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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1496
+- loss:LoggableMNRL
+widget:
+- source_sentence: According to the passage, why did the intellectual stagnation following
+    Aristotle's work persist for so long?
+  sentences:
+  - world in 587, the Chinese system was very enlightened. Europeans didn't introduce
+    formal civil service exams till the nineteenth century, and even then they seem
+    to have been influenced by the Chinese example. Before credentials, government
+    positions were obtained mainly by family influence, if not outright bribery. It
+    was a great step forward to judge people by their performance on a test. But by
+    no means a perfect solution. When you judge people that way, you tend to get cram
+    schools—which they did in Ming China and nineteenth century England just as much
+    as in present day South Korea. What cram schools are, in effect, is leaks in a
+    seal. The use of credentials was an attempt to seal off the direct transmission
+    of power between generations, and cram schools represent that power finding holes
+    in the seal. Cram schools turn wealth in one generation into credentials in the
+    next. It's hard to beat this phenomenon, because the schools adjust to suit whatever
+    the tests measure. When the tests are narrow and predictable, you get cram schools
+    on the classic model, like those that prepared candidates for Sandhurst (the British
+    West Point) or the classes American students take now to improve their SAT scores.
+    But as the te
+  - tter, but you had no choice in the matter, if you needed money on the scale only
+    VCs could supply. Now that VCs have competitors, that's going to put a market
+    price on the help they offer. The interesting thing is, no one knows yet what
+    it will be. Do startups that want to get really big need the sort of advice and
+    connections only the top VCs can supply? Or would super-angel money do just as
+    well? The VCs will say you need them, and the super-angels will say you don't.
+    But the truth is, no one knows yet, not even the VCs and super-angels themselves.
+    All the super-angels know is that their new model seems promising enough to be
+    worth trying, and all the VCs know is that it seems promising enough to worry
+    about. RoundsWhatever the outcome, the conflict between VCs and super-angels is
+    good news for founders. And not just for the obvious reason that more competition
+    for deals means better terms. The whole shape of deals is changing. One of the
+    biggest differences between angels and VCs is the amount of your company they
+    want. VCs want a lot. In a series A round they want a third of your company, if
+    they can get it. They don't care much how much they pay for it, but they want
+    a lot because the number of series A invest
+  - ' the wrong direction as well. [8] Perhaps worst of all, he protected them from
+    both the criticism of outsiders and the promptings of their own inner compass
+    by establishing the principle that the most noble sort of theoretical knowledge
+    had to be useless. The Metaphysics is mostly a failed experiment. A few ideas
+    from it turned out to be worth keeping; the bulk of it has had no effect at all.
+    The Metaphysics is among the least read of all famous books. It''s not hard to
+    understand the way Newton''s Principia is, but the way a garbled message is. Arguably
+    it''s an interesting failed experiment. But unfortunately that was not the conclusion
+    Aristotle''s successors derived from works like the Metaphysics. [9] Soon after,
+    the western world fell on intellectual hard times. Instead of version 1s to be
+    superseded, the works of Plato and Aristotle became revered texts to be mastered
+    and discussed. And so things remained for a shockingly long time. It was not till
+    around 1600 (in Europe, where the center of gravity had shifted by then) that
+    one found people confident enough to treat Aristotle''s work as a catalog of mistakes.
+    And even then they rarely said so outright. If it seems surprising that the gap
+    was so long, consider ho'
+- source_sentence: What is the main reason why Google's headquarters has a unique
+    feel compared to a typical large company's headquarters?
+  sentences:
+  - ' his need. More or less. Higher ranking members of the military got more (as
+    higher ranking members of socialist societies always do), but what they got was
+    fixed according to their rank. And the flattening effect wasn''t limited to those
+    under arms, because the US economy was conscripted too. Between 1942 and 1945
+    all wages were set by the National War Labor Board. Like the military, they defaulted
+    to flatness. And this national standardization of wages was so pervasive that
+    its effects could still be seen years after the war ended. [1]Business owners
+    weren''t supposed to be making money either. FDR said "not a single war millionaire"
+    would be permitted. To ensure that, any increase in a company''s profits over
+    prewar levels was taxed at 85% And when what was left after corporate taxes reached
+    individuals, it was taxed again at a marginal rate of 93% [2]Socially too the
+    war tended to decrease variation. Over 16 million men and women from all sorts
+    of different backgrounds were brought together in a way of life that was literally
+    uniform. Service rates for men born in the early 1920s approached 80% And working
+    toward a common goal, often under stress, brought them still closer together.
