metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:190175
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
Congruence of Triangles. Triangles. Maths. CBSE 9. CBSE Content - Final.
CBSE.
sentences:
- 'Expressing Multiplication Sentences Practice. . '
- 'Prove R-H-S criteria for congruence of triangle. . '
- >-
DNA. . I'm sure many of y'all have already heard of the molecule
DNA, and it stands for deoxyribonucleic acid. I wrote it out ahead of
time to spare you the pain of watching me spell this in real time. But
it is-- and I think you already have an idea. This is the basic unit of
heredity, or it's what codes all of our genetic information. And what I
want to do in this video-- because I think that's kind of common
knowledge. That's popular knowledge that, oh, everything that makes my
hair black or my eyes blue or whatever, that's all somehow encoded in
our DNA. But what I want to do in this video is give you an idea of how
something like DNA, a molecule, can actually code for what we are. How
does the information, one, get stored in this type of a molecule, then
how does that actually turn into the proteins that make up our enzymes
and our organs and our brain cells and everything else that really make
us us? So this is a computer graphics representation of DNA, and I'm
sure many of y'all have heard of the double helix. And that's in
reference to the structure that DNA takes. And you can see here it's a
double helix. As you can see here, you have two of these lines, and
they're intertwined with each other. You see there, that's one of them,
and then you see another one intertwined like that. And then they're
connected by-- you can almost view it as like these bridges between the
two helixes, and they twist around each other. I think you get the idea.
So the double helix just describes the structure, the shape that DNA
takes, and it leads to all sorts of interesting repercussions in terms
of how heredity takes place and how natural selection and variation
might take place as well. And actually, in the future, I do want to
actually read with you Watson and Crick's paper on the double helix
where they essentially talk about their discovery. The best thing about
that paper, besides the fact that it was probably one of the biggest
discoveries in the history of mankind, is that the paper is only a page
and a half long, and it goes to my general view that if you have
something good to say, it shouldn't take you that long to say it. But
with that said, let's think a little bit about how this can actually
generate the proteins and whatever else that make up all of us. So right
here this is a zoomed-up version of that graphic that I just showed you
a little bit earlier, and this is each of the helixes. So if this is the
magenta side, if you unwound this helix-- right now it shows it in its
wound state, but if I unwind this helix, one side would maybe be this
magenta side of our helix and then one side is this green side, right?
And if you twist it up, you get back to this drawing up here. And then
these bridges that you see in this drawing in the double helix, those
are these connections right here. These are the bridges. Now,
what allows us to code information is that the blocks that make up the
bridges are made of different molecules. And the four different
molecules that are made up in DNA are adenine-- and it's written here on
this little chart. I got all of this from Wikipedia, so if you want more
information I encourage you to go there. Adenine, that's up here. This
is the molecular structure of adenine. It's connected to a sugar right
here, ribose. I won't go into a deoxyribose. And then you have your
phosphate group. But these kind of form the backbone of the DNA: the
sugar and the phosphate groups. And I'm not going to go into the
microbiology of it, because that's not important right now to
understanding just how does this intuitively code for what we are. So
along the backbone, which is identical, and we'll talk about it. They
run in different directions. It's called antiparallel, so they label the
ends. And I'm not going to go into detail there, but the important thing
are these bases here. So you have adenine, and adenine pairs with
thymine, and you see that up here. If you have an adenine molecule here,
an adenine base here, it'll pair with thymine, and this is called the
base pair. Adenine and thymine pair with each other. If you have
thymine, it's going to pair with adenine. And then you have guanine and
it pairs with cytosine. And the names of these, you should know
these names, just because they are almost-- well, if you ever enter any
discussion about DNA and base pairs, this is expected knowledge. But the
names of the molecules and how they're structured, not important just
yet. But what's important is the fact that there are four of them and
that they essentially code information. So you can view one of these
strands in kind of a simplified way. You can just view it as a strand
of-- so this one, if it has an adenine and then it has a cytosine, then
it has a guanine. That's a guanine. They did it in purple. And then it
has a-- oh, no, it has a thymine, not a guanine. So it has a thymine in
purple, and then in blue, it has a guanine. So this strand right here
codes ACTG. And if you were to code the opposite side of the strand, you
could immediately-- I don't even have to look here. I can look at this
side and say, OK, adenine will pair with thymine, cytosine pairs with
guanine, thymine pairs with adenine, and guanine pairs with cytosine. So
they're complementary strands. So if you think about it, they're really
coding the same thing. If you have one of them, you have all of the
information for the other. Now, in our DNA, in a human's DNA, you might
say, hey, Sal, how do I go from these little chains of these molecules?
How does that turn into me? How does that turn into this complex
organism? And the simple answer is, well, the human genome has three
billion of these base pairs. And that's actually just in half of
your chromosomes. And I'll tell you, maybe in this video or a future
video, why we only consider half of your chromosomes, and that's because
essentially you have a pair of every chromosome. I'll talk in more
detail about that. And this number, to some people, they might say, it
only takes three billion base pairs to describe who I am? And some
people would say, wow, it takes three billion base pairs to describe who
I am. I never thought I was that complex. So depending on your point of
view, this is either a large or small number. But when you take these
three billion base pairs, you're actually encoding all of the
information that it takes to make in this case a human being. And
actually it turns out a lot of primates don't have that many different
base pairs than human beings. The amazing thing is even things like
roundworms and fruit flies also number in a surprisingly large fraction
of the base pairs of a human being. Maybe I'll do another video where I
go into comparative biology. But how do these base pairs actually lead
to proteins? I mean, it's fair enough. That's information. It's like you
can view these as ones and zeroes in some type of computer language, but
really they're not just ones and zeroes, because they can take on four
different values. They can take on an A, a T, a C or a G, so you could
think of them as zero, ones, twos and threes, but I won't go into that
whole aspect of it just now. So how does that actually code information?
