1624536552
There are various benefits when considering the tokenized debt.
Universality: As Debt is a global level concept that helps to operate and analyze the corresponding representations of debt that lead to a universal debt transportation.
Fractionalization: It is the ability to fractionalize the debt by considering the opens with the asset to a brand new and large category of investors.
OTC Trading: Many debt transportation trading takes place over the counter (OTC) manner across the globe. In general, security tokens can streamline by digitizing the existing processes.
Composability: Here, Debt is composed easily with the representation by making the real estate leases as a security token symbolizing the collateralized debt obligation (CDO).
Futures: Debt acts as the perfect way to lead futures and its derivatives towards a security tokens. Debt build a fairly incidental model rather considering the typical futures models
To Know More >> https://www.securitytokenizer.io/
1659601560
We are all in the gutter, but some of us are looking at the stars.
-- Oscar Wilde
WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.
Are you using WordsCounted to do something interesting? Please tell me about it.
Visit this website for one example of what you can do with WordsCounted.
["Bayrūt"]
and not ["Bayr", "ū", "t"]
, for example.Add this line to your application's Gemfile:
gem 'words_counted'
And then execute:
$ bundle
Or install it yourself as:
$ gem install words_counted
Pass in a string or a file path, and an optional filter and/or regexp.
counter = WordsCounted.count(
"We are all in the gutter, but some of us are looking at the stars."
)
# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")
.count
and .from_file
are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter
initialized with the tokens. The WordsCounted::Tokeniser
and WordsCounted::Counter
classes can be used alone, however.
WordsCounted.count(input, options = {})
Tokenises input and initializes a WordsCounted::Counter
object with the resulting tokens.
counter = WordsCounted.count("Hello Beirut!")
Accepts two options: exclude
and regexp
. See Excluding tokens from the analyser and Passing in a custom regexp respectively.
WordsCounted.from_file(path, options = {})
Reads and tokenises a file, and initializes a WordsCounted::Counter
object with the resulting tokens.
counter = WordsCounted.from_file("hello_beirut.txt")
Accepts the same options as .count
.
The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.
Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.
#tokenise([pattern: TOKEN_REGEXP, exclude: nil])
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise
# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")
# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)
See Excluding tokens from the analyser and Passing in a custom regexp for more information.
The WordsCounted::Counter
class allows you to collect various statistics from an array of tokens.
#token_count
Returns the token count of a given string.
counter.token_count #=> 15
#token_frequency
Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.
counter.token_frequency
[
["the", 2],
["are", 2],
["we", 1],
# ...
["all", 1]
]
#most_frequent_tokens
Returns a hash where each key-value pair is a token and its frequency.
counter.most_frequent_tokens
{ "are" => 2, "the" => 2 }
#token_lengths
Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.
counter.token_lengths
[
["looking", 7],
["gutter", 6],
["stars", 5],
# ...
["in", 2]
]
#longest_tokens
Returns a hash where each key-value pair is a token and its length.
counter.longest_tokens
{ "looking" => 7 }
#token_density([ precision: 2 ])
Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision
argument, which must be a float.
counter.token_density
[
["are", 0.13],
["the", 0.13],
["but", 0.07 ],
# ...
["we", 0.07 ]
]
#char_count
Returns the char count of tokens.
counter.char_count #=> 76
#average_chars_per_token([ precision: 2 ])
Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.
counter.average_chars_per_token #=> 4
#uniq_token_count
Returns the number of unique tokens.
counter.uniq_token_count #=> 13
You can exclude anything you want from the input by passing the exclude
option. The exclude option accepts a variety of filters and is extremely flexible.
:odd?
.tokeniser =
WordsCounted::Tokeniser.new(
"Magnificent! That was magnificent, Trevor."
)
# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]
# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]
# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]
# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]
# Using an array
tokeniser = WordsCounted::Tokeniser.new(
"Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]
The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.
/[\p{Alpha}\-']+/
You can pass your own criteria as a Ruby regular expression to split your string as desired.
For example, if you wanted to include numbers, you can override the regular expression:
counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]
Use the from_file
method to open files. from_file
accepts the same options as .count
. The file path can be a URL.
counter = WordsCounted.from_file("url/or/path/to/file.text")
A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.
counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency
[
["do", 2],
["how", 1],
["you", 1],
["-you", 1], # WTF, mate!
["are", 1],
# ...
]
In this example -you
and you
are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.