+    Though strictly speaking World '
+  - 'iew: Red Rock.7. GoogleGoogle spread out from its first building in Mountain
+    View to a lot of the surrounding ones. But because the buildings were built at
+    different times by different people, the place doesn''t have the sterile, walled-off
+    feel that a typical large company''s headquarters have. It definitely has a flavor
+    of its own though. You sense there is something afoot. The general atmos is vaguely
+    utopian; there are lots of Priuses, and people who look like they drive them.
+    You can''t get into Google unless you know someone there. It''s very much worth
+    seeing inside if you can, though. Ditto for Facebook, at the end of California
+    Ave in Palo Alto, though there is nothing to see outside.8. Skyline DriveSkyline
+    Drive runs along the crest of the Santa Cruz mountains. On one side is the Valley,
+    and on the other is the sea—which because it''s cold and foggy and has few harbors,
+    plays surprisingly little role in the lives of people in the Valley, considering
+    how close it is. Along some parts of Skyline the dominant trees are huge redwoods,
+    and in others they''re live oaks. Redwoods mean those are the parts where the
+    fog off the coast comes in at night; redwoods condense rain out of fog. The MROSD
+    manages a collection of'
+  - 'Written by Paul Graham
+
+
+    The Bus Ticket Theory of Genius
+
+
+    November 2019
+
+
+    Everyone knows that to do great work you need both natural ability and determination.
+    But there''s a third ingredient that''s not as well understood: an obsessive interest
+    in a particular topic. To explain this point I need to burn my reputation with
+    some group of people, and I''m going to choose bus ticket collectors. There are
+    people who collect old bus tickets. Like many collectors, they have an obsessive
+    interest in the minutiae of what they collect. They can keep track of distinctions
+    between different types of bus tickets that would be hard for the rest of us to
+    remember. Because we don''t care enough. What''s the point of spending so much
+    time thinking about old bus tickets?Which leads us to the second feature of this
+    kind of obsession: there is no point. A bus ticket collector''s love is disinterested.
+    They''re not doing it to impress us or to make themselves rich, but for its own
+    sake. When you look at the lives of people who''ve done great work, you see a
+    consistent pattern. They often begin with a bus ticket collector''s obsessive
+    interest in something that would have seemed pointless to most of their contemporaries.
+    One of the most striking '
+- source_sentence: According to the passage, why is innocence important for children,
+    and what consequence does early jadedness have on a person's development?
+  sentences:
+  - 'ful organizations is partly the history of techniques for preserving that excitement.
+    [4]The team that made the original Macintosh were a great example of this phenomenon.
+    People like Burrell Smith and Andy Hertzfeld and Bill Atkinson and Susan Kare
+    were not just following orders. They were not tennis balls hit by Steve Jobs,
+    but rockets let loose by Steve Jobs. There was a lot of collaboration between
+    them, but they all seem to have individually felt the excitement of working on
+    a project of one''s own. In Andy Hertzfeld''s book on the Macintosh, he describes
+    how they''d come back into the office after dinner and work late into the night.
+    People who''ve never experienced the thrill of working on a project they''re excited
+    about can''t distinguish this kind of working long hours from the kind that happens
+    in sweatshops and boiler rooms, but they''re at opposite ends of the spectrum.
+    That''s why it''s a mistake to insist dogmatically on "work/life balance." Indeed,
+    the mere expression "work/life" embodies a mistake: it assumes work and life are
+    distinct. For those to whom the word "work" automatically implies the dutiful
+    plodding kind, they are. But for the skaters, the relationship between work and
+    life would be better repr'
+  - tect helpless creatures, considering human offspring are so helpless for so long.
+    Without the helplessness that makes kids cute, they'd be very annoying. They'd
+    merely seem like incompetent adults. But there's more to it than that. The reason
+    our hypothetical jaded 10 year old bothers me so much is not just that he'd be
+    annoying, but that he'd have cut off his prospects for growth so early. To be
+    jaded you have to think you know how the world works, and any theory a 10 year
+    old had about that would probably be a pretty narrow one. Innocence is also open-mindedness.
+    We want kids to be innocent so they can continue to learn. Paradoxical as it sounds,
+    there are some kinds of knowledge that get in the way of other kinds of knowledge.
