README.md

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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Untangle Solving problems with fuzzy constraints Untangle Solving problems
    with fuzzy constraints Szymon Kaliski Marcel Goethals Mike Kluev January 2023
    Have you ever needed to find a time for all your friends to meet for dinner or
    to create a seating plan for wedding guests These are examples of problems that
    require navigating a set of overlapping constraints you are only available on
    every other Tuesday or Thursday Chris will not show up if Ash is not there but
    Ash is in town only the last week of November and you really wanted to catch up
    with Chris We often work out problems like this with a pencil and paper experimenting
    until we find a solution but it feels like computers should be the perfect tool
    to help us In fact there are programming tools called theorem provers which are
    designed to solve exactly this class of problems They excel at helping experts
    work through fullyspecified problems with a clear solution Unfortunately we rarely
    have a formal understanding of our problems We come to understand them interactively
    by trying to find a solution In fact we often just need a solution that is good
    enough instead of one proven to be optimal We set out to experiment with interactive
    computerassistance for this type of thinking Untangle is a research prototype
    that helps you think through everyday constraint problems using your tablet stylus
    With Untangle you leave handdrawn marks on a page sketch out the representation
    of your problem introduce constraints graphically and browse through sets of possible
    solutions This work was presented as a part of our Programmable Ink talk at Strange
    Loop 2022 We welcome your feedback inkandswitch or helloinkandswitchcom Contents
    Thinking fuzziness and constraints Design principles Inspirations Logic programming
    Pattern matching Graphical production systems Realworld applications of logic
    programming Untangle Assignment problem Frequency assignment bids Coffee shop
    schedule Generative art Recursive rewrites Findings A smooth ramp from concrete
    to abstract helps form intuitions about the system Informal solving is most useful
    for certain kinds of problems Many problem representations look like tables Handdrawn
    input is well aligned with exploratory problemsolving Fuzziness and live feedback
    contribute to a conversational feel Shortcomings The system lacks clear semantics
    Turning ink into symbols is an unnecessary technical crutch Some parts of the
    system lack visibility Responses from the computer are underdesigned Small canvas
    artificially constrains the problem representation There is no way to explore
    the solution space Conclusions Thinking fuzziness and constraints Computers can
    be great at solving logic problems like the ones mentioned above as long as we
    can describe them in a formally correct and detailed way Special programming languages
    and techniques—theorem provers—exist and can calculate solutions for huge datasets
    These languages are most often used by mathematicians to help with proving formal
    theorems or by domain experts to aid in modeling large scale industrial production
    systems A Constraint satisfaction problem can be solved using logic programming
    techniques such as Satisfiability Modulo Theories SMT or Answer Set Programming
    ASP These programming languages are useful after we have encoded a problem in
    machinereadable form but first we must do the harder part fully understand the
    problem For this we often reach for pen and paper which allows us to think fuzzily
    and omit various levels of detail when problemsolving We can quickly sketch out
    the representation of the problem without worrying about absolute correctness
    For a sampling of realworld pen paper constraint problem representations have
    a look at How People Visually Represent Discrete Constraint Problems by Xu Zhu
    et al This project explores what it might look like if computers could support
    this style of earlystage thinking Untangle is specifically not a tool for solving
    artificial logic puzzles nor is it a tool for creating formal specifications for
    industrial systems Instead we are interested in a tool that can help us think
    through illdefined problems understand compromises and learn about what kind of
    questions to ask Untangle is a continuation of the threads highlighted in the
    thinking modeling and computers section of the Crosscut essay Mainly we want a
    tool in which we can sketch a dynamic representation of the problem at hand and
    have a conversation with it Design principles Keep focus on the problem not the
    implementationWe want a tool in which your focus remains on the problem at hand
    as much as possible rather than thinking about the correct way to encode it in
    a machinereadable way No errors undefined values or unknown parameters to fill
    inThe tool should never block freeze or become unresponsive even if the user creates
    invalid states such as errors or incomplete input A wrong answer is better than
    no answer Everything is visibleBoth the domain model and the constraints should
    always be visible and interactive Conversation with the material is be encouragedWe
    want an iterative approach to problem solving—one where observing leads to thinking
    which leads to acting which leads back to observing You should be able to intuit
    connections between various rules and constraints by wiggling them and seeing
    other things wobble We also adopted most of the design principles from Crosscut
    A tablet stylus can become dynamic pen paper The content of what you are working
    on is the most important thing You should not have to use an onscreen keyboard
    for programming This is a personal thinking space Untangle shares a lot of context
    with Crosscut a research project in which we explored an approach to building
    dynamic models by direct manipulation There is one important design difference
    from Crosscut using handdrawn strokes instead of vector graphics We believe there
    is something special about leaving distinctively human marks on the page with
    a stylus so we want to go back to handdrawn marks like with Inkbase but with an
    entirely different computational model Inspirations Logic programming Logic programming
    is an important paradigm in computer science It is based on formal logic and allows
    programmers to describe problems using declarative statements “every human is
    mortal” and ask questions based on these statements “X is human is X mortal” Some
    notable logic programming languages include Prolog one of the first logic programming
    languages widely used for theorem proving term rewriting automated planning Z3
    Theorem Prover a satisfiability modulo theories SMT solver that is targeted at
    software verification and program analysis Untangle uses Z3 as a library for solving
    Alloy Analyzer Alloy is a language for describing and exploring software models
    It has been used in a wide range of applications from finding holes in security
    mechanisms to designing telephone switching networks Of particular interest is
    Alloy’s IDE that visualizes a possible structure based on the constraints provided
    by the user Alloy Analyzer’s interactive solver visualization Pattern matching
    Untangle relies heavily on spatial queries—finding symbols on the page by their
    spatial relation to other known symbols—which were inspired by Regex a domainspecific
    language for describing search patterns in text Qualitative Spatial Reasoning
    a calculus which allows a machine to represent and reason about spatial entities
    without resorting to traditional quantitative techniques QSR is often used in
    GIS Geographic Information Systems for querying geographical data For more information
    on QSR check out A survey of qualitative spatial representations by Chen et al
    Graphical production systems Work on Untangle was also inspired by graphical production
    systems which use shape matching rules and graphical rewrites to describe computations
    Publications that guided our work Shape Grammars and the Generative Specification
    of Painting and Sculpture a seminal paper by Stiny and Gips introducing shape
    grammars New graphical reasoning models for understanding graphical interfaces
    a paper from Furnas introducing BICPICT a pixelrewriting graphical reasoning system
    Wave Function Collapse an approach for generating tile maps from a single example
    which influenced our thinking on using superposition as a mental model for working
    with multiple possible values Wave Function Collapse algorithm visualzation Figure
    by Maxim Gumin Realworld applications of logic programming Finally we were guided
    by various examples of using theorem provers for working through everyday problems
    How people visually represent discrete constraint problems Using linear programming
    GLPK for scheduling problems Tax planning with Z3 Theorem Prover A shared pinboard
    becomes a collaborative modeling tool to plan a dinner party Untangle We are now
    going to introduce Untangle a tool for working out realworld logic problems Assignment
    problem To explore the basic concepts of the tool we are going to look at an assignment
    problem imagine you are teaching a class it is the end of the semester and each
    student needs to submit a short paper about some topic You want students to grade
    each other so you have less work to do Let us see how Untangle can help us solve
    this problem This reallife problem comes from Joshua Horowitz thank you Josh In
    its most basic form Untangle allows you to automatically assign symbols to other
    symbols For example you can put Bob into a box by drawing an arrow between the
    text Bob and the box—Bob will now appear in pink inside the box A symbol is anything
    that appears on the canvas Symbols can be a single stroke or collections of strokes
    Assigning Bob to a box We can also assign multiple symbols to a box Untangle will
    now show that there are two possible assignments by showing two dots at the bottom
    of the screen We can scroll through the different solutions Bob and Eve Assigning
    multiple students to the same box There are six students in our class so we will
    write all of their names Instead of drawing six arrows we can use spatial queries
    to find elements on the canvas Using a spatial query to grab all of the student
    names Whenever we make a selection of symbols on the canvas a popup appears This
    popup suggests different spatial queries we can use to match our selection In
    this case it suggests that we could look for a vertical column of symbols starting
    with Bob Representation of a spatial query capturing a column of symbols starting
    with Bob We can place this query onto the canvas and use it as a shortcut to refer
    to all the symbols that match it The matching symbols are highlighted in the same
    color for additional visual feedback Untangle only ever places one symbol into
    each box Whenever there are multiple ways to assign symbols Untangle will generate
    multiple solutions but display only one The dots at the bottom of the screen indicate
    that there are alternative possible assignments We can get a sense of the different
    solutions by scrolling through them Solution switcher We want to assign a student