The program will normalise (downcase) all incoming strings for consistency and filters.
def self.from_url
# open url and send string here after removing html
end
See contributors.
git checkout -b my-new-feature
)git commit -am 'Add some feature'
)git push origin my-new-feature
)Author: abitdodgy
Source code: https://github.com/abitdodgy/words_counted
License: MIT license
#ruby #ruby-on-rails
1658068560
WordsCounted
We are all in the gutter, but some of us are looking at the stars.
-- Oscar Wilde
WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.
["Bayrūt"]
and not ["Bayr", "ū", "t"]
, for example.Add this line to your application's Gemfile:
gem 'words_counted'
And then execute:
$ bundle
Or install it yourself as:
$ gem install words_counted
Pass in a string or a file path, and an optional filter and/or regexp.
counter = WordsCounted.count(
"We are all in the gutter, but some of us are looking at the stars."
)
# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")
.count
and .from_file
are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter
initialized with the tokens. The WordsCounted::Tokeniser
and WordsCounted::Counter
classes can be used alone, however.
WordsCounted.count(input, options = {})
Tokenises input and initializes a WordsCounted::Counter
object with the resulting tokens.
counter = WordsCounted.count("Hello Beirut!")
Accepts two options: exclude
and regexp
. See Excluding tokens from the analyser and Passing in a custom regexp respectively.
WordsCounted.from_file(path, options = {})
Reads and tokenises a file, and initializes a WordsCounted::Counter
object with the resulting tokens.
counter = WordsCounted.from_file("hello_beirut.txt")
Accepts the same options as .count
.
The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.
Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.
#tokenise([pattern: TOKEN_REGEXP, exclude: nil])
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise
# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")
# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)
See Excluding tokens from the analyser and Passing in a custom regexp for more information.
The WordsCounted::Counter
class allows you to collect various statistics from an array of tokens.
#token_count
Returns the token count of a given string.
counter.token_count #=> 15
#token_frequency
Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.
counter.token_frequency
[
["the", 2],
["are", 2],
["we", 1],
# ...
["all", 1]
]
#most_frequent_tokens
Returns a hash where each key-value pair is a token and its frequency.
counter.most_frequent_tokens
{ "are" => 2, "the" => 2 }
#token_lengths
Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.
counter.token_lengths
[
["looking", 7],
["gutter", 6],
["stars", 5],
# ...
["in", 2]
]
#longest_tokens
Returns a hash where each key-value pair is a token and its length.
counter.longest_tokens
{ "looking" => 7 }
#token_density([ precision: 2 ])
Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision
argument, which must be a float.
counter.token_density
[
["are", 0.13],
["the", 0.13],
["but", 0.07 ],
# ...
["we", 0.07 ]
]
#char_count
Returns the char count of tokens.
counter.char_count #=> 76
#average_chars_per_token([ precision: 2 ])
Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.
counter.average_chars_per_token #=> 4
#uniq_token_count
Returns the number of unique tokens.
counter.uniq_token_count #=> 13
You can exclude anything you want from the input by passing the exclude
option. The exclude option accepts a variety of filters and is extremely flexible.
:odd?
.tokeniser =
WordsCounted::Tokeniser.new(
"Magnificent! That was magnificent, Trevor."
)
# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]
# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]
# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]
# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]
# Using an array
tokeniser = WordsCounted::Tokeniser.new(
"Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]
The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.
/[\p{Alpha}\-']+/
You can pass your own criteria as a Ruby regular expression to split your string as desired.
For example, if you wanted to include numbers, you can override the regular expression:
counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]
Use the from_file
method to open files. from_file
accepts the same options as .count
. The file path can be a URL.
counter = WordsCounted.from_file("url/or/path/to/file.text")
A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.
counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency
[
["do", 2],
["how", 1],
["you", 1],
["-you", 1], # WTF, mate!
["are", 1],
# ...
]
In this example -you
and you
are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.
The program will normalise (downcase) all incoming strings for consistency and filters.
def self.from_url
# open url and send string here after removing html
end
Are you using WordsCounted to do something interesting? Please tell me about it.
Visit this website for one example of what you can do with WordsCounted.
Contributors
See contributors.
git checkout -b my-new-feature
)git commit -am 'Add some feature'
)git push origin my-new-feature
)Author: Abitdodgy
Source Code: https://github.com/abitdodgy/words_counted
License: MIT license
1622197808
SafeMoon is a decentralized finance (DeFi) token. This token consists of RFI tokenomics and auto-liquidity generating protocol. A DeFi token like SafeMoon has reached the mainstream standards under the Binance Smart Chain. Its success and popularity have been immense, thus, making the majority of the business firms adopt this style of cryptocurrency as an alternative.
