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[Assistant] You know what? Forget it. Cause you gotta see it to appreciate it. Yea, we'll do it tomorrow. That's too bad because it was a great moment for the sport of baseball.


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[Assistant] Alright, so what we saw was a young kid in the tennis, his name was Blake Grice. His grandfather had on his bucket list, he wanted to meet Clayton Kershaw. Clayton Kershaw was the guy that he idolized as a player.


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[Assistant] He sadly passed away with cancer and never had the opportunity to meet him.


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[Assistant] Well, somebody heard this story and said we gotta get this kid in the news conference and perhaps Clayton will have a moment with him.


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[Assistant] And the young man, you could tell he was nervous as hell. I think he was wearing his baseball uniform from his little league team.


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[Assistant] He walked up to Clayton Kershaw and told him the story.


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[Assistant] And after hearing the story, Clayton decided that he was gonna help make the day special for the young man. And hopefully we have that clip ready to roll now. Steve, you can take that. Thanks for coming.


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[Assistant] We'll run that back tomorrow. We'll see it tomorrow. Trust me, it was a great moment. Uh, Clayton Kershaw took, went the extra mile to make a kid's day. And, um, it's too bad we couldn't see it today, but we'll bring it out tomorrow.


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[Assistant] And with that, the Ultimate Cleveland Sports Show says goodbye for 22 hours. We are back tomorrow at 11 o'clock. You want to tease what's on the show tomorrow, McNuggets?


[User] Please transcribe this text

[Assistant] Alright, very good. Tune in. That's tomorrow, 22 hours from now. We'll see you then. Have a great rest of your Wednesday. Shout out to the Honolulu Browns backers. Oh, and it looks like vMix froze again.


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[Assistant] If there be a lesson I took from Spain, it is that my court should outshine all others. Just as England should outshine all nations.


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[Assistant] Never make defense or apology before you be accused. Advice fit for statesmen, courtiers, and husbands.


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[Assistant] Do you know what they say of me, Richard? In these pamphlets.


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[Assistant] That I conspire for the propagation of potpourri in my kingdoms.


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[Assistant] Will the people shake off their current sickness and better affection be to their king?


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[Assistant] Is it from Uncle Richard? Father, what news of the war?


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[Assistant] A cow hand that goes in for breaking horses. By the time he is your size, he's been hittin' the seat of the pants so many times he ain't got any brains anymore. Just a carneal oak meal in his head.


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[Assistant] Mr. Unsmiling Jones. A man with a certain standing in the community.


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[Assistant] Look Melody, if we aim to get across that line, don't you...


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[Assistant] Stick em up. You surely took long enough about it.


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[Assistant] You can look for the United States gallery to arrive in about three days. Because there was some mail on that stage.


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[Assistant] Looks like a fella can stand just so much of that pounding, and then...


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[Assistant] You needn't keep watching my gun. You couldn't stop it.


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[Assistant] There's only about three men in these parts that I can't slap leather with. One of them's Monty Jarrod. But you ain't him. No? No. You had me fooled for a time, but...


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[Assistant] Nobody else have to know, you know. Just you and me. 50-50.


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[Assistant] Is that the way you always draw? Yeah, numbskull.


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[Assistant] But why should I shoot the man, a perfect stranger? Why should I shoot? Oh, now wait a minute. Both of you, you can't hang it on me.


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[Assistant] Not having sex for that long though, does it just drive you crazy?


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[Assistant] That's dope. You have like, uh, like where do you see yourself in the rap game and like what you're doing right now? It's like, what have you been working on and stuff like that?


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[Assistant] Do you think there's a fine line between glorifying something and


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[Assistant] I feel like you know that. But hey, that lifestyle appeals to certain people. You drive around really fast, you don't stop at the stop signs, you pull people out of their cars. It's a lifestyle.


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[Assistant] Unless it was taken by somebody. Only in jest.


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[Assistant] But, we're all super excited for him. Uhm, Zach and I have been together for seven years in New Orleans, and man, that guy's gotten better every year, and just, uh, I know Blake's super excited, and uhm, it's a long time coming for him, so, congratulations Zach to you guys.