    to every other student so let us create an empty box next to every name Just like
    with the spatial query for a list of names we can create a query that simply looks
    for all boxes This generates all possible ways to assign one student to another
    one Using the all boxes query to assign every student to another student Currently
    students are sometimes assigned to grade themselves While students will surely
    be happy to do so as a teacher I would rather avoid this situation Let us add
    a rule that prevents students from grading themselves To do this we will add a
    third spatial query This query matches any box that has something to the left
    of it The question mark acts as a wildcard matching any symbol Using wildcard
    to capture something next to a box and make that something not equal to the contents
    of the box Finally we can draw an inequality constraint arrow This expresses that
    whatever ends up in the box cannot be the same as whatever symbol is on its left
    Bob can no longer grade Bob Inequality constraint Let us say we omitted a student
    when writing the original list of names Because we are using spatial queries to
    describe the column of student names we can easily extend the list Extending the
    student list with a new name The results update reactively It turns out that Bob
    and Claire have written papers about similar topics So it would be great if they
    grade each other We can ensure this by simply putting their names into the corresponding
    boxes and the system will adapt accordingly Forcing the assignment of Bob to Claire
    Again the system reacts with new results Frequency assignment bids To show a few
    more interesting properties of Untangle let us look at a different example—running
    frequency spectrum assignment bids The basic idea is that a specific radio frequency
    spectrum say 5G is divided into smaller parts and sold to various operators This
    reallife problem comes from William Taysom thank you William Let us start by modeling
    this problem There are three telephone companies telcos that are bidding on the
    frequency spectrum from 34 GHz to 42 GHz The spectrum is split into eight bands
    and the telcos can bid on individual bands Initial model of the spectrum assignment
    problem Just as in the student grading example we could use spatial queries to
    assign telcos randomly to bands in the spectrum Assigning operators to random
    bands of the available spectrum But this is not really how an auction works Instead
    telcos bid on a specific number of bands that they want to obtain For example
    Verizon might place a bid for four bands We can model this using a count modifier
    which limits the number of times a certain rule applies Using a count modifier
    to limit the assignment to four times Let us rearrange the canvas a bit and add
    some imaginary bids for each of the companies Modeling bids from all of the operators
    Some bands can be more valuable for example the middle bands often have less interference
    so a company may bit not just on a specific number of bands but also specific
    placement inside the spectrum To model this we can either draw an arrow directly
    to the specified band or simply drag a symbol in place We can guide the solver
    into a direction that we care about and it will respond immediately Constraining
    the solutionspace further by modeling bids on specific bands in the available
    spectrum The number of bids might not equal the number of available slots—an overconstrained
    system Using most solvers the result would be an error message that the constraints
    are unsatisfiable Instead of showing an error Untangle will attempt to generate
    a partially correct solution by ignoring some of the rules Arrows will turn red
    indicating that for the currently shown solution this rule is ignored Relaxing
    the overconstrained problem representation by ignoring some of the rules When
    having a conversation with the material hearing “no but what if…” is more encouraging
    than hearing just “no” The machine does not brake or scold at you for making a
    mistake but instead shows compromises and possible directions which in turn helps
    generate new ideas Finally companies sometimes bid specifically on a set of consecutive
    bands—rather than just bidding on four bands they want four bands in a row We
    can model this using a combination of spatial queries and counts Creating an assignment
    rule of four consecutive bands using a combination of spatial queries and count
    modifiers Coffee shop schedule In the following example we will show how to create
    a work schedule for a coffee shop An easy solution is to assign employees to random
    days but to make a good schedule everyone is happy with we need to consider employee
    availability To solve this problem we need to two dimensions First we list availability
    of the employees in the topleft corner For each day that an employee is available
    we draw an X For example Jim is available on Monday Wednesday and Friday In the
    bottom right we draw a simple empty schedule for the week Modeling the baristas
    availability Untangle has a special lookup spatial query that allows us to look
    up information in tablelike layouts In this case it matches columnrow pairs together
    whenever it finds an X Using the lookup spatial query on the availability table
    You can think of this as finding all the available people for each day Untangle
    will only generate solutions where the day and name are matched in the availability
    table Visualization of running the lookup spatial query We then create a spatial
    query to find all of the empty slots in the schedule using a something on left
    box on right query Spatial query using a wildcard symbol and a box to the right
    of it We only want to assign employees to days they are actually available To
    do this we bind the wildcard matches on days of the week We then use the names
    to fill in the boxes in the schedules Untangle will show us all possible schedules
    based employee availability Resulting coffee shop work schedules based on employee
    availability The proposed schedules often have people working two days in a row—something
    we might like to avoid We can set up a simple constraint to make sure no two boxes
    in a row hold the same value Constraining consecutive not to hold the same value
    It is worth noting that in this example technically all the information needed
    to solve the problem is already on the canvas However coming up with a valid solution
    still requires serious “System 2” level thinking shuffling around symbols in your
    head It feels great to have the system do this part for you System 1 System 2
    is a dichotomy introduced by Daniel Kahneman in Thinking Fast and Slow System
    1 describes fast intuitive loweffort thinking while System 2 is effortful and
    slow Generative art So far all the examples we looked at were about constraining
    the solution space—progressively going towards a small set of satisfying solutions
    This is only a one part of the problemsolving process which has two distinctive
    phases that feedback into each other—expanding and collapsing Expand and discover
    different possibilities then narrow scope and focus We can use Untangle’s primitives
    to force us to expand the possible solution space instead Let us illustrate this
    by recreating one of the most famous computer art pieces 10 PRINT 10 PRINT is
    a oneline Commodore 64 BASIC program that generates a visual pattern on the screen
    We can make a couple of boxes two symbols and fill all boxes Using spatial queries
    to fill all of the boxes with permutations of diagonal lines This already starts
    to look interesting but we can get to more compelling results by adding additional
    constraints Forcing more interesting results by applying inequality constraints
    And of course we can introduce additional symbols and keep exploring the solution
    space 10 PRINT variation using three symbols and two inequality constraints Recursive
    rewrites We can apply the rewrites recursively by flattening the pink results
    back onto the canvas and turning them into black ink This will in turn update
    the spatial queries and generate a new set of results New graphical reasoning
    models for understanding graphical interfaces is a seminal paper on using graphical
    rewrite rules for computation Progressively flattening solver results back onto
    the canvas to create basic 1D cellular automata Recursive rewrites can be used
    in a lot of interesting ways As an example below is a recreation of a logic gates
    demo from Inkbase We start by creating the symbols And the rules they follow Which
    shows us immediate feedback on the canvas We can then set up some logic gate networks
    and propagate the values through them by writing them onto the canvas to get to
    the final result A process of propagating values through two logic gate networks
    Findings We have used Untangle to solve the problems outlined above as we were
    building the prototype to test our assumptions and intuitions We also conducted
    several informal interviews with potential users with background in mathematics
    and logic programming Here are some reflections from that process A smooth ramp
    from concrete to abstract helps form intuitions about the system Symbols queries
    and arrows build up on each other Arrows are a reified way of moving a symbol
    into a box manually Spatial queries are a reified way of selecting things manually
    Combining the two is a reified way of drawing multiple arrows between multiple
    symbols “Reification” means giving a concrete representation to an abstract process
    In this sense each primitive is simply a way of expressing in more general terms
    what you could already do in an earlier step Gradually climbing this ladder of
    abstraction helps build intuition about how the system behaves Informal solving
    is most useful for certain kinds of problems There seem to be two dimensions of
    complexity for a given problem a problem can have relatively trivial constraints
    but many elements that need to be solved or can have a small number of elements
    but constraints that are difficult to satisfy There is a sweet spot where Untangle
    seems most useful The dataset must be small enough to be manually drawn but with
    constraints too complex to easily solve in your head If the dataset is large it
    takes too much effort to write it all down and the tablet screensize limits what
    can fit on the canvas If the set of constraints is simple enough to keep in your
    working memory you can often just solve the problem as you create its representation
    without additional help from the computer We found that the even a small number
    of overlapping constraints forces us to switch to effortful “System 2” thinking
    and this is where Untangle shines helping us think through a problem in an informal
    way Many problem representations look like tables The way we naturally represent
    assignment problems tends to drift toward using tables Examples of tablelike structures
    invented adhoc to represent specific problems Even though a structured approach—like
    a builtin table tool—might seem more appropriate to modeling these kinds of problems
    freeform input feels important The exact shape of the problem representation might