So DNA when it actually transcribes something-- the process is called
transcription, and I'm going to do a pretty gross simplification of it,
but I think it'll give you the gist of how it codes for proteins. So
what happens when transcription happens is that these two strands split
up, and one of the strands-- let me just take one of them. Let's say it
looks like this. I'll do it all in one color. Let's say it's just
ATGGACG-- I'm just making up stuff-- TA. Let's say that that's the
strand that got split up. And what happens is it transcribes-- and I
won't say itself. There's a whole bunch of enzymes and proteins and a
whole bunch of chemical reactions that have to happen, but this DNA
essentially transcribes a complementary mRNA. And I'll introduce RNA.
It's essentially the exact same thing as-- well, the word is
ribonucleic acid, so it's literally-- you get rid of the deoxy, so you
can kind of say it's got its oxy, and it's ribonucleic acid, but it's
very similar to DNA. It codes in the exact same way. The only difference
between RNA, instead of a thymine, it has something called a uracil. So
every place where you would have expected a thymine, you would have
expected a T, you'll now see a U. So, for example, if this is the DNA
strand, then an RNA, an mRNA, in a messenger RNA strand, will be built
complementary to this. So it'll be built-- let's see. With A, you'd
normally have thymine when you're talking DNA, but now we're talking
RNA, so it'll be a uracil, then an adenine, cytosine, cytosine, uracil,
then we got a guanine, a cytosine, an adenine, and then we'll have a
uracil. So this is the mRNA strand here. And all of this is occurring
inside the nucleus of your cells. And we'll do a whole series of videos
in the future about the structure of our cells, but I think most of us
know that our cells-- and I'll talk more about eukaryotic and
prokaryotic organisms in the future, but most complex organisms, they
have a cell nucleus where we have all of our chromosomes that contain
all of our DNA. And so this mRNA then detaches itself from the DNA that
it was transcribed from, and then it leaves the nucleus, and it goes to
these structures called ribosomes. I'm oversimplifying it a little bit,
but at the ribosomes, this mRNA is translated into proteins. So let me
do that. So let's say this is the mRNA. It was transcribed from that
DNA, so let me get rid of that DNA now. I got rid of the DNA. This is
the mRNA that we were able to transcribe from that DNA, and they have
these other things called tRNA or transfer RNA. And what these are-- and
this is the really interesting part. So you may or may not know that
pretty much everything we are is made up of proteins. And these
proteins, the building blocks of proteins are amino acids. And for those
of you who like to lift weights, I'm sure you've seen ads for amino acid
supplements and things of the like. And the reason why they talk about
amino acids is because those are the building blocks of proteins. My son
actually has an allergy to milk protein, so we had to get him a formula
that was just pure amino acids, just all of the milk proteins broken
down. So if you look at a protein, it's actually a chain of these amino
acids and usually a fairly long chain. We'll look at some protein
structures in the very near future, just to give you an idea of things.
It's a very long chain of these amino acids, and there are actually 20
different amino acids. Twenty different amino acids are pretty much the
structure of all of our proteins. Let me write that. So a very
obvious question is how can these things code for 20 different amino
acids? I can only have four different things in this little bucket right
here. And then you just have to go back to your combinatorics, or if you
can't go back to it to watch the playlist on probability and
combinatorics, and say, OK, there's only four ways that I can have for
each of these bases. There's only four different bases that I can have
here, either an adenine guanine, cytosine or, depending on whether we're
talking about DNA or RNA, a uracil or a thymine. But how can we increase
the combinations? Well, if we include two of them, if we include two
bases, then how many combinations can we have? Well, we have four
possibilities here, then we'd have four possibilities here, so we'd have
16 possibilities. But that's still not enough. That's still not enough
to code for one of 20 amino acids to say, hey, this is going to code for
amino acid number five, and we'll talk more about their actual names. So
what do we have to do? Well, we have to use three of them. So three of
them, there's actually four times four times four possibilities here, so
they could code for 64 different things. They could take on 64 different
combinations or permutations, this UAC right here. So if we have three
of these bases, we can actually code for an amino acid. Actually, it's
overkill, because we can actually have 64 combinations here, and there
are only 20 amino acids, so we can even have redundant combinations code
for different amino acids. For example, we might say that, and this
isn't the actual code, but maybe UAC, and I should look these up. This
codes for amino acid number 1. And if it was AAU, then this codes for
amino acid number 2. And if I have-- I mean, I think you get the idea.
If I have GGG, this codes for amino acid number 10. And what happens is
when this messenger RNA leaves the nucleus, it goes to the ribosomes,
and at the ribosomes-- we're going to look at that diagram in a few
seconds-- but at the ribosomes-- let me take my same mRNA molecule. And
they're much longer than what I'm showing here. This is just a fraction
of an mRNA molecule. So I'll take my mRNA molecule, and what they
do is they essentially act as a template for tRNA molecules. And tRNA
molecules are these molecules that are attached to the-- they're almost
like the trucks for the amino acids. So let's say I have some amino acid
right here, and then I have another amino acid that's right here like
that, and then I have another amino acid that's like that. They'll be
attached to tRNA molecules. So let's say that this tRNA molecule has on
it-- so this amino acid is attached to a tRNA molecule that has the code
on it A-- let me do it in a darker color. It has the code AUG.
