Fixes for transcripts.

Author: mikeckennedy

transcripts/154-python-in-genomics.txt

@@ -798,7 +798,7 @@
 
 00:30:28 Looks really cool.
 
-00:30:29 It definitely, they have it lined up to, when you go to visit spacey.io, it really looks appealing and polished.
+00:30:29 It definitely, they have it lined up to, when you go to visit spaCy.io, it really looks appealing and polished.
 
 00:30:37 I was wondering why you didn't choose, what the difference or what made you choose spaCy over NLTK?
 

transcripts/154-python-in-genomics.vtt

@@ -1204,7 +1204,7 @@ So I highly recommend it.
 Looks really cool.
 
 00:30:29.300 --> 00:30:37.160
-It definitely, they have it lined up to, when you go to visit spacey.io, it really looks appealing and polished.
+It definitely, they have it lined up to, when you go to visit spaCy.io, it really looks appealing and polished.
 
 00:30:37.160 --> 00:30:42.440
 I was wondering why you didn't choose, what the difference or what made you choose spaCy over NLTK?

transcripts/202-software-biz.txt

@@ -1772,7 +1772,7 @@
 
 00:53:11 also the more like data science focused stuff that I've been working on also I'd
 
-00:53:16 always wanted like a spacey extension I haven't I haven't had time to look into
+00:53:16 always wanted like a spaCy extension I haven't I haven't had time to look into
 
 00:53:20 this but like we have an open issue on the tracker so if someone listens to this and
 
@@ -1782,11 +1782,11 @@
 
 00:53:33 but some of the people you're like oh somebody they I found this and they should
 
-00:53:37 know about it and I'll go ahead and throw spacey out there for you already so we can
+00:53:37 know about it and I'll go ahead and throw spaCy out there for you already so we can
 
-00:53:41 put spacey on the list right yeah of course one idea I've had just that we have to
+00:53:41 put spaCy on the list right yeah of course one idea I've had just that we have to
 
-00:53:45 mention like spacey wouldn't have been possible without siphon and lots of other
+00:53:45 mention like spaCy wouldn't have been possible without siphon and lots of other
 
 00:53:48 probably most sometimes people don't realize this but most Python packages you
 
@@ -1848,19 +1848,19 @@
 
 00:55:55 chance for a final call to action for folks I think we already did that around
 
-00:55:58 the building a successful software business so maybe just around spacey and
+00:55:58 the building a successful software business so maybe just around spaCy and
 
 00:56:02 prodigy tell people that are interested in NLP and what you guys are up to
 
 00:56:07 where can they check it out if you don't know about space yet you should check it
 
-00:56:10 out if you feel like spacey is not going to be useful to you then you don't like
+00:56:10 out if you feel like spaCy is not going to be useful to you then you don't like
 
 00:56:13 I'm not you know I don't want to go here and tell everyone they should use
 
 00:56:16 our software we're building very specific maybe slightly niche developer product
 
-00:56:20 developer tools but you know spacey I think we really we put a lot of effort
+00:56:20 developer tools but you know spaCy I think we really we put a lot of effort
 
 00:56:24 into our documentation we have a nice getting started guide if you're interested I'm hoping it should you know
 

transcripts/202-software-biz.vtt

@@ -8296,7 +8296,7 @@ also the more like data science focused
 stuff that I've been working on also I'd
 
 00:53:16.040 --> 00:53:18.960
-always wanted like a spacey extension I
+always wanted like a spaCy extension I
 
 00:53:18.960 --> 00:53:20.160
 haven't I haven't had time to look into
@@ -8329,16 +8329,16 @@ somebody they I found this and they should
 know about it and I'll go ahead and throw
 
 00:53:38.840 --> 00:53:41.180
-spacey out there for you already so we can
+spaCy out there for you already so we can
 
 00:53:41.180 --> 00:53:43.700
-put spacey on the list right yeah of course
+put spaCy on the list right yeah of course
 
 00:53:43.700 --> 00:53:45.380
 one idea I've had just that we have to
 
 00:53:45.380 --> 00:53:46.620
-mention like spacey wouldn't have been
+mention like spaCy wouldn't have been
 