+    If you're going to learn that the world is a brutal place full of people trying
+    to take advantage of one another, you're better off learning it last. Otherwise
+    you won't bother learning much more. Very smart adults often seem unusually innocent,
+    and I don't think this is a coincidence. I think they've deliberately avoided
+    learning about certain things. Certainly I do. I used to think I wanted to know
+    everything. Now I know I don't. DeathAfter sex, death is the topic adults lie
+    most conspic
+  - 'do all eight things wrong. In fact, if you look at the way software gets written
+    in most organizations, it''s almost as if they were deliberately trying to do
+    things wrong. In a sense, they are. One of the defining qualities of organizations
+    since there have been such a thing is to treat individuals as interchangeable
+    parts. This works well for more parallelizable tasks, like fighting wars. For
+    most of history a well-drilled army of professional soldiers could be counted
+    on to beat an army of individual warriors, no matter how valorous. But having
+    ideas is not very parallelizable. And that''s what programs are: ideas. It''s
+    not merely true that organizations dislike the idea of depending on individual
+    genius, it''s a tautology. It''s part of the definition of an organization not
+    to. Of our current concept of an organization, at least. Maybe we could define
+    a new kind of organization that combined the efforts of individuals without requiring
+    them to be interchangeable. Arguably a market is such a form of organization,
+    though it may be more accurate to describe a market as a degenerate case—as what
+    you get by default when organization isn''t possible. Probably the best we''ll
+    do is some kind of hack, like making the program'
+- source_sentence: According to the passage, why are salesmen and top managers exceptions
+    when it comes to being rewarded for increased productivity within large companies?
+  sentences:
+  - olleague from 100 years ago, they'd just get into an ideological argument. Yes,
+    of course, you'll learn something by taking a psychology class. The point is,
+    you'll learn more by taking a class in another department. The worthwhile departments,
+    in my opinion, are math, the hard sciences, engineering, history (especially economic
+    and social history, and the history of science), architecture, and the classics.
+    A survey course in art history may be worthwhile. Modern literature is important,
+    but the way to learn about it is just to read. I don't know enough about music
+    to say. You can skip the social sciences, philosophy, and the various departments
+    created recently in response to political pressures. Many of these fields talk
+    about important problems, certainly. But the way they talk about them is useless.
+    For example, philosophy talks, among other things, about our obligations to one
+    another; but you can learn more about this from a wise grandmother or E. B. White
+    than from an academic philosopher. I speak here from experience. I should probably
+    have been offended when people laughed at Clinton for saying "It depends on what
+    the meaning of the word 'is' is." I took about five classes in college on what
+    the meaning o
+  - at are a safe bet to be acquired for $20 million. There needs to be a chance,
+    however small, of the company becoming really big. Angels are different in this
+    respect. They're happy to invest in a company where the most likely outcome is
+    a $20 million acquisition if they can do it at a low enough valuation. But of
+    course they like companies that could go public too. So having an ambitious long-term
+    plan pleases everyone. If you take VC money, you have to mean it, because the
+    structure of VC deals prevents early acquisitions. If you take VC money, they
+    won't let you sell early.7. VCs want to invest large amounts. The fact that they're
+    running investment funds makes VCs want to invest large amounts. A typical VC
+    fund is now hundreds of millions of dollars. If $400 million has to be invested
+    by 10 partners, they have to invest $40 million each. VCs usually sit on the boards
+    of companies they fund. If the average deal size was $1 million, each partner
+    would have to sit on 40 boards, which would not be fun. So they prefer bigger
+    deals, where they can put a lot of money to work at once. VCs don't regard you
+    as a bargain if you don't need a lot of money. That may even make you less attractive,
+    because it means their invest
+  - 'imes as much wealth as an average employee. A programmer, for example, instead
+    of chugging along maintaining and updating an existing piece of software, could
+    write a whole new piece of software, and with it create a new source of revenue.
+    Companies are not set up to reward people who want to do this. You can''t go to
+    your boss and say, I''d like to start working ten times as hard, so will you please
+    pay me ten times as much? For one thing, the official fiction is that you are
+    already working as hard as you can. But a more serious problem is that the company
+    has no way of measuring the value of your work. Salesmen are an exception. It''s
+    easy to measure how much revenue they generate, and they''re usually paid a percentage
+    of it. If a salesman wants to work harder, he can just start doing it, and he
+    will automatically get paid proportionally more. There is one other job besides
+    sales where big companies can hire first-rate people: in the top management jobs.