This one right here has the code-- let me pick another one. Let's say it
has GGAC. So what's going to happen? When you're in the ribosome,
and it's a complex situation, but actually what's happening isn't too
fancy. This tRNA, it wants to bond to this part of the mRNA. Why?
Because adenine bonds with uracil, uracil bonds with adenine, and
guanine bonds with cyotsine, so it'll pull up right here. It'll pull up
right next to this thing, and actually, I should probably-- well, I
don't know if I can rotate it. But it'll just pull up right here and
attach to this mRNA molecule. And this right here is tRNA. This
is mRNA. And the names don't matter. I really just want to give you the
big picture idea of how the proteins are actually formed. And this is an
amino acid. I don't know, let's call it amino acid 1, amino acid 5,
amino acid 20. This guy, he's going to pull up right here. The guanine
is attracted to the cytosine, and if you watch the chemistry videos,
these are actually hydrogen bonds that form the base pairs. Adenine,
wants to pull up to uracil, cytosine to guanine, and so on and so forth.
And so once all of these guys have pulled up-- let me do that. So once
you've pulled up, let's say that this is-- I could do it up here. This
is my mRNA molecule. I'm not going to draw the specifics right there. My
little tRNA's pull up, pull up next to it, and they each hold a payload,
right? So this first one holds this payload right here of this amino
acid. The second one holds this payload of this amino acid and so forth
and so on. And so it might keep going, and there's another green amino
acid here. They really don't have those colors, but I'm just-- just for
the sake of simplicity like that. And then the amino acids bond to each
other when they're held like that close to each other. This doesn't
happen all by itself. The ribosome serves a purpose, and there are
enzymes that facilitate this process, but once these guys bond together,
the tRNA detaches, and you have this chain of amino acids. And then the
chain of amino acids starts to bend around so they have all of these--
and it's actually a fascinating-- I mean, people spend their lives
studying how proteins fold, and that's actually where they get most of
their structural properties. It's not just the chain of the amino acids,
but what's more important is how these amino acids actually fold. So
once you fold them, they form these really ultracomplex patterns based
on what amino acid is attracted to what other amino acid in these very
intricate three-dimensional shapes. And what I took here from Wikipedia
is these are some amino acids. And just to be able to relate this to the
DNA, this right here is insulin. It's key in our ability to process
glucose in our body. So this right here is insulin. It's a hormone. So
sometimes you hear people talk about your immune system. Sometimes you
hear people talking about your endocrine system and hormones, sometimes
your digestive system. This is hemoglobin, what essentially transports
our oxygen in our blood. But all of these things are proteins, and all
these little, little folds you see, these are all little amino-- I mean,
they're just little dots of amino acids. Some of these are multiple
chains of amino acids kind of fitting together like a big puzzle, but
some of them or just single chains of amino acids. For insulin right
here, this is 50 amino acids. And then once the chain forms, it all
bundles together and forms this little blob like you see, but the shape
of that blob is super important for insulin being able to perform the
function that it needs to perform in our systems. But this right here is
approximately 50-- I forgot the exact number-- amino acids. This
right here, this immunoglobulin G, which is part of our immune system,
this is roughly 1,500 amino acids. So how much DNA or how many base
pairs had to code for this? Well, three times as much, right? Because
you have to have three base pairs that code for one amino acid, and
actually, three base pairs, this is called a codon, because it codes for
amino acids. So three base pairs make a codon. So if you have 50 amino
acids that make up insulin, that means you're going to have to have 50
codons, which means you have to have 150 bases or 150 of these A's and
G's and T's. If you have 1,500 amino acids, that means you're going to
have to have 1,500 codons, which means you're going to have roughly
4,500 of these base pairs that code for it. Now, there are some notions
that get confused a lot, so I went to kind of the smallest level of our
DNA right here, and this is the level at which-- well, this is RNA that
I'm pointing to right there, but this is the smallest level of DNA, and
that's the level at which the information is actually coded. But how
does that relate to things like genes and chromosomes and things that
you might talk about in other contexts? So let's say the 150 base
pairs that coded for insulin, these make up a gene. And these
4,500 base pairs make up another gene. Now, all of the genes don't make
proteins, but all of the proteins are made by genes. So let's say I have
just a bunch of-- I'll just make another A, G, and it goes down, down,
down, and you have a T and then a C and a C, and let's say I have 4,500
of these. These could code for a protein. These could code for protein,
or they could have all of these other kind of regulatory functions
telling what other parts of the DNA should and should not be coded and
how the DNA behaves, so it becomes super, super complex. But this kind
of section of our DNA, this is what we refer to as a gene, and a gene
can have anywhere from a couple of hundreds of these base pairs or these
bases to several thousand of these base pairs. Now, a gene is that part
of our chromosome that codes for a particular protein or serves a
certain function. Now, there are different versions of genes.
It's a gross oversimplification, but let me say this is the gene for
insulin. Now, there might be slight variations in how insulin can
be coded for, and I'm kind of going out of my domain right here, because
I don't know if that's true. And maybe I shouldn't just speak
specifically about insulin, but it's coding for some protein, but
there's maybe multiple different ways that that protein can be coded.