    not be clear initially and sketchiness facilitates finding it It feels good to
    build up to a table rather than being prematurely forced into one Another possibility
    is that the provided spatial queries column with symbol at top row with symbol
    at left table lookup etc encourage drawing problems in gridlike structures A rich
    area for future work would be adding more ways to query the canvas which could
    lead to more diverse representations Handdrawn input is well aligned with exploratory
    problemsolving One of our motivations for handdrawn input was to enable drawing
    symbols and elements that mapped closely to a particular problem domain For example
    you could draw chairs and tables for a seating arrangement In practice we rarely
    drew domain objects instead favoring symbols like names logos or even dashes and
    dots However handdrawn input still felt much more aesthetically fitting than the
    vector version we tried early on Early iteration of Untangle using vector graphics
    and artificial symbols At the lab we believe that the fidelity of the tool you
    use should be proportional to the maturity of the idea you are working on Being
    forced into crisp vector shapes for exploratory problems creates a cognitive dissonance
    between the fuzzy nascent problem in our head and the precise symbols on the canvas
    Fuzziness and live feedback contribute to a conversational feel Untangle was designed
    to get the user to a result as fast as possible Simply drawing a single arrow
    can generate multiple results To find a satisfying solution it is often not even
    necessary to add additional constraints Instead you can just scrub through proposed
    solutions to find one that makes sense If you find a satisfying solution you can
    simply stop working even if the results are underspecified In a similar vein if
    the solver cannot satisfy your constraints rather than showing an error message
    the system will attempt to ignore some constraints relaxing the problem statement
    to generate a technically incorrect solution The interface will highlight the
    arrows that were ignored to generate each solution In realworld contexts we often
    are not trying to find the globally optimal solution but rather just any reasonable
    one Spatial queries are also fuzzy We do not look for something “exactly 137px
    to the left” but “roughly to the left” This plays well with handdrawn aesthetics
    as you never create perfect sketches Additionally spatial queries highlight their
    matches directly on the canvas This has two advantages first it helps explain
    what the query is doing—even if you have no idea of the underlying formalism you
    can experimentally find out what is happening Secondly it makes it transparent
    when the imperfect matching algorithm does not work as expected You can always
    just wiggle your drawing a bit to get the system to recognize it Finally arrows
    point to and inside of queries rather than of connecting to ports This has a specific
    informal feel which meshes well with the fuzziness of other parts of the system
    and is distinctively different from the feel of things snapping into each other
    Shortcomings The system lacks clear semantics Untangle’s “language” is unspecified
    and not very composable The set of provided spatial queries is adhoc We created
    new queries for problems we were solving in the examples instead of building them
    up from first principles As a result we can solve the examples nicely queries
    are not composable or abstractable—you cannot combine to the left and to the top
    to create the lookup query used in the coffee shop schedule nor can you push the
    results from one query into another one to create reusable functionalities Additionally
    creating these queries purely by example can be quite tedious—especially when
    it comes to selecting wildcard configurations It seems clear it would be better
    to create a match by example then interactively refine it through direct manipulation
    One exciting piece of research in this direction is described in Perceptual Grouping
    Selection Assistance for Digital Sketching by David Lindlbauer et al Most importantly
    it is hard to intuit how the primitives behave beyond basic demos For example
    there is a subtle difference between constraining elements of the solution space
    and constraining elements of the match Careful thought is required to discern
    the difference Turning ink into symbols is an unnecessary technical crutch Untangle
    is a system for assigning candidate symbols to potential targets In our experience
    these symbols often consisted of many ink strokes such as for people’s names In
    order to assign these to a target we need to recognize those strokes as being
    part of a grouping Using a magic wand to group multiple strokes into a single
    symbol In past research we found that it was very difficult to reliably and unambiguously
    group ink strokes or to recognize repetitions For this project we simply sidestepped
    the problem with a command to create a symbol out of a group of strokes This was
    convenient for a research prototype but we feel this is unfortunate technical
    ceremony Similarly when creating a drawing we encourage users to reuse copies
    of the same target symbol to hint the solver system that those targets are related
    Sometimes copying objects can be a fast and intuitive thing to do Other times—especially
    when the shapes are simple—it feels more natural just to draw them again and have
    them match automatically The history of inferring specific user intent from ink
    gestures remains an open problem after many years of related work including work
    on shape recognition for drawing programs and back to early stylusbased text input
    systems such as Palm’s Graffiti Some parts of the system lack visibility A computing
    system should never leave a user feeling uncertain about whether the intent of
    the user has been understood In Untangle assignment arrows are freeform they can
    exist with or without valid sources and targets This meshes well with the fuzzy
    aesthetic but our implementation does not provide user feedback whether the arrow
    has actually connected the items on the canvas other than the updated solutions
    For this specific example it is easy to imagine how to improve this For example
    by color coding arrows and connected spatial queries the same way we do for query
    matches However color coding query components as well as their matches creased
    a visually noisy rainbow canvas Finding the right queues and feedback for a system
    like this is a subtle task There is a fundamental tension between maintaining
    a focus on the user’s input by avoiding unnecessary UI chrome and preventing confusion
    by providing sufficient feedback Responses from the computer are underdesigned
    In systems where users and computers collaborate it helps to distinguish between
    user input and computed responses In Untangle user input is always black and we
    use pink to distinguish computed results from userdrawn strokes We refer to this
    as the user voice vs the computer voice Throughout our research we often want
    the machine to respond and draw with us but what is the “correct” way to do so
    Because Untangle limits user input to a single color rendering results in pink
    makes the distinction immediately clear but we worry this would become problematic
    in a more fullfledged system We briefly explored rendering computed results using
    a different “pen” for example a stylized marker but that felt uncanny—the user’s
    input strokes redrawn exactly but with a different aesthetic Small canvas artificially
    constrains the problem representation In Untangle the relatively small screen
    size and lack of support for canvas features such as panning or zooming limits
    the amount of data and the complexity of the problems you can represent Drawing
    everything by hand also contributes to this limitation—it simply requires too
    much effort to draw hundreds of student names or create a staff calendar for an
    entire year We are interested in how the experience of Untangle would evolve as
    we explored larger scale problems or more complicated representations For example
    one future project we would love to see is “Untangle with external data” There
    is no way to explore the solution space One omission in this work is the limited
    ability to visualize the solution space Yes we can narrow down the solution space
    by reifying a result “Sam has to review Steve’s paper” or by adding constraints
    “the wildcard cannot be the same as the contents of the box next to it” but while
    Untangle allows you to “scrub” through results it only ever shows one result at
    a time In fact the solution space is not a homogeneous list there are recurring
    patterns It would be interesting to explore visualizations that revealed clusters
    or branches of candidate solutions that share similarities or how how different
    constraints “cut off” certain areas of the solution space Conclusions With this
    project we set out to discover what a nonbureaucratic theorem prover might look
    like The traditional programming interface to a theorem prover is both strict
    and formal Untangle shows a glimpse of a computational model with fuzziness at
    its core Being able to handwave at a problem and get to results—often on the first
    browse through the solution space—feels wonderful and is a stark contrast Spatial
    queries provide a way to create structure on top of a freeform drawing Instead
    of forcing problems prematurely into tabular form you can start sketching the
    problem however feels natural You then work with the system to query that diagram
    for a satisfying solution In some cases the final result may take the form of
    more traditional tabular data but we found that building up to it from a freeform
    drawing and not being constrained by it prematurely allowed us to explore our
    ideas more naturally The combination of symbols spatial queries and arrows provides
    a nice onramp for abstracting logic Rules can be built up out of simple examples
    gradually adding assignment arrows or replacing those arrows’ concrete sources
    and targets with spatial queries We feel this conceptual buildup is very promising
    and points at a possible way of solving the repetition problem described in the
    Crosscut essay Untangle is part of our “programmable ink” track of research continuing
    from previous projects Inkbase and Crosscut We remain optimistic about systems
    in which you directly manipulate the representation of the problem at hand and
    that remain alive and reactive This combination allows you to improvise and rely
    on intuitions instead of having to switch your thinking mode to one of effortful
    logical computation We see here an exciting glimpse of conversation with a dynamic
    medium—sketching at the speed of thought and collaborating with the machine We
    welcome your feedback inkandswitch or helloinkandswitchcom Thank you to everyone
    who offered feedback and guidance throughout the project Peter van Hardenberg
    James Lindenbaum Todd Matthews Kevin Lynagh Geoffrey Litt Scott Jenson Joshua
    Horowitz Patrick Dubroy William Taysom Daniel Krasner Ivan Reese Paul Shen Max
    Schoening
- text: Harnessing the Medici Effect for More Profound Web3 Impact CARBON Copy Front
    PageFeatures ReFi Data ProjectsBuildersTokensLandscapeImpact DashboardOpportunitiesKnowledge