A DeFi token like SafeMoon is almost similar to the other crypto-token, but the only difference being that it charges a 10% transaction fee from the users who sell their tokens, in which 5% of the fee is distributed to the remaining SafeMoon owners. This feature rewards the owners for holding onto their tokens.
Read More @ https://bit.ly/3oFbJoJ
#create a defi token like safemoon #defi token like safemoon #safemoon token #safemoon token clone #defi token
1607417383
DK Business Patron has been famous not only for their dedicated and inventive services but also because they have been foraying into services that no other Outsourcing provider looks up to in order to serve their clients from a 360 degree view. This Outsourcing Company keeps coming up with inventive and out of the box functions that they can provide their clients with at utmost level of efficiency and effectiveness through the skilled personnel that they have in their teams.
In a recent development it has come to notice that DK Business Patron has launched a global debt collection service segment for their clients across countries. The development is quite a shocking deal for most of the Indian Outsourcing service providers as the launch of a global debt collection unit is unconventional and quiet not much heard of before this. Debt collection deals with recovery of bad loans that are pending for any business. This is a segment that organisations often overlook and do not consider much important as it does not account for centric business functions.
DK Business Patron while launching this new segment has stated that they wanted to tap the yet untapped potential in debt collection services that would aid their clients in recovery of debt benefiting them in terms of improved cash flows and would also increase good relations with their customers.
The debt collection services will be offered throughout countries in the Asian continent, the United Kingdom, the United States of America, Canada and India. The reach of DK Business Patron in outsourcing services has been global since a long time and their relationship with international clients has only accelerated on the part of trust and goodwill since then. This is a major reason as to why DK Business Patron is always confident in launching the unexplored segments of business in Outsourcing services because it is known that their clientele will support it given their work quality and professional relationships that have been established over the years.
Because of the goodwill that they have maintained amidst all their clients, it won’t be wrong to state that they already might be having certain clients for their global debt collection and settlement service from their previous client base itself.
After multiple major developments that DK Business Patron has launched in recent times, such launches of new units and segments is no more new to the market. In fact the other players in market are now always prepared with eyes glued to the next step that this leading organisation will take in the Outsourcing service sector.
The launch of yet another global unit is said to increase the value of this organisation manifold. The global unit will serve across a major portion of the globe and will help DK Business Patron in establishing their footprint on a vast map.
They have always stood up and maintained their image as one of the most trusted, dedicated and active Outsourcing Company in India and also on a global level recently. Their continuous dedication and skills have made them the best Outsourcing service provider in India.
With an image this strong and a client base and employ base this vast, an organisation like DK Business Patron has all the promising aspects that project it as a global leader in Outsourcing services in the coming times. They have successfully attained the position of one of the best Outsourcing service providers in India offering a vast range of services for businesses of almost all kinds across all industries and the organisation has now ventured forward to attain a similar platform on a global level.
The market now sits with glued eyes upon the next step that DK Business Patron will take for development and launches that will take it ahead in the Outsourcing service sector. Its competitors will have to be heavily prepared and equipped to fight this strong an organisation with skills and resources to lead the market in all fronts.
#global debt collection and settlement service #debt collection and settlement service #global debt collection and settlement #outsourced debt collection and settlement #outsourced debt collection services #outsourced debt settlement
1621844791
The SafeMoon Token Clone Development is the new trendsetter in the digital world that brought significant changes to benefit the growth of investors’ business in a short period. The SafeMoon token clone is the most widely discussed topic among global users for its value soaring high in the marketplace. The SafeMoon token development is a combination of RFI tokenomics and the auto-liquidity generating process. The SafeMoon token is a replica of decentralized finance (DeFi) tokens that are highly scalable and implemented with tamper-proof security.
The SafeMoon tokens execute efficient functionalities like RFI Static Rewards, Automated Liquidity Provisions, and Automatic Token Burns. The SafeMoon token is considered the most advanced stable coin in the crypto market. It gained global audience attention for managing the stability of asset value without any fluctuations in the marketplace. The SafeMoon token clone is completely decentralized that eliminates the need for intermediaries and benefits the users with less transaction fee and wait time to overtake the traditional banking process.
The SafeMoon Token Clone Development is a promising future for upcoming investors and startups to increase their business revenue in less time. The SafeMoon token clone has great demand in the real world among millions of users for its value in the market. Investors can contact leading Infinite Block Tech to gain proper assistance in developing a world-class SafeMoon token clone that increases the business growth in less time.
#safemoon token #safemoon token clone #safemoon token clone development #defi token