Maybe instead of a T here, sometimes there's a C there. It still codes
for the same protein. It doesn't change it quite enough, but that
protein acts just a little bit different. It's a slight variant. I'll
use that word. Now, each variant of this gene is called an allele.
It's a specific variant of your gene. Now, if you take
this DNA chain, and this chain over here-- let's see. This is one base
pair. This might be like one base. This is another base. Maybe this is
an adenine and then this would be a thymine over here in green. This is
an adenine and this would be a thymine. If right here this is a guanine,
then right here would be a cytosine. This would be just a very small
section. If I were to like zoom out, and let's say we have a big chain
of DNA where each of these little dots are a base pair that I'm drawing
here, maybe this section codes for gene 1. And then there's some noise
or things that we haven't fully understood yet. Now, I want to be clear.
Just with a simple discussion of DNA, we're already kind of approaching
the frontiers of what we know and what we don't know, because DNA is
hugely complex, and there's all of these feedback structures, and
certain genes tell you to code for other genes and not to code for other
genes and to code under certain circumstances, hugely complex. So
there's huge sections of DNA that we still don't understand what exactly
they do. But then maybe they'll have another section here that codes for
gene 2. Maybe gene 2 is a little bit longer. Maybe it's 1,000 base
pairs. But when you take all of these and you turn it into a-- it kind
of winds in on itself like this. Let me do it. So it'll wind up, winding
in on itself like this and do all sorts of crazy things. Remember, it
completely bundles itself up, and then it looks something like that.
Then you get a chromosome. And just to get an idea of how large a
chromosome is compared to the actual base pairs, chromosome number one
in the human genome-- so we have 23 pairs. If you look at it inside of a
nucleus-- so let's say that's the nucleus. Let's say this is the cell.
The cell is much bigger than what I'm showing. But we have 23 pairs of
chromosomes. I won't do all of them. You can actually see
chromosomes in a not-too-expensive microscope, so we're already getting
to a scale that we can start to look at. But the largest chromosome,
which is chromosome number one in the human genome, just to give an idea
of how much information it's packing, that thing right there has 220
million base pairs. Sometimes people talk about chromosomes and genetics
and genes and base pairs interchangeably, but it's very important to
kind of get an idea of scale. These chromosomes are a super-long strand
of DNA that's all configured and bundled up, and it contains 220 million
base pairs. So the actual elements that are coding for the information
are unbelievably small relative to the chromosome itself. But now that
we understand a little bit, and actually I want to take a look back at
this, because this kind of blows my mind, that if you just take those
little combinations of those amino acids, you can form these very
intricate, very advanced structures that we're still fully understanding
how they actually interact with each other and regulate how all of our
biological processes work. And what's even more amazing is that this
scheme that I've talked about in this video about DNA to mRNA to tRNA to
these molecules, this is true for all of life on our planet, so we all
share this same mechanism. Me and this plant, we share that common root
that we all have DNA. As different as me and that roach that I might not
like to be in the same room, we all share that same common root of DNA
and that all of it codes to proteins in this exact same way, that
there's this commonality amongst all life. That, to me, is mind blowing.
Then even more mind blowing is how these very complex shapes are formed
by the DNA. And this isn't speculation. This is observed behavior. This
is a fascinating structure right here, but it's just based on 20 amino
acid-- you can almost view the amino acid as the LEGOS, and you put the
LEGOS together, and just the chemical interactions form these fairly
impressive structures right here. So now that we know a little bit about
DNA and how it codes into protein, we can take a little jump back and
talk a little bit more about how variation is actually introduced into a
population.
- source_sentence: >-
Explore. Assessments. Cell. Cell Structure and Micro-organisms. Grade 7.
Science channel.
sentences:
- >-
Area Builder. Create your own shapes using colorful blocks and explore
the relationship between perimeter and area. Compare the area and
perimeter of two shapes side-by-side. Challenge yourself in the game
screen t.
- 'Cells Practice. . '
- ": Human Actions and the Sixth Mass Extinction. . This is one of the most powerful birds (http://www.ck12.org/biology/Birds) in the world. Could it go extinct?\n\nThe Philippine Eagle, also known as the Monkey-eating Eagle, is among the rarest, largest, and most powerful birds (http://www.ck12.org/biology/Birds) in the world. It is critically endangered, mainly due to massive loss of habitat due to deforestation in most of its range. Killing a Philippine Eagle is punishable under Philippine law by twelve years in jail and heavy fines.\n\nHuman Actions and the\_Sixth Mass Extinction\n\nOver 99 percent of all species that ever lived on Earth have gone extinct. Five mass extinctions (http://www.ck12.org/life-science/Mass-Extinctions-in-Life-Science) are recorded in the fossil record (http://www.ck12.org/biology/The-Fossil-Record). They were caused by major geologic and climatic events. Evidence shows that a sixth mass extinction is occurring now. Unlike previous mass extinctions (http://www.ck12.org/life-science/Mass-Extinctions-in-Life-Science), the sixth extinction is due to human actions.\n\nSome scientists consider the sixth extinction to have begun with early hominids during the Pleistocene. They are blamed for over-killing big mammals such as mammoths. Since then, human actions have had an ever greater impact on other species. The present rate of extinction is between 100 and 100,000 species per year. In 100 years, we could lose more than half of Earth’s remaining species.