    Content NewslettersThe ReFi WeeklyLearnWeb3 Fundraising Guide for Nigerian NGOs
    Resources OnChain Grant DirectoryGitcoin Grants CanvasThe Regen AtlasThe Climate
    Capital StackCCN Metrics GardenX List of ReFi ProjectsAboutSubmit News OpinionHarnessing
    the Medici Effect for More Profound Web3 ImpactHow we have strayed off the path
    of originality and how to get back on itBy Trinity Morphy May 21st 2024In this
    piece Trinity Morphy takes inspiration from a recent event to look at why originality
    has been so difficult to attain in the Web3 impact space and how a phenomenon
    called the Medici Effect can get us back on trackI had the privilege of attending
    a Green Pill Nigeria Impact Tour event two weeks ago In his talk Izzy the lead
    at Green Pill Nigeria pointed out the troubling trend of imitation in the impact
    space Each cycle he said seems to be filled with recycled project ideas It has
    become customary for Project XYZ to emerge with a seemingly novel concept only
    to be quickly followed by Project ABC essentially a copycat with a new bell or
    whistleThis lack of originality especially in Web3 impact is not just a hurdle
    its a roadblock For instance other effective ways exist to utilise impact NFTs
    apart from sequestering carbon or planting new trees Yet we keep seeing new projects
    based on the same idea but with a more exciting descriptionHow can we actively
    encourage a shift from simple recycling to genuine originality The Medici Effect
    that the most innovative ideas happen when you combine concepts from different
    fields offers insight By looking beyond the impact field we can unlock the potential
    for more impactful solutionsWhy we recycle ideas and why originality is so importantpictwittercomsRtpkwgMGk—
    OwockiΞth owocki May 17 2024Reduced risk and familiarity Humans naturally gravitate
    towards familiarity Replicating a successful project offers security by minimising
    the uncertainty of venturing into something completely new Its like following
    a wellworn path instead of forging one through the wildernessAccess to funding
    Investors and funding institutions are more likely to back sectors with a proven
    track record of projects A copycat project can point to the success of the original
    and argue that it can replicate that success with its own twistBandwagon effect
    When a project becomes popular others aim to capitalise on the hype and user base
    On the surface launching a copycat project appears easier as does the marketing
    that goes with itAvoiding mistakes A successful project has already learned what
    works and what does not Emulation enables new projects to sidestep those pitfalls
    and concentrate on innovationOriginality is crucial to tackling our social and
    ecological problems It allows us to see things differently question assumptions
    and uncover new possibilities This fresh perspective can lead to new solutions
    we would not have considered otherwise It empowers us to challenge outdated or
    dysfunctional systems and propose solutions that address their shortcomings from
    the ground up Original thinking enables us to recognise the potential in underutilised
    resources and address problems at their roots not simply treat their symptomsEasier
    said than done of course We need to look to the Medici Effect for a roadmapThe
    Medici Effect and Web3 impactThe Medici Effect states that the most original and
    extraordinary ideas occur when you combine concepts from different fields disciplines
    or cultures It was discovered by Frans Johansson and shared in a book of the same
    name A majority of the ideas in this section are borrowed from the book and modified
    to fit Web3 impactSome examples of great products that have stemmed from the Medici
    Effect include Silvi reforestation Web3 M3tering Protocol renewable energy distribution
    Web3 Gitcoin fundraising Web3 and Toucan Protocol voluntary carbon markets Web3Heres
    how we can get better at using the Medici Effect to our advantageDismantle the
    barrier between Web3 and other fieldsFirst we must be willing to break the associative
    barriers that exist between Web3 and other fields How do we do this Through exposure
    to a diverse set of cultures Culture in this context extends beyond geographic
    boundaries to encompass ethnic class professional and organisational differences
    By immersing ourselves in these diverse backgrounds we can unlock a more open
    and questioning mindset and challenge our assumptionsWe also need to embrace broad
    selfeducation Traditional education often compartmentalises knowledge but selfdirected
    exploration across disciplines expands what is possible Without it our thinking
    is limited and we inadvertently stifle creativity Selfeducation empowers us to
    discover unexpected connections and envision how concepts in other fields can
    combine to create groundbreaking Web3 impact solutionsCombine random conceptsIntersectional
    ideas are groundbreaking because the concepts involved are so different and the
    combinations so unusual that no one would have thought them possible Take the
    inspiration for this article as an example It happened after I came across an
    X account named Cozomo de Medici I was not thinking about Web3 impact or the Medici
    Effect That Is how random combinations work You have no control over it It just
    comes Rather than leaving luck to do the work we can continuously stimulate our
    brains to keep producing these random combinations How do we achieve thisNurturing
    our curiosity By obsessing over new ideas approaches and perspectives we enhance
    our brains ability to blend random concepts In the foreword of Exploring MycoFi
    Scott Morris pointed to Emmett Jeffs relentless fascination with mushrooms and
    mycelial networks and how it led to the creation of MycoFi a novel cryptoeconomic
    model inspired by the structure of mycelial networksInteracting with diverse groups
    of people Engage a diverse group of people and we will be presented with a diverse
    set of perspectives Interacting with such groups can spark unexpected connections
    For example a conversation between a Web3 enthusiast and a custodian of tradition
    might lead to an idea to preserve cultural heritage using blockchainThese are
    what create the conditions for the next phaseIgnite and evaluate an explosion
    of ideasThe strongest correlation between quality and quantity of ideas is in
    fact the number of ideas Do you know how many ideas you can get by simply combining
    concepts from environmental science with Web3 concepts A LOT That is why it irks
    me when I see so many projects centering on carbon credit tokenisation when there
    are so many ideas yet to be explored The question here is once these ideas start
    flooding in how do we handle themFirstly capture as many ideas as we can Keep
    track of all the ideas our brains generate and set a target to reach before evaluating
    their feasibility Secondly we need to take our time evaluating because our minds
    will quickly judge the value of an idea by comparing it to what is already known
    to work Its important that we evaluate each idea as sincerely as the next whether
    its gamifying traditional classroom learning or eradicating open defecation in
    underserved communitiesComing up with great ideas does not guarantee innovation
    however We must make those ideas happenMake intersectional ideas happenThe paradox
    of innovation at the intersection of fields is rooted in the symbiotic relationship
    between ideas and failures the more ideas we explore the higher the rate of failure
    Far from being a negative consequence failures are a vital part of the innovation
    journey We learn and grow through them so that we can do better the next time
    Not acting for fear of failure robs us of this crucial phaseDaring to try ideas
    opens us to a world of invaluable insights that help us refine our approach identify
    weaknesses and ultimately discover the path to a truly groundbreaking solution
    In my interview with Christwin of Switch Electric and M3tering Protocol he shared
    a fascinating story about the early days of his solution The first model they
    tried unexpectedly incentivised the consumer to overload the solar infrastructure
    leading to severe component wear and tear and attracting steep maintenance costs
    This was a significant failure but it led to research that brought about the idea
    of building M3tering Protocol on blockchain to track consumption and ensure transparencySince
    failures are inevitable we can allocate specific resources for testing each new
    idea and minimising the potential for catastrophic failure This allows for rapid
    iteration where we can learn from each attempt refine our approach and move on
    to the next experiment with valuable insights Do Not forget to document everything
    along the way and share the results within the team This knowledge base becomes
    a valuable resource for future projects because it prevents the same mistakes
    and accelerates future breakthroughs We saw this in the transition from Gitcoin
    10 and Gitcoin 20We also need to stay motivated along the way because the path
    to ingenuity is rarely linear There will be setbacks and failures that make us
    question our actions Staying motivated requires keeping the longterm vision in
    mind and focusing on the positive impact our work can bring to the world We need
    to celebrate all wins big and small whether its successfully completing a pilot
    project achieving a user engagement milestone or receiving positive feedback from
    a target community This reinforces our belief and helps us pushing forward despite
    the inevitable setbacksConclusionThe Web3 impact space has potential to change
    the way we tackle the worlds most pressing challenges Were not getting there however
    if we keep recycling the same old ideas Originality is the key if we are to truly
    unleash Web3 impact We must look beyond our own siloes break down barriers and
    ignite curiosity across a variety of fields to spark the kind of breakthrough
    ideas that will move the needleAnd when those ideas do come we must not shy away
    from failure and its valuable lessons Nature exemplifies this beautifully a butterflys
    struggle to escape its chrysalis is anything but pleasant Still this phase is
    necessary for the butterfly to strengthen its muscles expand its wings and ultimately
    fly Embrace the journey celebrate even the smallest wins and surround yourself
    with a supportive network Together we can embrace the power of the Medici Effect
    to create a better future for ourselves and the planetThis article represents
    the opinion of the authors and does not necessarily reflect the editorial stance
    of CARBON CopyCopyright © 2025 CARBON CopyToken data provided by CoinGeckoFront
    PageFeaturesLearnReFi ProjectsAboutSubmit News
- text: 5 DeSci projects disrupting scientific research and development — Crypto Altruism
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    5 DeSci projects disrupting scientific research and development Project HighlightsScienceDAOs
    Mar 30 Written By Drew Simon 2021 was the year of decentralization and this momentum
    has only increased into 2022 Not only have we seen incredible growth in the decentralized
    finance DeFi space but we have also seen the emergence of social impact DAOs decentralized