 00:53:46.620 --> 00:53:48.980
 possible without siphon and lots of other
@@ -8530,7 +8530,7 @@ folks I think we already did that around
 the building a successful software
 
 00:56:00.340 --> 00:56:02.980
-business so maybe just around spacey and
+business so maybe just around spaCy and
 
 00:56:02.980 --> 00:56:04.500
 prodigy tell people that are interested
@@ -8545,7 +8545,7 @@ where can they check it out if you don't
 know about space yet you should check it
 
 00:56:10.040 --> 00:56:11.460
-out if you feel like spacey is not going
+out if you feel like spaCy is not going
 
 00:56:11.460 --> 00:56:13.460
 to be useful to you then you don't like
@@ -8563,7 +8563,7 @@ our software we're building very specific
 maybe slightly niche developer product
 
 00:56:20.620 --> 00:56:23.040
-developer tools but you know spacey I
+developer tools but you know spaCy I
 
 00:56:23.040 --> 00:56:24.260
 think we really we put a lot of effort

transcripts/465-the-ai-revolution-wont-be-monopolized.txt

@@ -266,13 +266,13 @@
 
 00:11:05 is how do you teach these things? Information and how do you get them to know things and,
 
-00:11:11 and so on. And, you know, for the spacey world, you have Prodigy and maybe give a shout out to
+00:11:11 and so on. And, you know, for the spaCy world, you have Prodigy and maybe give a shout out to
 
 00:11:15 Prodigy Teams. That's something you just are just announcing, right?
 
 00:11:19 Yeah. So that's currently in beta. It's something we've been working on. So the idea of Prodigy has
 
-00:11:23 always been, hey, you know, support a spacey, also other libraries. And how can we, yeah,
+00:11:23 always been, hey, you know, support a spaCy, also other libraries. And how can we, yeah,
 
 00:11:28 how can we make the training and data collection process more efficient or so efficient that companies
 
@@ -928,9 +928,9 @@
 
 00:30:22 So that's really models that we're trying to do one specific or some specific things.
 
-00:30:27 It's kind of what we distribute for spacey.
+00:30:27 It's kind of what we distribute for spaCy.
 
-00:30:30 There's also a lot of really cool community projects like size spacey for scientific biomedical
+00:30:30 There's also a lot of really cool community projects like size spaCy for scientific biomedical
 
 00:30:37 techs.
 

transcripts/465-the-ai-revolution-wont-be-monopolized.vtt

@@ -403,7 +403,7 @@ give you a chance to give a shout out to the other thing that you all have is,
 is how do you teach these things? Information and how do you get them to know things and,
 
 00:11:11.340 --> 00:11:15.880
-and so on. And, you know, for the spacey world, you have Prodigy and maybe give a shout out to
+and so on. And, you know, for the spaCy world, you have Prodigy and maybe give a shout out to
 
 00:11:15.880 --> 00:11:19.360
 Prodigy Teams. That's something you just are just announcing, right?
@@ -412,7 +412,7 @@ Prodigy Teams. That's something you just are just announcing, right?
 Yeah. So that's currently in beta. It's something we've been working on. So the idea of Prodigy has
 
 00:11:23.420 --> 00:11:28.820
-always been, hey, you know, support a spacey, also other libraries. And how can we, yeah,
+always been, hey, you know, support a spaCy, also other libraries. And how can we, yeah,
 
 00:11:28.820 --> 00:11:34.240
 how can we make the training and data collection process more efficient or so efficient that companies
@@ -1396,10 +1396,10 @@ So one of them is what I've called task specific models.
 So that's really models that we're trying to do one specific or some specific things.
 
 00:30:27.680 --> 00:30:30.100
-It's kind of what we distribute for spacey.
+It's kind of what we distribute for spaCy.
 
 00:30:30.100 --> 00:30:37.460
-There's also a lot of really cool community projects like size spacey for scientific biomedical
+There's also a lot of really cool community projects like size spaCy for scientific biomedical
 
 00:30:37.460 --> 00:30:38.280
 techs.

transcripts/477-spacy-nlp-v2.txt

@@ -798,7 +798,7 @@
 
 00:19:53 Well, I'll link that as well.
 
-00:19:56 Now, let's dive into the whole NLP and spacey side of things.
+00:19:56 Now, let's dive into the whole NLP and spaCy side of things.
 