\n\nCauses of Extinction\n\nThe single biggest cause of extinction today is habitat loss. Agriculture (http://www.ck12.org/chemistry/Agriculture), forestry, mining, and urbanization have disturbed or destroyed more than half of Earth’s land area. In the U.S., for example, more than 99 percent of tall-grass prairies have been lost. Other causes of extinction today include:\n\nExotic species introduced by humans into new habitats. They may carry disease, prey on native species, and disrupt food webs. Often, they can out-compete native species because they lack local predators. An example is described in Figure below (http://www.ck12.org/book/CK-12-Biology-Concepts/section/6.26/#x-ck12-QmlvLTEyLTIzLWJyb3duLXRyZWUtc25ha2U.).\n\nOver-harvesting of fish (http://www.ck12.org/biology/Fish), trees, and other organisms. This threatens their survival and the survival of species that depend on them.\n\nGlobal climate change, largely due to the burning of fossil fuels. This is raising Earth’s air and ocean temperatures. It is also raising sea levels. These changes threaten many species.\n\nPollution, which adds chemicals, heat (http://www.ck12.org/physical-science/Heat-in-Physical-Science), and noise to the environment beyond its capacity to absorb them. This causes widespread harm to organisms.\n\nHuman overpopulation, which is crowding out other species. It also makes all the other causes of extinction worse.\n\nThe brown tree snake is an exotic species that has caused many extinctions on Pacific islands such as Guam.\n\nEffects of Extinction\n\nThe results of a study released in the summer of 2011 have shown that the decline in the numbers of large predators like sharks, lions and wolves is disrupting Earth's ecosystem in all kinds of unusual ways. The study, conducted by scientists from 22 different institutions in six countries, confirmed the sixth mass extinction. The study states that this mass extinction differs from previous ones because it is entirely driven by human activity through changes in land use, climate, pollution, hunting, fishing and poaching. The effects of the loss of these large predators can be seen in the oceans and on land.\n\nFewer cougars in the western US state of Utah led to an explosion of the deer population. The deer ate more vegetation, which altered the path of local streams and lowered overall biodiversity (http://www.ck12.org/biology/Biodiversity).\n\nIn Africa, where lions and leopards are being lost to poachers, there is a surge in the number of olive baboons, who are transferring intestinal parasites to humans living nearby.\n\nIn the oceans, industrial whaling led a change in the diets of killer whales, who eat more sea lions, seals, and otters and have dramatically lowered the population counts of those species.\n\nThe study concludes that the loss of big predators has likely driven many of the pandemics, population collapses and ecosystem shifts the Earth has seen in recent centuries.\n\nDisappearing Frogs\n\nAround the world, frogs are declining at an alarming rate due to threats like pollution, disease, and climate change. Frogs bridge the gap between water (http://www.ck12.org/biology/Water-Advanced) and land habitats, making them the first indicators (http://www.ck12.org/chemistry/Indicators) of ecosystem changes.\n\nNonnative Species\n\nScoop a handful of critters out of the San Francisco Bay and you'll find many organisms from far away shores. Invasive kinds of mussels, fish (http://www.ck12.org/biology/Fish), and more are choking out native species, challenging experts around the state to change the human behavior that brings them here.\n\nHow You Can Help Protect Biodiversity\n\nThere are many steps you can take to help protect biodiversity (http://www.ck12.org/biology/Biodiversity). For example:\n\nConsume wisely. Reduce your consumption wherever possible. Re-use or recycle rather than throw out and buy new. When you do buy new, choose products that are energy (http://www.ck12.org/physics/Energy) efficient and durable.\n\nAvoid plastics. Plastics are made from petroleum and produce toxic waste.\n\nGo organic. Organically grown food is better for your health. It also protects the environment from pesticides and excessive nutrients in fertilizers.\n\nSave energy (http://www.ck12.org/physics/Energy). Unplug electronic equipment and turn off lights when not in use. Take mass transit instead of driving.\n\nLost Salmon\n\nWhy is the salmon population of Northern California so important? Salmon do not only provide food for humans, but also supply necessary nutrients for their ecosystems (http://www.ck12.org/biology/Ecosystems). Because of a sharp decline in their numbers, in part due to human interference, the entire salmon fishing season off California and Oregon was canceled in both 2008 and 2009. The species in the most danger of extinction is the California coho salmon.\n\nSummary\n\nEvidence shows that a sixth mass extinction is occurring. The single biggest cause is habitat loss caused by human actions.\n\nThere are many steps you can take to help protect biodiversity. For example, you can use less energy (http://www.ck12.org/physics/Energy).\n\nReview\n\nHow is human overpopulation related to the sixth mass extinction?\n\nWhy might the brown tree snake or the Philippine Eagle serve as “poster species” for causes of the sixth mass extinction?\n\nDescribe a hypothetical example showing how rising sea levels due to global (http://www.ck12.org/earth-science/Global-Warming)warming (http://www.ck12.org/earth-science/Global-Warming) might cause extinction.\n\nCreate a poster that conveys simple tips for protecting biodiversity.\n\nResources"
- source_sentence: >-
Classifying geometric shapes. Plane figures. 4th grade. Math by grade.
Khan Academy (English - US curriculum).
sentences:
- >-
Classifying shapes by lines and angles. Lindsay classifies a shape based
on hints about its sides and angles.
. - [Voiceover] Which shape matches all three clues? So here we have
three clues and we want to see which shape down below matches all three
of these statements. So let's start with the first clue. The first clue
says the shape is a quadrilateral, quad meaning four-sided. So looking
down here at our shapes, let's see which ones match that first clue.
Shape one has one, two, three, four sides. So it is a quadrilateral.
Shape two has one, two, three, four sides. So also a quadrilateral.
