deliver summarization, translation, unsupervised keyword extraction, emotion analysis, question answering, spell checking, named entity recognition, document classification, and other common NLP tasks. T his is all done with a single line of code, that works directly on Python strings or pandas data frames. Since NLU is based on Spark NLP, no code changes are required to scale processing to multi-core or cluster environment - integrating natively with Ray, Dask, or Spark data frames.
The recent releases for Spark NLP and NLU include pre-trained models for over 200 languages and language detection for 375 languages. This includes 20 languages families; non-Latin alphabets; languages that do not use spaces for word segmentation like Chinese, Japanese, and Korean; and languages written from right to left like Arabic, Farsi, Urdu, and Hebrew. We'll also cover some of the algorithms and models that are included. The code notebooks will be freely available online.
NLU 1.1.2 New Models and Pipelines
NLU 1.1.2 New Non-English Models
| Language | nlu.load() reference | Spark NLP Model reference | Type |
|---|---|---|---|
| Bengali | bn.ner | ner_jifs_glove_840B_300d |