    media platforms decentralized VC funds and more recently the emergence of a new
    field – Decentralized Science or DeSci In short “the decentralized science DeSci
    movement aims to harness new technologies such as blockchain and ‘Web3’ to address
    some important research pain points silos and bottlenecks” Whereas scientific
    research has long been viewed as overly bureaucratic and disjointed the DeSci
    movement aims to improve this by using blockchain to offer greater transparency
    and to take on the “profit hungry intermediaries” such as scientific journals
    that have dominated the traditional research spaceFor some resources on DeSci
    I recommend you check out the following articlesDeSci an opportunity to decentralize
    scientific research and publicationA Guide to DeSci the Latest Web3 MovementCall
    to join the decentralized science movementFor this blog post we will be highlighting
    5 DeSci projects that are leading the way and positively disrupting scientific
    research and development1 VitaDAOOne of the best examples of DeSci in action is
    VitaDAO a Decentralized Autonomous Organization DAO focused on funding longevity
    research in “an open and democratic manner” Specifically they are focused on the
    decentralization of drug development focused on the extension of human life and
    healthspan They fund earlystage research with the goal of turning the research
    into biotech companiesVitaDAO is government by holders of VITA tokens which can
    either be purchased or earned through contributions of work or intellectual property
    With over 4000 members and 9M in funding raised to support scientific research
    VitaDAO has proven that the DeSci movement is no laughing matterCheck out some
    of their featured projects here2 SCINETThe SCINET platform which is built on blockchain
    enables retail and institutional investors to securely invest in scientific research
    and technology directly In addition to funding promising scientific research they
    also offer a “blockchainpowered” cloud laboratory for researchers a rigorous decentralized
    peer review process and enable researches to document their IP on an immutable
    blockchain3 AntidoteDAOAntidoteDAO is a decentralized community focused on funding
    cancer research and other cancer initiatives Their ecosystem includes a governance
    token and NFT collection which both enable individuals to vote on where to allocate
    funds In addition to providing funding to charities supporting cancer research
    and cancer patients a core focus of the DAO is on providing 100K seed fund grants
    to cancer research teams Research projects are first reviewed by the DAO’s Medical
    Advisory team and then put to the community for a vote Fun fact we have an upcoming
    podcast episode with AntidoteDAO that when available will be published HERE Crypto
    Altruism uses Ledger to keep its assets safeYou’ve probably heard the phrase “not
    your keys not your coins” By choosing a hard wallet like the Nano S Plus to store
    your crypto you can rest assured that the keys and the crypto are truly yoursGet
    your Ledger Nano S Plus now by clicking HERE or on the image below 4 LabDAOLabDAO
    is an emerging organization which is dedicated to operating a communityrun network
    of wet and dry labs with the goal of advancing scientific research and development
    A wet lab is one focused on analysing drugs chemicals and other biological matter
    whereas a dry lab is one focused on applied or computational mathematical analysis
    LabDAO is a relatively new project that is still in its infancy but has a promising
    mission and strong community of support around it 5 MoleculeMolecule is a decentralized
    scientific research funding platform that operates as a marketplace for researchers
    seeking out funding and individuals looking to invest in scientific research projects
    They are “connecting leading researchers to funding by turning intellectual property
    and its development into a liquid and easily investable asset”Researchers can
    list their research projects on the Molecule marketplace as a means to engage
    with potential investors and to secure funding for their project Molecule currently
    has over 250 research projects listed on their marketplace over 4500 DAO community
    members and 3 “Bio DAOs” with over 10M in funding in their network According to
    Molecule “The future of life science research will be driven by open liquid markets
    for intellectual property powered by web3 technology”We cover more amazing DeSci
    projects in our more recent postTen more DeSci projects disrupting scientific
    research development and knowledge sharing Buy me a coffee Send a tip in ETH cryptoaltruismethLike
    what you are reading Consider contributing to Crypto Altruism so we can continue
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    and services DeScidecentralizationscienceblockchainlists Drew Simon Previous Previous
    Crypto Altruism Podcast Episode 39 AntidoteDAO Decentralized funding of cancer
    research and charitable initiatives Next Next Crypto Altruism Podcast Episode
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- text: A Short History of BiDirectional Links Home The Garden Essays Notes Patterns
    Smidgeons Talks Podcasts Library Antilibrary Now About The Garden Essays Notes
    Patterns Smidgeons Talks Podcasts Library Now About Essays evergreen A Short History
    of BiDirectional Links Seventy years ago we dreamed up links that would allow
    us to create twoway contextual conversations Why do not we use them on the web
    Design Digital Gardening The Web Web Development Planted almost 5 years ago Last
    tended about 4 years ago Table of Contents Bidirectional links are not new Bidirection
    Linking in Personal Digital Gardens Building Your Own BiDirectionals BiDirectional
    Linking with WebMentions Table of Contents Bidirectional links are not new Bidirection
    Linking in Personal Digital Gardens Building Your Own BiDirectionals BiDirectional
    Linking with WebMentions With the recent rise of Roam Research the idea of bidirectional
    linking is having a bit of a moment We are all very used to the monodirectional
    link the World Wide Web is built around They act as oneway pointers we follow
    in a linear sequence While we can link to any site the destination page has not
    a clue we have done so We set up all these singledirection paths trying to signal
    relevance and context only to have the other side completely ignore our efforts
    Our monolinks are trying to establish relationships in vain We are starting to
    look around our monolinked environment and wonder why it is so hard to surface
    relevant contextual relationships Manually interlinking content takes an awful
    lot of human curation and effort Efforts we should probably slog off onto our
    systems Enter the bidirectional link A bidirectional link has social awareness
    it knows about other pages or ‘nodes’ that point to it and can make them visible
    to people This means we get a twoway conversation flowing between our web locations
    Bidirectional links are not new The idea of the bidirectional link goes back to
    194580ya when Vannevar Bush dreamed up the Memex machine Vannevar outlined this
    hypothetical gadget in an essay in The Atlantic called As We May Think He wanted
    a system capable of “associative indexing… whereby any item may be caused at will
    to select immediately and automatically another… so that numerous items have been
    thus joined together to form a trail” This essay turned out to be a foundational
    document for the ideologies that directly led to both the internet and the Web
    Yes those are two entirely separate pieces of technology Vannevar was one of the
    key movers and shakers rallying folks to help build the original internet infrastructure
    He corraled folks at MIT the US Department of Defence the National Science Foundation
    and various research labs like the Standford Lincoln Lab Bell Labs the RAND Corporation
    and Xerox PARC to get involved Walter Isaacson The Innovators How a Group of Hackers
    Geniuses and Geeks Created the Digital Revolution London Simon Schuster 201510ya
    Suffice to say the guy was driven by a belief that enabling people to connect
    information and share knowledge would expand the scope of human understanding
    The Memex was one idea of how that might manifest in material form Vannevar even
    created a small informative diagram of this deskbound vision Marketing chops 101
    Vannevar’s evocative description of the Memex is especially impressive given that
    digital computers had only come into existence 5 years earlier Most were still
    the domain of large military operations like Bletchley Park and were seen as inconveniently
    large calculators Implementing a wildly interactive computational personal knowledge
    base was not much of an option So the idea went into hiberation and did not resurface
    until the idea of personal computing began blooming in the sixties and seventies
    Ted Nelson an unlikely film director and sociologist stumbled into a series of
    computing lectures and began to imagine how graphical interfaces might reinvent
    the way we write and connect ideas He took inspiration directly from Vannevar’s
    essay and in 196560ya when he coined the term hypertext to describe his vision
    for a sprawling network of interlinking information Nelson planned to implement
    these hypertextual dreams in his perpetuallyimminent Project Xanadu If you have
    some time this is quite the internet history rabbit hole to run down Ted Nelson
    is on another level The Xanadu project was a hypertext system that imagined that
    every sentence block and page would be part of a vast bidirectionally linked network
    A design mockup of how Project Xanadu might visually connect pieces of text across
    multiple documents You would be able to trace information back to its origin the
    way current web links do But you would also be able to see who had referenced
    remixed and expanded off that original The full Pattern Language of Project Xanadu
    expands far beyond just bidirectional links to include features like Transclusions
    but we will not dive into it all here Suffice it to say Xanadu did not pan out
    Instead we got the less fancy but far more real and useable World Wide Web that
    currently does not support bidirectionals on an infrastructure level While Sir
    Tim Berner’s Lee wrote himself a note debating their pros and cons back in 199926ya
    there is an obvious design issue with letting twoway connections flow freely around
    the web If every site that linked to yours was visible on your page and you had
    no control over who could and could not link to you it is not hard to imagine
    the Trollish implications… Figuring out how we might filter moderate and set permissions
    around link visibility turned into quite the challenge The design details grew
    complex It became clear implementing the Web with simpler monodirectional links
    was the right thing to do given that its creators wanted universal adoption Lots
    of people are still mad about it Let us not venture too far down that historical
    wormhole The TLDR is technology is hard Until Xanadu ships and we are all immersed
    in the universe of multilinked versioncontrolled nodes of remixable microcontent
    that somehow solves the problems of permissions and moderation there are still