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[Assistant] No, it was really cool. Uhm, look, Zach's one of my best friends. He was in my wedding. Uhm, Zach's friendship means a lot to me outside of football. Uhm, Blake, Zach and I are all really close and I think that's important. I think this organization values keeping a battery together that


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[Assistant] You know, has shown works. Uhm, you know, we've made a lot of big kicks together and obviously I hope that continues for, you know, the duration of that.


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[Assistant] Right now I'm trying to get back to my old ways and, uh, kind of find a rhythm and, you know, I didn't have a great day the other day, so I thought today was really important to bounce back and hit the ball well, so.


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[Assistant] Uhm, overall I was happy with today and, you know, right now I'm in the business of trying to stack some good days together and, uhm, like I said, get back to what I was doing in the past.


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[Assistant] Well, I'll say this first, Blake is extremely talented. You guys see it. Uhm, we see it every day. He works hard. He's got, he's got the marketing to him and hit the ball away in this league. Uhm, so look.


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[Assistant] You guys act like I don't compete with everybody every year, right? Um, and that sounds cliche, but you know, I, the first five years I was here, we didn't have competition and I never looked at it that way. You know, there's right now there's about 64 other guys in the league that I'm competing with and that's just part of the business. Um, obviously it's a little more.


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[Assistant] There's a lot more to the game than kicking six field goals in a row, right? It's easy to get a rhythm when you're snap kick, snap kick, snap kick. Uhm, but look in a, on game day, I'm sitting on the sidelines for three hours.


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[Assistant] And you might kick a field goal in the first quarter and the fourth quarter and that's it. So I like that coach has us doing that. I hope he continues to do it. I think it, um.


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[Assistant] You know, it kind of puts pressure on you that you don't get the feel during training camp. And I think that's really important. And so, um, yeah, I mean, that was a fun kick to make and hopefully we string a few more together.


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[Assistant] Yeah, look, I feel great. I felt great last year. Um, mentally it was tough last year. I felt like I wasn't in the right headspace coming out of 2021. Um, and I was a deer in the headlights. I felt like a rookie again. So.


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[Assistant] Physically, I felt great last year. Physically, I feel good this year. And really, it's a matter of getting back to that, you know, mental game that I've always been so strong in. Uhm, I really take pride in.


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[Assistant] So I've missed three, uhm, missed a 47 early on, extra point, and a 42 the other day. And I believe I was 27 of, I was 26 of 29 going into the day, I think. So somewhere in that area. I know I've missed three kicks. Those are the ones I remember, unfortunately.


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[Assistant] Yeah, it's different, and I give Luke credit, he's a good holder. Me missing with him has nothing to do with him. The difference is, it's just new. And so, I got a little work with him in OTAs and took us a little bit to kind of gel, but uhm, he's got


[User] Please transcribe this text

[Assistant] He's got good hands, he gets the ball down quick. So, he's moving in the right direction, you know, getting comfortable with Zach and I, Groupie and Blake Gilligan. Uhm, look,


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[Assistant] You know, me being comfortable is everything. And uhm, that's something I'm trying to work with him right now. And you know, like I said, me missing with him is not a detriment to him. It's me trying to get used to him again.


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[Assistant] Look, that's not for me to decide. Uhm, I think Blake's really talented, like I said. So uhm, my job right now is to take it one kick at a time, make my kicks and, you know.


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[Assistant] Kind of see how, how it plays out with him. You know, obviously he wouldn't be here if he wasn't talented. Um, and he's shown it on the field. So, you know, that's not really my, my area to, uh, to judge. Uh, my job is to come in here and make kicks and, um, take that into the season.


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[Assistant] That's a, that's a tough question to answer.


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[Assistant] Like I said, there's more to it than just kicking six field goals in a row. You know, like today we, we end with a 62 yarder, 62 yarder and that's great. You know, I was getting a rhythm there, right? So.