Shape three has one, two, three, four, five, six sides. So it is not a
quadrilateral. It's a six-sided shape or a hexagon. So we can rule that
one out. It doesn't match clue one so there's no way it can match all
three clues. And finally shape four has one, two, three, four sides
again so it is also a quadrilateral. So after clue one, we still have
three possible answers. This first shape, the second shape, and the
fourth shape all match clue one, they're all quadrilaterals. Looking at
clue two, it says our shape has no right angles. Right angles are also
90 degree angles. Right angles are 90 degree angles and they look
something like this and we often see them marked with a square in the
middle because they are sort of like square angles. We can create a
square from the opening that they form, that these angles form. So this
is a right angle. Looking now down at our shapes, we can see right away
on shape one has two right angles. There's a square corner and another
square corner. So this has right angles, but the shape we're looking for
has no right angles so we can rule this shape out. Shape two does not
have any right angles. These are not squared off corners. And same with
shape four, no right angles. So both of those still match both clues one
and two. So we have two shapes left. They're both quadrilaterals and
they have no right angles. And finally our last clue, the shape has four
sides, we knew that 'cause it was a quadrilateral, and those sides are
of equal length. That means each of the sides is the same length.
Looking at this first one that we have left, shape two, it looks like
these sides on the ends are shorter than the sides going up and down. So
it looks like they are not equal length. So we can rule this one out.
But let's be sure this last one works. Here the sides all look like
they're the same length, but the way we can know for sure that they are
is these tick marks. Any time you have these marks, it's saying that any
side that has the same amount of marks is the same length. All four of
these sides have exactly one tick mark so they are all equal in length.
So shape four matches all three clues. It is a quadrilateral, there are
no right angles and it has four sides of equal length. So shape four is
our answer.
- 'Resistors in Series. . '
- >-
Amoeba in motion. This a video of an Amoeba . Movement of the Amoeba is
shown. First the colorless ectoplasma moves in front of the pseodopodia,
followed by the grained entoplasma. The video is done with the phase
contrast technique. Please have a look at my homepage for more:
http://www.dr-ralf-wagner.de.
- source_sentence: >-
Electromagnet. Electricity and Magnetism. Physical Science. Science.
K-12.
sentences:
- 'Determining Unknown Angles in Complex Composite Figures Practice. . '
- 'Electromagnet. . '
- >-
Literal vs figurative language Exercise. . It this an example of literal
or figurative language?
He has lost his marbles.
- Literal
- Figurative
- It could be both.
Has the word literally been used correctly in this sentence?
Stars are literally millions of kilometres away.
- Yes
- No
Has the word literally been used correctly in this sentence?
I haven't been to a comic book store in literally a million years.
- Yes
- No
Is this an example of literal or figurative language?
The old wall is falling apart.
- Literal
- Figurative
- It could be both.
Is this an example of literal or figurative language?
Our debating team is falling apart.
- Literal
- Figurative
- It could be both.
Is this an example of literal or figurative language?
I am feeling blue.
- Literal
- Figurative
- It could be both.
Is this an example of literal or figurative language?
The sky is blue.
- Literal
- Figurative
- It could be both.
What is the danger of writing using only literal language?
- The language can be dry and boring.
- Meaning can be lost.
- Meaning can be exaggerated.
- There are no dangers of writing in literal language.
Which of these is most likely to be written using literal language?
- A recipe
- A poem
- A soliloquy
- A short story
Which of the following would you not find in literal language?
- Descriptive words
- Direct language
- Exactly what's happening in the story
- Similes
- source_sentence: >-
Determining Unknown Angles in Complex Composite Figures. Triangles.
Geometry. Grade 4. Elementary Math. Math. K-12.
sentences:
- 'Determining Unknown Angles in Complex Composite Figures. . '
- 'Area of parallelograms. . '
- >-
Initial value & common ratio of exponential functions. Get comfortable
with the basic ingredients of exponential functions: the
Initial value and the common ratio.
. - [Voiceover] So let's think about a function. I'll just give an
example. Let's say, h of n is equal to one-fourth times two to the n.
So, first of all, you might notice something interesting here. We have
the variable, the input into our function. It's in the exponent. And a
function like this is called an exponential function. So this is an
exponential. Ex-po-nen-tial. Exponential function, and that's because
the variable, the input into our function, is sitting in its definition
of what is the output of that function going to be. The input is in the
exponent. I could write another exponential function. I could write, f
of, let's say the input is a variable, t, is equal to is equal to five
times times three to the t. Once again, this is an exponential function.
Now there's a couple of interesting things to think about in exponential
function. In fact, we'll explore many of them, but I'll get a little
used to the terminology, so one thing that you might see is a notion of
an initial value. In-i-tial Intitial value. And this is essentially the
value of the function when the input is zero. So, for in these cases,
the initial value for the function, h, is going to be, h of zero. And
when we evaluate that, that's going to be one-fourth times two to the
zero. Well, two to the zero power, is just one. So it's equal to
one-fourth. So the initial value, at least in this case, it seems to
just be that number that sits out here. We have the initial value times
some number to this exponent. And we'll come up with the name for this
number. Well let's see if this was true over here for, f of t. So, if we
look at its intial value, f of zero is going to be five times three to
the zero power and, the same thing again. Three to the zero is just one.
Five times one is just five. So the initial value is once again, that.
So if you have exponential functions of this form, it makes sense. Your
initial value, well if you put a zero in for the exponent, then the
number raised to the exponent is just going to be one, and you're just
going to be left with that thing that you're multiplying by that.