 00:20:00 I had Ines from Explosion on just back a couple months ago in June.
 
@@ -808,13 +808,13 @@
 
 00:20:13 So two to three months ago.
 
-00:20:15 Anyway, we talked more about LLMs, not so much spacey, even though she's behind it.
+00:20:15 Anyway, we talked more about LLMs, not so much spaCy, even though she's behind it.
 
-00:20:20 So give people a sense of what is spacey.
+00:20:20 So give people a sense of what is spaCy.
 
 00:20:23 We just talked about Scikit-Learn and the types of problems it solves.
 
-00:20:26 What about spacey?
+00:20:26 What about spaCy?
 
 00:20:28 There's a couple of stories that could be told about it.
 
@@ -834,9 +834,9 @@
 
 00:20:56 And it was definitely kind of useful, but it wasn't necessarily a coherent pipeline.
 
-00:20:59 And one way to, I think, historically describe spacey, it was like a very honest, good attempt to make a pipeline for all these different NLP components that kind of click together.
+00:20:59 And one way to, I think, historically describe spaCy, it was like a very honest, good attempt to make a pipeline for all these different NLP components that kind of click together.
 
-00:21:09 And the first component inside of spacey that made it popular was basically a tokenizer.
+00:21:09 And the first component inside of spaCy that made it popular was basically a tokenizer.
 
 00:21:15 Something I can take text and split it up into separate words.
 
@@ -870,7 +870,7 @@
 
 00:21:46 Because then if you like went back when I worked at the company, I used to work at Explosion just for context.
 
-00:21:51 They would emphasize like the way you spell spacey is not with a capital S, it's with a capital C.
+00:21:51 They would emphasize like the way you spell spaCy is not with a capital S, it's with a capital C.
 
 00:21:55 It's like when you go and put what is your location and your social media.
 
@@ -932,7 +932,7 @@
 
 00:23:01 This is going to happen.
 
-00:23:02 But anyway, but back to spacey, I suppose.
+00:23:02 But anyway, but back to spaCy, I suppose.
 
 00:23:04 Like this is sort of the origin story.
 
@@ -1702,13 +1702,13 @@
 
 00:41:11 This is the thing that people don't always recognize.
 
-00:41:12 But the way that spacey is made, if you're from scikit-learn, this sounds a bit surprising
+00:41:12 But the way that spaCy is made, if you're from scikit-learn, this sounds a bit surprising
 
 00:41:17 because in scikit-learn land, you are typically used to the fact that you do batching and stuff
 
 00:41:21 that's vectorized and numpy and that's sort of the way you would do it.
 
-00:41:23 But spacey actually has a small preference to using generators.
+00:41:23 But spaCy actually has a small preference to using generators.
 
 00:41:27 And the whole thinking is that in natural language problems, you are typically dealing
 
@@ -1762,7 +1762,7 @@
 
 00:42:46 that.
 
-00:42:46 But my spacey habit would always be do the generator thing.
+00:42:46 But my spaCy habit would always be do the generator thing.
 
 00:42:49 Yeah.
 
@@ -1782,7 +1782,7 @@
 
 00:43:12 nested data structures as well.
 
-00:43:13 So that's the first thing that I usually end up doing when I'm doing something with spacey.
+00:43:13 So that's the first thing that I usually end up doing when I'm doing something with spaCy.
 
 00:43:17 Just get it into a generator.
 
@@ -2330,7 +2330,7 @@
 
 00:57:17 A trick that I always like to use in terms of what examples should I annotate first?
 
-00:57:22 At some point, you got to imagine I have some sort of spacey model.
+00:57:22 At some point, you got to imagine I have some sort of spaCy model.
 
 00:57:25 Maybe it has like 200 data points of labels.
 
@@ -2340,7 +2340,7 @@
 
 00:57:31 When those two models disagree, something interesting is usually happening.
 
-00:57:35 Because the LLM model is pretty good and the spacey model is pretty good.
+00:57:35 Because the LLM model is pretty good and the spaCy model is pretty good.
 
 00:57:38 But when they disagree, then I'm probably dealing with either a model that can be improved or data point that's just kind of tricky or something like that.