+    And for the same reason: their performance can be measured. The top managers are
+    held responsible for the performance of the entire company. Because an ordinary
+    employee''s performance can''t usually be measured, he is not expected to do more
+    than put in a solid effo'
+- source_sentence: How can a startup founder's ambitions be influenced by YC (a startup
+    accelerator) and what is the potential trap founders often fall into when they're
+    trying to seem big?
+  sentences:
+  - 'Written by Paul Graham
+
+
+    The Hardest Lessons for Startups to Learn
+
+
+    April 2006
+
+
+    In something that''s out there, problems are alarming. There is a lot more urgency
+    once you release. And I think that''s precisely why people put it off. They know
+    they''ll have to work a lot harder once they do. [2] 2. Keep Pumping Out Features.
+    Of course, "release early" has a second component, without which it would be bad
+    advice. If you''re going to start with something that doesn''t do much, you better
+    improve it fast. What I find myself repeating is "pump out features." And this
+    rule isn''t just for the initial stages. This is something all startups should
+    do for as long as they want to be considered startups. I don''t mean, of course,
+    that you should make your application ever more complex. By "feature" I mean one
+    unit of hacking-- one quantum of making users'' lives better. As with exercise,
+    improvements beget improvements. If you run every day, you''ll probably feel like
+    running tomorrow. But if you skip running for a couple weeks, it will be an effort
+    to drag yourself out. So it is with hacking: the more ideas you implement, the
+    more ideas you''ll have. You should make your system better at least in some small
+    way every day or two. This '
+  - 'e that they pay attention; it''s when they notice you''re still there. It''s
+    just as well that it usually takes a while to gain momentum. Most technologies
+    evolve a good deal even after they''re first launched — programming languages
+    especially. Nothing could be better, for a new techology, than a few years of
+    being used only by a small number of early adopters. Early adopters are sophisticated
+    and demanding, and quickly flush out whatever flaws remain in your technology.
+    When you only have a few users you can be in close contact with all of them. And
+    early adopters are forgiving when you improve your system, even if this causes
+    some breakage. There are two ways new technology gets introduced: the organic
+    growth method, and the big bang method. The organic growth method is exemplified
+    by the classic seat-of-the-pants underfunded garage startup. A couple guys, working
+    in obscurity, develop some new technology. They launch it with no marketing and
+    initially have only a few (fanatically devoted) users. They continue to improve
+    the technology, and meanwhile their user base grows by word of mouth. Before they
+    know it, they''re big. The other approach, the big bang method, is exemplified
+    by the VC-backed, heavily marketed sta'
+  - 'It tipped from being this boulder we had to push to being a train car that in
+    fact had its own momentum."[4] One of the more subtle ways in which YC can help
+    founders is by calibrating their ambitions, because we know exactly how a lot
+    of successful startups looked when they were just getting started.[5] If you''re
+    building something for which you can''t easily get a small set of users to observe
+    — e. g. enterprise software — and in a domain where you have no connections, you''ll
+    have to rely on cold calls and introductions. But should you even be working on
+    such an idea?[6] Garry Tan pointed out an interesting trap founders fall into
+    in the beginning. They want so much to seem big that they imitate even the flaws
+    of big companies, like indifference to individual users. This seems to them more
+    "professional." Actually it''s better to embrace the fact that you''re small and
+    use whatever advantages that brings.[7] Your user model almost couldn''t be perfectly
+    accurate, because users'' needs often change in response to what you build for
+    them. Build them a microcomputer, and suddenly they need to run spreadsheets on
+    it, because the arrival of your new microcomputer causes someone to invent the
+    spreadsheet.[8] If you have to '
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    "How can a startup founder's ambitions be influenced by YC (a startup accelerator) and what is the potential trap founders often fall into when they're trying to seem big?",
+    'It tipped from being this boulder we had to push to being a train car that in fact had its own momentum."[4] One of the more subtle ways in which YC can help founders is by calibrating their ambitions, because we know exactly how a lot of successful startups looked when they were just getting started.[5] If you\'re building something for which you can\'t easily get a small set of users to observe — e. g. enterprise software — and in a domain where you have no connections, you\'ll have to rely on cold calls and introductions. But should you even be working on such an idea?[6] Garry Tan pointed out an interesting trap founders fall into in the beginning. They want so much to seem big that they imitate even the flaws of big companies, like indifference to individual users. This seems to them more "professional." Actually it\'s better to embrace the fact that you\'re small and use whatever advantages that brings.[7] Your user model almost couldn\'t be perfectly accurate, because users\' needs often change in response to what you build for them. Build them a microcomputer, and suddenly they need to run spreadsheets on it, because the arrival of your new microcomputer causes someone to invent the spreadsheet.[8] If you have to ',