    plenty of ways we can resurrect the possibility of bidirectional links on the
    web Bidirection Linking in Personal Digital Gardens Most of the design issues
    with adding bidirectional links to the global web were related to moderation and
    permissions However adding them within the bounds of a single website with one
    author sidesteps that problem There is been a flurry of interest around bidirectionals
    among people involved in the Digital Gardening movement Much of this was originally
    sparked by Andy Matuschak’s notes Go have a good browse through them Andys linked
    notes stack on top of one another allowing you to browse to new notes while previous
    notes are still visible There is plenty to admire here It should be noted Andy
    is an experienced developer and interaction designer and these notes should not
    be taken as the standard expectation for the rest of us normal plebby internet
    citizens But the key part of this system that creates interlinked context is the
    “Links to this Note” section at the bottom of each post Anytime Andy links to
    another one of their notes on the site it will pop up as a related note at the
    bottom of the page This is the bidirectional dream It gives us a way to navigate
    through these ideas in exploratory mode rather than navigating a hierarchy of
    categories on a main index page Since it is all contained within a singleauthor
    site our SpammishTrollrisk factor is at a comfortable zero This is mildly tangential
    but I love how the topic of bidirectional links makes fully visible our “websites
    are locations” and “websites are containers” conceptual metaphors with “inside”
    and “outside” links Building Your Own BiDirectionals That is all very cool but
    how are you supposed to build bidirectionals into your own site Thankfully setting
    up your own public gardening bidirectional Memex does not involve Xanadu One fantastic
    option for nondevelopers is based around a personal wiki system called TiddlyWiki
    AnneLaure Le Cunff wrote up an easytofollow guide to getting your own up and running
    For those of us here for the hypercustomised overengineered JavaScript solution
    that would be me the Gatsbyjs community has a number of active gardening enthusiasts
    building themes and plugins I built mine using Aengus McMillin’s gatsbythemebrain
    Aengus has documented the theme well and it is not too challenging to implement
    as long as you are comfortable in JavaScript and React I also curate a list of
    tools for Digital Gardening on this Github repo BiDirectional Linking with WebMentions
    While I argued that Webwide bidirectional links are unlikely to happen at a global
    scale there is a way you can add bidirectionals to your personal website that
    picks up on references anywhere on the web WebMentions are a piece of web infrastructure
    the IndieWeb community has done a lot of work to advocate for The W3C gave the
    specification recommendation status in 20178ya The system notifies a URL whenever
    that site is mentioned elsewhere on the web You are then able to show that mention
    and its contents on your site It is essentially an optin bidirectional linking
    system Plenty of folks have written useful guides on how to add these to your
    site Here is one for any static site one for Gatsby one for Nextjs There is a
    whole list of implementation examples on the IndieWeb Wiki you can look through
    5 Backlinks A Brief History Ethos of the Digital Garden A newly revived philosophy
    for publishing personal knowledge on the web Digital Gardening for NonTechnical
    Folks How to build a digital garden without touching code Transclusion and Transcopyright
    Dreams The lost permissioning and copyright system of the Web The Pattern Language
    of Project Xanadu Project Xanadu as a pattern language rather than a failed software
    project A MetaTour of This Site A video tour through how I build the old version
    of this site Mentions around the webCarlos Sanmartín Bustosmentioned in En busca
    de enlaces bidireccionalesJune 24 2024Una de las cosas que echo en falta al haber
    pasado el blog a un sitio estático son los links bidireccionales No tanto los
    links que provengan de fuera que hoy en día van a ser mínimos como los links internos
    que permiten explorar el blog por ejemplo para avanzar hacia el futurAngsuman
    Chakrabortymentioned January 07 2023A Short History of BiDirectional Links CypherNewsmentioned
    January 04 2023 Hacker News A short history of bidirectional links 2020 hackernews
    HN Front Pagementioned January 04 2023A Short History of BiDirectional Links L
    C newsycombinatorcomitemid342447…Winson Tangmentioned January 04 2023A short history
    of bidirectional links 2020 Hacker Newsmentioned January 04 2023A short history
    of bidirectional links 2020 Patrick Durusau ⏳ White Person on White Supremacymentioned
    May 18 2022codexeditor I Have lost the tweet where I saw this mentioned earlier
    today I like the Web Mentions idea esp if we could use XPath to point to less
    than the page level Thoughts Show 3 more Want to stay up to date Subscribe via
    RSS Feed © 2025 Maggie Appleton The Garden Essays About Notes Now Patterns Podcasts
    Talks Smidgeons Colophon Library
- text: Part 1 Introduction to Responsible Technology by goodbot MediumOpen in appSign
    upSign inWriteSign upSign inPart 1 Introduction to Responsible TechnologygoodbotFollow11
    min read·Aug 3 2023ListenShareIntroductionIn late 2022 the GoodBot team — consisting
    of a group of sociallyminded professionals working in law technology and policy
    — came together to develop a snapshot of Canada’s current Responsible Technology
    landscape This is a space that to date had been heavily defined by voices from
    the United States Our goal is to understand the Canadian ecosystem’s current composition
    capacity and direction particularly how technology impacts Canadians what issues
    are of focus in research and programming and how the policy landscape is evolving
    at the provincial national and international levelsTechnology is revolutionizing
    everything from healthcare to education to law to climate science and government
    but is also associated with a wide range of risks and harms making responsible
    technology governance a critical priority for governments nonprofits and marketsNew
    visions clear policy frameworks effective implementation methods and multistakeholder
    oversight bodies are needed to navigate this landscape It also requires public
    interestfocused strategic collaboration that includeslongterm research on harmsmore
    transparency and collaborative efforts with technology companies to strengthen
    safetyeffective mechanisms to hold companies accountable when they fail to act
    in response to harminvestment in public interest technology and philosophies that
    prioritize healthy technology ecosystemsunderstanding the leverage points and
    incentive systems that contribute to these outcomes andthe development of the
    critical capacities needed to meet this momentWe know from the last two decades
    that when technology tools and platforms become deeply embedded in institutions
    like media and education before they are regulated they become harder to govern
    This reality has led to escalating and lasting harm in a number of areas The pressing
    question now is how to forge a new direction recognizing the immediate need to
    take actionThis is the introduction to a research project on Canada’s Responsible
    Technology landscape GoodBot’s goal with this research is threefoldTo understand
    the current landscape and priorities of Canada’s Responsible Technology ecosystem
    including the who what where and howTo highlight gaps and opportunities within
    this ecosystem that can help Canada develop a more robust impactful and collaborative
    approach and agenda andTo understand what role GoodBot can play in advancing Responsible
    Technology at home and around the worldWhat is Responsible TechnologyResponsible
    Technology acts as an umbrella for a range of approaches and terms that all focus
    on different issues or specific intervention points in technology and business
    life cycles It includes concepts such as ethical tech humane tech tech stewardship
    and public interest tech all of which are connected and overlap but which also
    center on different locus of influence These and other terms will be explored
    in Part 1 of our seriesResponsible Technology is a relatively new framing that
    includes a wide range of issues related to technology Some issues — like privacy
    and freedom of expression — are longstanding cornerstones of the most established
    technology nonprofits in the country while conversations on Generative AI risks
    are relatively new Yet technology and AI ethics have been around for decades with
    themes prominent among academic labs human rights defenders peacebuilders and
    in other convening spaces What has changed is the scale and pace of technology
    and the amplification of new risks and narratives surrounding technology harms
    These have created a growing awareness of the need for safetyfocused design and
    research at the outset meaningful oversight of technology companies and effective
    accountability mechanisms when companies fail to act in the public interestMany
    technology tools and businesses currently fail to meet even a vague understanding
    of Responsible Technology This is especially true for small to medium technology
    companies who focus on survival and leave proactive assessment and harm mitigation
    as an afterthought if they get any attention at all Others have social impact
    strategies that are completely detached from their business modelFew companies
    start with the goal of causing harm but unintended consequences can arise due
    to a lack of intentional consideration capacity and unanticipated and conflicting
    priorities Additionally as products scale seemingly harmless matters can lead
    to real harm as user bases grow use cases expand and incentive structures change
    The explosive emergence of generative AI has made it even more clear that left
    unaddressed these structural factors risk widening the gap between privatized
    profit and socialized riskIn spite of these growing concerns several Big Tech
    companies have chosen to lay off large segments of their Trust and Safety teams
    seeing them as cost centers that add undesirable operational complexity Even when
    companies have Trust and Safety teams in place they are often pitted against product
    teams The result is companies that increasingly seek to automate these decisions
    which frequently and disproportionately impact minority communities including
    human rights defendersSome companies have begun sharing Transparency Reports which
    is a move in the right direction However there are no agreed standards or metrics
    against which to assess companies’ commitment to social sustainability nor is
    there any external oversight These factors lead to the possibility of ‘tech washing’
    and cherrypicking data that provide the appearance of taking action but in ways
    that lack substantive effect or because new harmMoreover even companies that have
    the desire to act responsibly can lose sight of their original goals when they
    face demands for outsized returns from investors — including venture capitalists
