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README.md
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README.md
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wordfreq is a Python library for looking up the frequencies of words in many
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wordfreq is a Python library for looking up the frequencies of words in many
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languages, based on many sources of data.
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languages, based on many sources of data.
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The word frequencies are a snapshot of language usage through about 2021. I may
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continue to make packaging updates, but the data is unlikely to be updated again.
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The world where I had a reasonable way to collect reliable word frequencies is
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not the world we live in now. See [SUNSET.md](./SUNSET.md) for more information.
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Author: Robyn Speer
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Author: Robyn Speer
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## Installation
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## Installation
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Policy. This software gives statistics about words that are commonly used on
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Policy. This software gives statistics about words that are commonly used on
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Twitter; it does not display or republish any Twitter content.
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Twitter; it does not display or republish any Twitter content.
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## Can I convert wordfreq to a more convenient form for my purposes, like a CSV file?
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No. The CSV format does not have any space for attribution or license
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information, and therefore does not follow the CC-By-SA license. Even if you
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tried to include the proper attribution in a header or in another file, someone
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would likely just strip it out.
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wordfreq isn't particularly separable from its code, anyway. It depends on its
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normalization and word segmentation process, which is implemented in Python
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code, to give appropriate results.
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A reasonable way to transform wordfreq would be to port the library to another
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programming language, with all credits included and packaged in the usual way
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for that language.
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## Citing wordfreq
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## Citing wordfreq
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If you use wordfreq in your research, please cite it! We publish the code
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If you use wordfreq in your research, please cite it! We publish the code
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SUNSET.md
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# Why wordfreq will not be updated
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The wordfreq data is a snapshot of language that could be found in various
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online sources up through 2021. There are several reasons why it will not be
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updated anymore.
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## Generative AI has polluted the data
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I don't think anyone has reliable information about post-2021 language usage by
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humans.
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The open Web (via OSCAR) was one of wordfreq's data sources. Now the Web at
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large is full of slop generated by large language models, written by no one to
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communicate nothing. Including this slop in the data skews the word
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frequencies.
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Sure, there was spam in the wordfreq data sources, but it was manageable and
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often identifiable. Large language models generate text that masquerades as
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real language with intention behind it, even though there is none, and their
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output crops up everywhere.
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As one example, [Philip Shapira
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reports](https://pshapira.net/2024/03/31/delving-into-delve/) that ChatGPT
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(OpenAI's popular brand of language model circa 2024) is obsessed with the word
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"delve" in a way that people never have been, and caused its overall frequency
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to increase by an order of magnitude.
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## Information that used to be free became expensive
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wordfreq is not just concerned with formal printed words. It collected more
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conversational language usage from two sources in particular: Twitter and
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Reddit.
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The Twitter data was always built on sand. Even when Twitter allowed free
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access to a portion of their "firehose", the terms of use did not allow me to
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distribute that data outside of the company where I collected it (Luminoso).
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wordfreq has the frequencies that were built with that data as input, but the
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collected data didn't belong to me and I don't have it anymore.
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Now Twitter is gone anyway, its public APIs have shut down, and the site has
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been replaced with an oligarch's plaything, a spam-infested right-wing cesspool
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called X. Even if X made its raw data feed available (which it doesn't), there
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would be no valuable information to be found there.
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Reddit also stopped providing public data archives, and now they sell their
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archives at a price that only OpenAI will pay.
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And given what's happening to the field, I don't blame them.
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## I don't want to be part of this scene anymore
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wordfreq used to be at the intersection of my interests. I was doing corpus
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linguistics in a way that could also benefit natural language processing tools.
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The field I know as "natural language processing" is hard to find these days.
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It's all being devoured by generative AI. Other techniques still exist but
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generative AI sucks up all the air in the room and gets all the money. It's
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rare to see NLP research that doesn't have a dependency on closed data
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controlled by OpenAI and Google, two companies that I already despise.
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I don't want to work on anything that could be confused with generative AI,
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or that could benefit generative AI.
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OpenAI and Google can collect their own damn data. I hope they have to pay a
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very high price for it, and I hope they're constantly cursing the mess that
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they made themselves.
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— Robyn Speer
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