Guide to Robustness Gym: Unifying the NLP Evaluation Landscape -

Once the AI/ML model is built, researchers spend a considerable amount of time to come up with different parameters on which that model should be evaluated. Evaluation methods are problem-specific. Recently, Stanford University along with Salesforce Research and UNC-Chapel Hill has proposed a system for the evaluation of NLP pipelines, commonly referred to as Robustness Gym.

Read more: https://analyticsindiamag.com/guide-to-robustness-gym-unifying-the-nlp-evaluation-landscape/

#nlp #machine-learning

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Guide to Robustness Gym: Unifying the NLP Evaluation Landscape -

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.


Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.


#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

Guide to Robustness Gym: Unifying the NLP Evaluation Landscape -

Once the AI/ML model is built, researchers spend a considerable amount of time to come up with different parameters on which that model should be evaluated. Evaluation methods are problem-specific. Recently, Stanford University along with Salesforce Research and UNC-Chapel Hill has proposed a system for the evaluation of NLP pipelines, commonly referred to as Robustness Gym.

Read more: https://analyticsindiamag.com/guide-to-robustness-gym-unifying-the-nlp-evaluation-landscape/

#nlp #machine-learning

Oyente | An Analysis Tool for Smart Contracts

Oyente

An Analysis Tool for Smart Contracts

This repository is currently maintained by Xiao Liang Yu (@yxliang01). If you encounter any bugs or usage issues, please feel free to create an issue on our issue tracker.

Quick Start

A container with required dependencies configured can be found here. The image is however outdated. We are working on pushing the latest image to dockerhub for your convenience. If you experience any issue with this image, please try to build a new docker image by pulling this codebase before open an issue.

To open the container, install docker and run:

docker pull luongnguyen/oyente && docker run -i -t luongnguyen/oyente

To evaluate the greeter contract inside the container, run:

cd /oyente/oyente && python oyente.py -s greeter.sol

and you are done!

Note - If need the version of Oyente referred to in the paper, run the container from here

To run the web interface, execute docker run -w /oyente/web -p 3000:3000 oyente:latest ./bin/rails server

Custom Docker image build

docker build -t oyente .
docker run -it -p 3000:3000 -e "OYENTE=/oyente/oyente" oyente:latest

Open a web browser to http://localhost:3000 for the graphical interface.

Installation

Execute a python virtualenv

python -m virtualenv env
source env/bin/activate

Install Oyente via pip:

$ pip2 install oyente

Dependencies:

The following require a Linux system to fufill. macOS instructions forthcoming.

solc evm

Full installation

Install the following dependencies

solc

$ sudo add-apt-repository ppa:ethereum/ethereum
$ sudo apt-get update
$ sudo apt-get install solc

evm from go-ethereum

  1. https://geth.ethereum.org/downloads/ or
  2. By from PPA if your using Ubuntu

z3 Theorem Prover version 4.5.0.

Download the source code of version z3-4.5.0

Install z3 using Python bindings

$ python scripts/mk_make.py --python
$ cd build
$ make
$ sudo make install

Requests library

pip install requests

web3 library

pip install web3

Evaluating Ethereum Contracts

#evaluate a local solidity contract
python oyente.py -s <contract filename>

#evaluate a local solidity with option -a to verify assertions in the contract
python oyente.py -a -s <contract filename>

#evaluate a local evm contract
python oyente.py -s <contract filename> -b

#evaluate a remote contract
python oyente.py -ru https://gist.githubusercontent.com/loiluu/d0eb34d473e421df12b38c12a7423a61/raw/2415b3fb782f5d286777e0bcebc57812ce3786da/puzzle.sol

And that's it! Run python oyente.py --help for a list of options.

Paper

The accompanying paper explaining the bugs detected by the tool can be found here.

Miscellaneous Utilities

A collection of the utilities that were developed for the paper are in misc_utils. Use them at your own risk - they have mostly been disposable.

  1. generate-graphs.py - Contains a number of functions to get statistics from contracts.
  2. get_source.py - The get_contract_code function can be used to retrieve contract source from EtherScan
  3. transaction_scrape.py - Contains functions to retrieve up-to-date transaction information for a particular contract.

Benchmarks

Note: This is an improved version of the tool used for the paper. Benchmarks are not for direct comparison.

To run the benchmarks, it is best to use the docker container as it includes the blockchain snapshot necessary. In the container, run batch_run.py after activating the virtualenv. Results are in results.json once the benchmark completes.

The benchmarks take a long time and a lot of RAM in any but the largest of clusters, beware.

Some analytics regarding the number of contracts tested, number of contracts analysed etc. is collected when running this benchmark.

Contributing

Checkout out our contribution guide and the code structure here.

$ sudo apt-get install software-properties-common
$ sudo add-apt-repository -y ppa:ethereum/ethereum
$ sudo apt-get update
$ sudo apt-get install ethereum

Download Details:
Author: enzymefinance
Source Code: https://github.com/enzymefinance/oyente
License: GPL-3.0 license

#blockchain #smartcontract #ethereum

Kolby  Wyman

Kolby Wyman

1596726420

Why NLP Suffers From The Issue Of Underrepresented Languages

Natural language processing (NLP) has made several remarkable breakthroughs in recent years by providing implementations for a range of applications including optical character recognition, speech recognition, text simplification, question-answering, machine translation, dialogue systems and much more.

With the help of NLP, systems learn to identify spam emails, suggest medical articles or diagnosis related to a patient’s symptoms, etc. NLP has also been utilised as a critical ingredient in case of crucial decision-making systems such as criminal justice, credit, allocation of public resources, sorting a list of job candidates, to name a few.

However, despite all these critical use cases, NLP is still lagging and faces the problem of underrepresentation. For instance, one of the significant limitations of NLP is the ambiguity of words in languages. The ambiguity and imprecise characteristics of the natural languages make NLP difficult for machines to implement.

#developers corner #issues in nlp #natural language processing #nlp ai #nlp papers #nlp research

Best NLP Course in Chennai

Learn Natural Language Processing Course Training in Chennai. Natural Language Processing Training in Chennai provided by Expert level Professionals.

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