    and private equity investors — which can place them at odds with decisions that
    are in the public interestIn this context a wide range of social harms and externalities
    have arisen — many of which are unintentional — and which includeBiased Unaccountable
    Untransparent AutomationBias in untransparent algorithms that discriminate against
    marginalized groupsDisruption of the workforce by generative AI in almost all
    professional sectorsBig Tech DominationBig Tech market domination to control value
    chains through predatory pricing terms and acquisitionsNonconsensual selling of
    personal data to and from thirdparty data brokersAddiction Mental HealthThe use
    of dark patterns to drive engagement and addiction in gaming and social mediaThe
    decline of attention spans at a population level in the last 20 yearsThe decline
    in mental health and body image especially among youth leading to self harm and
    deathHarassment Violence ExtremismIncitement to radicalization extremism and even
    genocideTrolling doxing and harassment including targeting women trans and BIPOC
    peopleBad ActorsTargeted and opportunistic disinformation and microtargeting to
    undermine democraciesScams to steal millions from people through generative AI
    or with crypto hacksTrafficking of women and girls on the dark web and in mainstream
    platformsThis is by no means a comprehensive list In response Responsible Technology
    advocates have advanced efforts in recent years to understand the wide array of
    externalities impacting different levels of society These initiatives variously
    aim to understand harms and causes increase public awareness and engagement incentivize
    governments to enact new laws and enforce existing ones and create solutions that
    lead to safer and more responsible technologyenabled environmentsAdditionally
    a new wave of government and nonprofit investigations and litigation aims to clarify
    technology companies’ responsibilities and identify leverage points to incentivize
    responsibility These efforts have had some successes but are often too incremental
    and underresourced to keep paceThe scale and complexity of issues arising from
    technology are unprecedented Canada needs a clear Responsible Technology agenda
    and sufficient investment to move toward a technological future defined by healthy
    people businesses markets societies and democraciesThe Asymmetry Sustainability
    GapA key barrier facing Responsible Technology advocates — including journalists
    academics tech nonprofits technologists tech ethics experts policymakers and citizens
    — in addressing technology harms is an existing and growing set of asymmetrical
    disadvantages when compared to the companies and sectors responsible for harmThese
    disadvantages manifest in many forms including limited access to talent data sets
    algorithms infrastructure information internal research and audits knowledge and
    resources It shows up in access to capital restrictions on how capital can be
    used and comparatively robust ethical requirements It is further exacerbated by
    companies’ anticompetition tactics to buy out underprice feature bundle and otherwise
    aggressively quash any disruptors who may be able to offer healthier alternativesAdditionally
    asymmetries show up when comparing the inputs and outcomes around harm It is —
    for example — much less resource intensive to create campaigns to disseminate
    disinformation on vaccines — than it is to undo the damage caused by this disinformation
    This reality places a societal premium on considering what effective oversight
    governance and accountability of technology look like but also raises the need
    to balance corrective actions with Freedom of Expression FoE norms The global
    nature of many technology platforms means that US norms are effectively imposed
    on Canada and other countries Yet Canada has its own unique and wellestablished
    interpretations of fundamental freedoms that should be considered and protected
    in the face of technological changeCanada’s Responsible Technology ecosystem is
    small and underresourced compared to its US counterparts and at this time there
    are few prominent ecosystemlevel organizations that are wellpositioned to guide
    a Responsible Technology community and strategy Yet to effectively influence outcomes
    Responsible Technology advocates need to work together including by establishing
    new governance innovationsA critical factor in the Canadian context is that much
    of the funding to date has focused on understanding symptoms and immediate causes
    rather than underlying structural issues and incentive systems at play Some of
    this is a product of new and emerging organizations with limited track records
    while other issues arise from inadequate and restrictive funding and opportunities
    Some organizations only work on technology matters at a project level rather than
    a mission level Such factors are important to understanding the limitations on
    Canada’s capacity and sustainability Our sixth report will explore Canada’s nonprofit
    capacity in more depthAn additional barrier is that there are no obvious market
    solutions to address many of the problems that arise from technology For example
    while technology has led to unprecedented online harassment of women people of
    color and LGBTQ communities companies offer limited solutions aimed at limiting
    harassmentThis reality is exacerbated by the fact that markets recently rewarded
    technology companies for cuts that significantly depleted their Trust Safety teams
    even when it occurred at a time of high salience around the risks posed by technology
    platformsLack of coordination is also a factor among tech companies For example
    some companies argue that if they do not employ harmful tactics such as polarizing
    content or algorithms to attract attention they will lose out to rivals who will
    These realities point to an increasing need for sectors to collaborate toward
    reducing harm and promoting public interestIndeed in areas where companies have
    invested more material resources — including to address extremism and protect
    children — even Big Tech lacks the bandwidth to address the complex range of issues
    on its own making collaboration essential Within the tech sector new multistakeholder
    initiatives such as Global Internet Forum to Counter Terrorism and the Tech Coalition
    have been launched in an effort by technology platforms — and include human rights
    advocates governments and researchers — who work collectively to reduce extremism
    and child sexual abuse material online respectively Such issues are even more
    challenging for smaller platforms and startups that lack internal resources to
    respond to emerging and unanticipated issuesUltimately while technology companies
    contribute many benefits to society they also contribute significant problems
    that neither society the markets nor they are presently able to solve They often
    lack incentives to prevent and address problems up front and even when they want
    to do the right thing investor incentives can derail their decisions There are
    limited market incentives to address challenges created by businesses and market
    competition Moreover our governance institutions operate in ways that are incompatible
    with the fastmoving pace of technology which presently tend to focus on harm to
    individuals instead of on harm to society These factors together mean that we
    are in a situation that is by definition unsustainable We need collective action
    to address these risks including through responsible resources capacities frameworks
    budgets and policiesCanada’s Policy LandscapeWhile several technologyfocused bills
    are in development Canada’s current policy landscape is far behind the advances
    of the last 20 years This is also true of many other countries that currently
    lack the policies capacities institutions and enforcement mechanisms needed to
    govern a rapidly evolving technology environment Furthermore research shows that
    even where effective policies are in place underresourced enforcement mechanisms
    such as antitrust hamper the ability of governments to enforce existing laws In
    this context the role of civil society organizations is particularly importantWhile
    the policy landscape is expected to evolve rapidly we do not know what effect
    these policies will have or how coherent they will be across jurisdictions and
    issues Canada has introduced many bills for consideration but few have passed
    and all leave much to be desired In the intervening time legal firms and the Privacy
    Commissioner have advanced litigation — with a particular focus on privacy and
    antitrust — to hold technology companies accountableCivil society organizations
    often lack consensus on addressing key issues For instance Online Harms advocates
    support stricter content moderation to tackle harassment extremism and child exploitation
    In Canada some lean towards Freedom of Expression while others recognize the need
    to address such harms but fear that wellintentioned policies could inadvertently
    suppress the voices of vulnerable communities Balancing these competing rights
    is complex Responding to emerging issues and harms that affect us all requires
    better public engagement This includes open and inclusive dialogue transparent
    consultation processes and effective accountability mechanisms to navigate these
    complexities and to help uphold and balance a range of fundamental Canadian rightsIndeed
    there are no ‘right’ answers but rather ‘different tradeoffs’ Moreover there are
    ways to escape such polarity traps which can easily become politicized resulting
    in deadlock Even among nonprofit organizations such as the ones that are seemingly
    at odds on online harms for example both largely agree that passing comprehensive
    privacy frameworks would mark an important victory and achievementYet even if
    we achieve effective regulation and enforcement addressing entrenched asymmetries
    — especially those caused by Big Tech — requires a collective agenda and roadmap
    What is clear is that our current institutions lack the capacity and resilience
    needed to address the challenge we faceAs Canadians we have an opportunity to
    draw upon the values that make us strong as we reimagine our relationship with
    technology Ideas such as Indigenous approaches to data sovereignty collaboration
    and multiculturalism have much to teach us about how to navigate these complex
    issues This moment presents an opportunity to rethink and localize the who how
    and why of technology governance This is both a daunting and an exciting challengeOpenSourcing
    GoodBot’s ResearchFor GoodBot our first step is to practice our open principles
    by sharing what we have learned We hope that this research can lay the groundwork
    for building a Canadian coalition to support its nascent and necessary Responsible
    Technology movementGetting to impact requires understanding Canada’s existing
    capacity understanding the systemic issues at play exploring moral and policy
    considerations surfacing current and emerging asymmetries of power and exploring
    how AI is upending companies and industries It also requires a collective strategy
    and targeted action focused on moving toward responsibilityThese documents are