Hopefully that makes sense, but since you're looking at it, hopefully it
does make a little bit. Now, you might be saying, well what do we call
this number? What do we call that number there? Or that number there?
And that's called the common ratio. The common common ratio. And in my
brain, we say well why is it called a common ratio? Well, if you thought
about integer inputs into this, especially sequential integer inputs
into it, you would see a pattern. For example, h of, let me do this in
that green color, h of zero is equal to, we already established
one-fourth. Now, what is h of one going to be equal to? It's going to be
one-fourth times two to the first power. So it's going to be one-fourth
times two. What is h of two going to be equal to? Well, it's going to be
one-fourth times two squared, so it's going to be times two times two.
Or, we could just view this as this is going to be two times h of one.
And actually I should have done this when I wrote this one out, but this
we can write as two times h of zero. So notice, if we were to take the
ratio between h of two and h of one, it would be two. If we were to take
the ratio between h of one and h of zero, it would be two. That is the
common ratio between successive whole number inputs into our function.
So, h of I could say h of n plus one over h of n is going to be equal to
is going to be equal to actually I can work it out mathematically.
One-fourth times two to the n plus one over one-fourth times two to the
n. That cancels. Two to the n plus one, divided by two to the n is just
going to be equal to two. That is your common ratio. So for the function
h. For the function f, our common ratio is three. If we were to go the
other way around, if someone said, hey, I have some function whose
initial value, so let's say, I have some function, I'll do this in a new
color, I have some function, g, and we know that its initial initial
value is five. And someone were to say its common ratio its common ratio
is six, what would this exponential function look like? And they're
telling you this is an exponential function. Well, g of let's say x is
the input, is going to be equal to our initial value, which is five.
That's not a negative sign there, Our initial value is five. I'll write
equals to make that clear. And then times our common ratio to the x
power. So once again, initial value, right over there, that's the five.
And then our common ratio is the six, right over there. So hopefully
that gets you a little bit familiar with some of the parts of an
exponential function, why they are called what they are called.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@10
- cosine_precision@50
- cosine_precision@100
- cosine_recall@10
- cosine_recall@50
- cosine_recall@100
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: eval ir
type: eval-ir
metrics:
- type: cosine_accuracy@1
value: 0.6326203208556149
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7914438502673797
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8481283422459893
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8967914438502674
name: Cosine Accuracy@10
- type: cosine_precision@10
value: 0.23825311942959004
name: Cosine Precision@10
- type: cosine_precision@50
value: 0.0709126559714795
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.03923529411764706
name: Cosine Precision@100
- type: cosine_recall@10
value: 0.7040714788488945
name: Cosine Recall@10
- type: cosine_recall@50
value: 0.8725457895726481
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.9169531730172458
name: Cosine Recall@100
- type: cosine_ndcg@10
value: 0.652860842686591
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7233662960133574
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5971091711102727
name: Cosine Map@100
SentenceTransformer
This is a sentence-transformers 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
- Maximum Sequence Length: 128 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel
(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})
(2): Normalize()
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Determining Unknown Angles in Complex Composite Figures. Triangles. Geometry. Grade 4. Elementary Math. Math. K-12. ',
'Determining Unknown Angles in Complex Composite Figures. . ',
"Initial value & common ratio of exponential functions. Get comfortable with the basic ingredients of exponential functions: the\nInitial value and the common ratio.\n\n. - [Voiceover] So let's think about a function. I'll just give an example. Let's say, h of n is equal to one-fourth times two to the n. So, first of all, you might notice something interesting here. We have the variable, the input into our function. It's in the exponent. And a function like this is called an exponential function. So this is an exponential. Ex-po-nen-tial. Exponential function, and that's because the variable, the input into our function, is sitting in its definition of what is the output of that function going to be. The input is in the exponent. I could write another exponential function. I could write, f of, let's say the input is a variable, t, is equal to is equal to five times times three to the t. Once again, this is an exponential function. Now there's a couple of interesting things to think about in exponential function. In fact, we'll explore many of them, but I'll get a little used to the terminology, so one thing that you might see is a notion of an initial value. In-i-tial Intitial value. And this is essentially the value of the function when the input is zero. So, for in these cases, the initial value for the function, h, is going to be, h of zero. And when we evaluate that, that's going to be one-fourth times two to the zero. Well, two to the zero power, is just one. So it's equal to one-fourth. So the initial value, at least in this case, it seems to just be that number that sits out here. We have the initial value times some number to this exponent. And we'll come up with the name for this number. Well let's see if this was true over here for, f of t. So, if we look at its intial value, f of zero is going to be five times three to the zero power and, the same thing again. Three to the zero is just one. Five times one is just five. So the initial value is once again, that. So if you have exponential functions of this form, it makes sense. Your initial value, well if you put a zero in for the exponent, then the number raised to the exponent is just going to be one, and you're just going to be left with that thing that you're multiplying by that. Hopefully that makes sense, but since you're looking at it, hopefully it does make a little bit. Now, you might be saying, well what do we call this number? What do we call that number there? Or that number there? And that's called the common ratio. The common common ratio. And in my brain, we say well why is it called a common ratio? Well, if you thought about integer inputs into this, especially sequential integer inputs into