+    "e that they pay attention; it's when they notice you're still there. It's just as well that it usually takes a while to gain momentum. Most technologies evolve a good deal even after they're first launched — programming languages especially. Nothing could be better, for a new techology, than a few years of being used only by a small number of early adopters. Early adopters are sophisticated and demanding, and quickly flush out whatever flaws remain in your technology. When you only have a few users you can be in close contact with all of them. And early adopters are forgiving when you improve your system, even if this causes some breakage. There are two ways new technology gets introduced: the organic growth method, and the big bang method. The organic growth method is exemplified by the classic seat-of-the-pants underfunded garage startup. A couple guys, working in obscurity, develop some new technology. They launch it with no marketing and initially have only a few (fanatically devoted) users. They continue to improve the technology, and meanwhile their user base grows by word of mouth. Before they know it, they're big. The other approach, the big bang method, is exemplified by the VC-backed, heavily marketed sta",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.5865, 0.4398],
+#         [0.5865, 1.0000, 0.3588],
+#         [0.4398, 0.3588, 1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 1,496 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 16 tokens</li><li>mean: 30.39 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 77 tokens</li><li>mean: 274.6 tokens</li><li>max: 359 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>According to the passage, what is more important than the size of a beachhead, and what characteristic must the people within it possess for it to be considered viable?</code>                                     | <code>urgently need, you have a beachhead. [11]The question then is whether that beachhead is big enough. Or more importantly, who's in it: if the beachhead consists of people doing something lots more people will be doing in the future, then it's probably big enough no matter how small it is. For example, if you're building something differentiated from competitors by the fact that it works on phones, but it only works on the newest phones, that's probably a big enough beachhead. Err on the side of doing things where you'll face competitors. Inexperienced founders usually give competitors more credit than they deserve. Whether you succeed depends far more on you than on your competitors. So better a good idea with competitors than a bad one without. You don't need to worry about entering a "crowded market" so long as you have a thesis about what everyone else in it is overlooking. In fact that's a very promising starting point. Google was that type of idea. Your thesis has to be more precis...</code> |
+  | <code>According to the passage, what specific group of workers is uniquely affected by the "cost of checks," and why?</code>                                                                                              | <code>So it was left to the Europeans to explore and eventually to dominate the rest of the world, including China. In more recent times, Sarbanes-Oxley has practically destroyed the US IPO market. That wasn't the intention of the legislators who wrote it. They just wanted to add a few more checks on public companies. But they forgot to consider the cost. They forgot that companies about to go public are usually rather stretched, and that the weight of a few extra checks that might be easy for General Electric to bear are enough to prevent younger companies from being public at all. Once you start to think about the cost of checks, you can start to ask other interesting questions. Is the cost increasing or decreasing? Is it higher in some areas than others? Where does it increase discontinuously? If large organizations started to ask questions like that, they'd learn some frightening things. I think the cost of checks may actually be increasing. The reason is that software plays an increasin...</code> |
+  | <code>According to the passage, what is the most important thing an applicant can do during a Y Combinator interview, and why is this considered more valuable than meeting a higher standard of "convincingness"?</code> | <code>ou're in unless there's some other disqualifying flaw. That is a hard standard to meet, though. Airbnb didn't meet it. They had the first part. They had made something they themselves wanted. But it wasn't spreading. So don't feel bad if you don't hit this gold standard of convincingness. If Airbnb didn't hit it, it must be too high. In practice, the YC partners will be satisfied if they feel that you have a deep understanding of your users' needs. And the Airbnbs did have that. They were able to tell us all about what motivated hosts and guests. They knew from first-hand experience, because they'd been the first hosts. We couldn't ask them a question they didn't know the answer to. We ourselves were not very excited about the idea as users, but we knew this didn't prove anything, because there were lots of successful startups we hadn't been excited about as users. We were able to say to ourselves "They seem to know what they're talking about. Maybe they're onto something. It's not gro...</code> |
+* Loss: <code>__main__.LoggableMNRL</code> with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 5
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.3
+- PyTorch: 2.9.0+cu126
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### LoggableMNRL
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.3",
+    "pytorch": "2.9.0+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ed15161e93b6d775eb8e319f918d7e8368759e48d450bd621529c8ea56b2339
+size 437951328

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
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+      "normalized": false,
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+      "special": true
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}