    intended to act as a primer for anyone seeking to make an impact in addressing
    critical priorities facing Canada and the world While our early research may initially
    be of more value to nonprofits and academics and should be considered a WorkinProgress
    we aspire to a future where solutionsoriented multistakeholder collaboration is
    a new norm Our research will be broken into two partsPart 1 is a Canadian Responsible
    Technology Landscape that explores common terms civil society stakeholders current
    and emerging policies and litigation and how asymmetries of power manifestPart
    2 reviews the results of a survey conducted key observations on the current and
    emerging landscape and critical reflections on how to strengthen interdisciplinary
    collaboration among nonprofit organizations academia the tech sector and governmentIn
    an ideal world this work will lead to highlevel consultations and strategies developed
    in collaboration with other ecosystem organizations motivated to move this conversation
    forwardCanada as a Global Leader in Responsible TechDespite and perhaps because
    of the wide array of challenges Canada has an opportunity to become a global leader
    in developing deploying and governing technology in socially sustainable ways
    Getting there requires an urgent focus on strengthening national capabilities
    by investing in strategic and systemsfocused multistakeholder mechanismsIndeed
    organized effectively Canadian civil society represents critical and untapped
    assets to help meet this moment There is also a need to strengthen citizen education
    advance responsible policy and oversight create technical solutions to advance
    the public interest introduce responsible technology certifiers and respond to
    systemic factors that lead to harmful outcomes Canadians can no longer afford
    to wait The time to engage is nowVersion 10 July 2023 Written by Renee BlackFollowWritten
    by goodbot3 Followers·1 FollowingFollowNo responses yetHelpStatusAboutCareersPressBlogPrivacyRulesTermsText
    to speech
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.5517241379310345
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a MultiOutputClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5517   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("praisethefool/human_tech-fields-multilabelclassifier")
# Run inference
preds = model("5 DeSci projects disrupting scientific research and development — Crypto Altruism 0 Skip to Content BLOG CATEGORIES DAOs EDUCATION ENVIRONMENT REFI EQUITY INCLUSION FINANCIAL INCLUSION DEFI NFTs PHILANTHROPY SCIENCE DESCI SOCIAL IMPACT SUPPLY CHAIN COMMENTARY PODCASTS CRYPTO ALTRUISM PODCAST THE WEB3 NONPROFIT IMPACT ON OPTIMISM INFOGRAPHICS RESOURCES BECOME A CRYPTO CHARITY DONATING CRYPTO LEVERAGING AI AT YOUR NONPROFIT ABOUT US WHO WE ARE TRANSPARENCY AFFILIATE PARTNERSHIPS CONTACT SUPPORT US Open Menu Close Menu Open Menu Close Menu BLOG CATEGORIES DAOs EDUCATION ENVIRONMENT REFI EQUITY INCLUSION FINANCIAL INCLUSION DEFI NFTs PHILANTHROPY SCIENCE DESCI SOCIAL IMPACT SUPPLY CHAIN COMMENTARY PODCASTS CRYPTO ALTRUISM PODCAST THE WEB3 NONPROFIT IMPACT ON OPTIMISM INFOGRAPHICS RESOURCES BECOME A CRYPTO CHARITY DONATING CRYPTO LEVERAGING AI AT YOUR NONPROFIT ABOUT US WHO WE ARE TRANSPARENCY AFFILIATE PARTNERSHIPS CONTACT SUPPORT US BLOG Folder CATEGORIES Back DAOs EDUCATION ENVIRONMENT REFI EQUITY INCLUSION FINANCIAL INCLUSION DEFI NFTs PHILANTHROPY SCIENCE DESCI SOCIAL IMPACT SUPPLY CHAIN COMMENTARY Folder PODCASTS Back CRYPTO ALTRUISM PODCAST THE WEB3 NONPROFIT IMPACT ON OPTIMISM INFOGRAPHICS Folder RESOURCES Back BECOME A CRYPTO CHARITY DONATING CRYPTO LEVERAGING AI AT YOUR NONPROFIT Folder ABOUT US Back WHO WE ARE TRANSPARENCY AFFILIATE PARTNERSHIPS CONTACT SUPPORT US 5 DeSci projects disrupting scientific research and development Project HighlightsScienceDAOs Mar 30 Written By Drew Simon 2021 was the year of decentralization and this momentum has only increased into 2022 Not only have we seen incredible growth in the decentralized finance DeFi space but we have also seen the emergence of social impact DAOs decentralized media platforms decentralized VC funds and more recently the emergence of a new field – Decentralized Science or DeSci In short “the decentralized science DeSci movement aims to harness new technologies such as blockchain and ‘Web3’ to address some important research pain points silos and bottlenecks” Whereas scientific research has long been viewed as overly bureaucratic and disjointed the DeSci movement aims to improve this by using blockchain to offer greater transparency and to take on the “profit hungry intermediaries” such as scientific journals that have dominated the traditional research spaceFor some resources on DeSci I recommend you check out the following articlesDeSci an opportunity to decentralize scientific research and publicationA Guide to DeSci the Latest Web3 MovementCall to join the decentralized science movementFor this blog post we will be highlighting 5 DeSci projects that are leading the way and positively disrupting scientific research and development1 VitaDAOOne of the best examples of DeSci in action is VitaDAO a Decentralized Autonomous Organization DAO focused on funding longevity research in “an open and democratic manner” Specifically they are focused on the decentralization of drug development focused on the extension of human life and healthspan They fund earlystage research with the goal of turning the research into biotech companiesVitaDAO is government by holders of VITA tokens which can either be purchased or earned through contributions of work or intellectual property With over 4000 members and 9M in funding raised to support scientific research VitaDAO has proven that the DeSci movement is no laughing matterCheck out some of their featured projects here2 SCINETThe SCINET platform which is built on blockchain enables retail and institutional investors to securely invest in scientific research and technology directly In addition to funding promising scientific research they also offer a “blockchainpowered” cloud laboratory for researchers a rigorous decentralized peer review process and enable researches to document their IP on an immutable blockchain3 AntidoteDAOAntidoteDAO is a decentralized community focused on funding cancer research and other cancer initiatives Their ecosystem includes a governance token and NFT collection which both enable individuals to vote on where to allocate funds In addition to providing funding to charities supporting cancer research and cancer patients a core focus of the DAO is on providing 100K seed fund grants to cancer research teams Research projects are first reviewed by the DAO’s Medical Advisory team and then put to the community for a vote Fun fact we have an upcoming podcast episode with AntidoteDAO that when available will be published HERE Crypto Altruism uses Ledger to keep its assets safeYou’ve probably heard the phrase “not your keys not your coins” By choosing a hard wallet like the Nano S Plus to store your crypto you can rest assured that the keys and the crypto are truly yoursGet your Ledger Nano S Plus now by clicking HERE or on the image below 4 LabDAOLabDAO is an emerging organization which is dedicated to operating a communityrun network of wet and dry labs with the goal of advancing scientific research and development A wet lab is one focused on analysing drugs chemicals and other biological matter whereas a dry lab is one focused on applied or computational mathematical analysis LabDAO is a relatively new project that is still in its infancy but has a promising mission and strong community of support around it 5 MoleculeMolecule is a decentralized scientific research funding platform that operates as a marketplace for researchers seeking out funding and individuals looking to invest in scientific research projects They are “connecting leading researchers to funding by turning intellectual property and its development into a liquid and easily investable asset”Researchers can list their research projects on the Molecule marketplace as a means to engage with potential investors and to secure funding for their project Molecule currently has over 250 research projects listed on their marketplace over 4500 DAO community members and 3 “Bio DAOs” with over 10M in funding in their network According to Molecule “The future of life science research will be driven by open liquid markets for intellectual property powered by web3 technology”We cover more amazing DeSci projects in our more recent postTen more DeSci projects disrupting scientific research development and knowledge sharing Buy me a coffee Send a tip in ETH cryptoaltruismethLike what you are reading Consider contributing to Crypto Altruism so we can continue putting out great content that shines a light on the good being done in the crypto and blockchain community SUPPORT CRYPTO ALTRUISM Please note we make use of affiliate marketing to provide readers with referrals to high quality and relevant products and services DeScidecentralizationscienceblockchainlists Drew Simon Previous Previous Crypto Altruism Podcast Episode 39 AntidoteDAO Decentralized funding of cancer research and charitable initiatives Next Next Crypto Altruism Podcast Episode 38 Using NFTs to empower content creators and help kids learn ft Susie Jaramillo CONTENTBLOGPODCASTINFOGRAPHICSCURATED LISTS ABOUTABOUTSUPPORT USCONTACTDISCLAIMERPRIVACY POLICY Buy me a coffee ETHERC20 cryptoaltruismeth 0xac5C0105914F3afb363699996C9914f193aeDD4A Sign up for our monthly newsletter Thank you © Crypto Altruism 2023 FOLLOW")
```

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### Downstream Use

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<!--
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### Recommendations

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-->

## Training Details

### Training Set Metrics
| Training set | Min | Median    | Max   |
|:-------------|:----|:----------|:------|
| Word count   | 20  | 2568.9241 | 13352 |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- evaluation_strategy: steps
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0017 | 1    | 0.2236        | -               |
| 0.0694 | 40   | -             | 0.1379          |
| 0.0868 | 50   | 0.1722        | -               |
| 0.1389 | 80   | -             | 0.1440          |
| 0.1736 | 100  | 0.0536        | -               |
| 0.2083 | 120  | -             | 0.1412          |
| 0.2604 | 150  | 0.0293        | -               |
| 0.2778 | 160  | -             | 0.1343          |
| 0.3472 | 200  | 0.0234        | 0.1406          |
| 0.4167 | 240  | -             | 0.1266          |
| 0.4340 | 250  | 0.0176        | -               |
| 0.4861 | 280  | -             | 0.1118          |
| 0.5208 | 300  | 0.0193        | -               |
| 0.5556 | 320  | -             | 0.1095          |
| 0.6076 | 350  | 0.0162        | -               |
| 0.625  | 360  | -             | 0.0926          |
| 0.6944 | 400  | 0.0223        | 0.0995          |
| 0.7639 | 440  | -             | 0.0923          |
| 0.7812 | 450  | 0.018         | -               |
| 0.8333 | 480  | -             | 0.0814          |
| 0.8681 | 500  | 0.0045        | -               |
| 0.9028 | 520  | -             | 0.0801          |
| 0.9549 | 550  | 0.0074        | -               |
| 0.9722 | 560  | -             | 0.0794          |

### Framework Versions
- Python: 3.11.12
- SetFit: 1.1.2
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

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