it, you would see a pattern. For example, h of, let me do this in that green color, h of zero is equal to, we already established one-fourth. Now, what is h of one going to be equal to? It's going to be one-fourth times two to the first power. So it's going to be one-fourth times two. What is h of two going to be equal to? Well, it's going to be one-fourth times two squared, so it's going to be times two times two. Or, we could just view this as this is going to be two times h of one. And actually I should have done this when I wrote this one out, but this we can write as two times h of zero. So notice, if we were to take the ratio between h of two and h of one, it would be two. If we were to take the ratio between h of one and h of zero, it would be two. That is the common ratio between successive whole number inputs into our function. So, h of I could say h of n plus one over h of n is going to be equal to is going to be equal to actually I can work it out mathematically. One-fourth times two to the n plus one over one-fourth times two to the n. That cancels. Two to the n plus one, divided by two to the n is just going to be equal to two. That is your common ratio. So for the function h. For the function f, our common ratio is three. If we were to go the other way around, if someone said, hey, I have some function whose initial value, so let's say, I have some function, I'll do this in a new color, I have some function, g, and we know that its initial initial value is five. And someone were to say its common ratio its common ratio is six, what would this exponential function look like? And they're telling you this is an exponential function. Well, g of let's say x is the input, is going to be equal to our initial value, which is five. That's not a negative sign there, Our initial value is five. I'll write equals to make that clear. And then times our common ratio to the x power. So once again, initial value, right over there, that's the five. And then our common ratio is the six, right over there. So hopefully that gets you a little bit familiar with some of the parts of an exponential function, why they are called what they are called.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Dataset:
eval-ir - Evaluated with
InformationRetrievalEvaluator
| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.6326 |
| cosine_accuracy@3 | 0.7914 |
| cosine_accuracy@5 | 0.8481 |
| cosine_accuracy@10 | 0.8968 |
| cosine_precision@10 | 0.2383 |
| cosine_precision@50 | 0.0709 |
| cosine_precision@100 | 0.0392 |
| cosine_recall@10 | 0.7041 |
| cosine_recall@50 | 0.8725 |
| cosine_recall@100 | 0.917 |
| cosine_ndcg@10 | 0.6529 |
| cosine_mrr@10 | 0.7234 |
| cosine_map@100 | 0.5971 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 190,175 training samples
- Columns:
topicandcontent - Approximate statistics based on the first 1000 samples:
topic content type string string details - min: 15 tokens
- mean: 41.93 tokens
- max: 128 tokens
- min: 5 tokens
- mean: 62.57 tokens
- max: 128 tokens
- Samples:
topic content Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and sides of a convex polygon, the size and exterior angle of any convex polygon.Regular and Irregular Polygons. .Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and sides of a convex polygon, the size and exterior angle of any convex polygon.Classifying triangles based on its angles. A triangle is a closed figure consisting of three-line segments which are joined end to end. The joined line segments of a triangle form three angles. You can classify triangles according to sides and angles.. Classifying triangles based on its angles
Albert Mhango, Mzimba
Introduction:
A triangle is a closed figure consisting of three-line segments which are joined end to
end. The joined line segments of a triangle form three angles. You can classify
triangles according to sides and angles.
What is an interior angle? An interior angle is an inside of a shape.
Explanation:
When classifying triangles according to its angles, you look at the sizes of their
interior angles. Under this classification, you have the following types of triangles:
1. Acute angled triangle: A triangle in which all interior angles are acute angles. Do
you remember the meaning of acute angle? It is an angle which is less than 90°.
Figure shows an example of an acute an...Triangles and polygons. Space, shape and measurement. Form 1. Malawi Mathematics Syllabus. Learning outcomes: students must be able to solve problems involving angles, triangles and polygons including: types of triangles, calculate the interior and exterior angles of a triangle, different types of polygons, interior angles and sides of a convex polygon, the size and exterior angle of any convex polygon.Classifying triangles. Learn to categorize triangles as scalene, isosceles, equilateral, acute,
right, or obtuse.
. What I want to do in this video is talk about the two main ways that triangles are categorized. The first way is based on whether or not the triangle has equal sides, or at least a few equal sides. Then the other way is based on the measure of the angles of the triangle. So the first categorization right here, and all of these are based on whether or not the triangle has equal sides, is scalene. And a scalene triangle is a triangle where none of the sides are equal. So for example, if I have a triangle like this, where this side has length 3, this side has length 4, and this side has length 5, then this is going to be a scalene triangle. None of the sides have an equal length. Now an isosceles triangle is a triangle where at least two of the sides have equal lengths. So for example, this would be an isosceles triangle. Maybe this has length 3, this has length 3, and this... - Loss:
MultipleNegativesRankingLosswith these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 128per_device_eval_batch_size: 128learning_rate: 2e-05num_train_epochs: 1warmup_ratio: 0.05fp16: Trueload_best_model_at_end: Truebatch_sampler: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 128per_device_eval_batch_size: 128per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 2e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 1max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.05warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Truefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Trueignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportional
Training Logs
| Epoch | Step | Training Loss | eval-ir_cosine_ndcg@10 |
|---|---|---|---|
| 0.0007 | 1 | 0.1782 | - |
| 0.1999 | 297 | 0.1245 | 0.6279 |
| 0.3997 | 594 | 0.1224 | 0.6423 |
| 0.5996 | 891 | 0.1168 | 0.6493 |
| 0.7995 | 1188 | 0.1179 | 0.6541 |
| 0.9993 | 1485 | 0.1227 | 0.6529 |
Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 2.14.4
- Tokenizers: 0.21.1
Citation
BibTeX
Sentence Transformers
@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",
}
MultipleNegativesRankingLoss
@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}
}