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[Assistant] Yeah, that's going to be interesting. As far as my approach, no. I still have to be able to dial up a touchback when it's time. Uhm, but I think that's what preseason is for. I know the special teams coaches talk a little bit. They try to figure out, kind of, get the gist of what they're going to do.


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[Assistant] As far as the way we're going to approach it, see how that goes next week really.


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[Assistant] Uhm, range is important, leg strength is important. Right, you gotta be able to show that if it's needed you can hit it. Which I believe I've shown. Uhm.


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[Assistant] You know, I take a lot of pride in my leg strength. Uhm, so.


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[Assistant] I think that I've shown that leg strength isn't an issue when it comes to field goals, but right now I'm trying to work on my consistency inside 55. So the long ones are fun, but look, the ones, the average kicks every day are the ones that make you money. So my goal is to make those kicks inside 55 and you know, it's icing on the cake when you make the long ones.


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[Assistant] Well, I mean, it's all strategic, right? No one's gonna go out and hit a 60 yarder in the second quarter on fourth and four.


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[Assistant] Of a three point game, right? So there's circumstances. I mean, shoot, you look at a guy like Brett Maher who set the record for most 60 yarders in the season. Guy's got a fiery leg. Greg's airline's hit a lot in his career. So you gotta be able to show that you can do it. Uhm, but look, some guys who,


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[Assistant] You know, I've, I've already had, shoot, I guess, four attempts in seven years. So, and some guys have had one in 15, so.


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[Assistant] You know, I don't think it has anything to do with kickers not being capable of it. I just think there's a time and place for it and some teams have it and some teams don't.


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[Assistant] I'm alone. Hello, Mrs. Baker. That young lady in your apartment.


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[Assistant] Timmy Shukolsky is kind of a mystic, a modern master.


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[Assistant] I love our bath time silliness. The way we splash and play.


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[Assistant] I love to lift you wayyyy up high, so you feel really tall.


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[Assistant] I love to share the magic of the shining skies above. Like when we're touching noses.


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[Assistant] So snuggle safely in my arms. Our day is nearly done.


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[Assistant] I thought about it for a second. I guess that should piss me off, but I don't really care. I'm sorry, I should have asked. It's okay doc, you didn't mean any harm. No, I promise you that I did not mean you any harm. It's just that I find your species fascinating.


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[Assistant] Your family's divergence from humanity is much more interesting, magical almost. Bibbidi-bobbidi-boo, I mumbled. He was just like Bella with all the magic garbage. Carlisle laughed another weary laugh. Then we heard Edward's voice inside the house, and we both paused to listen. I'll be right back, Bella. I want to speak with Carlisle for a moment. Actually, Rosalie, would you mind accompanying me?


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[Assistant] Edward sounded different. There was a little life in his dead voice, a spark of something. Not hope exactly, but maybe the desire to hope. What is it, Edward? Bella asked hoarsely. Nothing you need to worry about, love. It will just take a second. Please, Rose. Esme, Rosalie called. Can you mind Bella for me?


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[Assistant] I heard the whisper of wind as Esme flitted down the stairs. Of course, she said. Carlisle shifted, twisting to look expectantly at the door.


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[Assistant] Edward was through the door first, with Rosalie right on his heels. His face was, like his voice, no longer dead. He seemed intensely focused. Rosalie looked suspicious. Edward shut the door behind her. Carlisle, he murmured. What is it, Edward? Perhaps we've been going about this the wrong way. I was listening to you and Jacob just now, and when you were speaking of what the fetus wants, Jacob had an interesting thought.


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[Assistant] Me? What had I thought? Besides my obvious hatred for the thing. At least I wasn't alone in that.


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[Assistant] I could tell that Edward had a difficult time using a term as mild as fetus. We haven't actually addressed that angle, Edward went on. We've been trying to get Bella what she needs.


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[Assistant] And her body is accepting it about as well as one of ours would. Perhaps we should address the needs of the fetus first. Maybe if we can satisfy it, we'll be able to help her more effectively. I'm not following you, Edward, Carlisle said. Think about it, Carlisle. If that creature is more vampire than human, can't you guess what it craves? What it's not getting? Jacob did. I did?


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[Assistant] Hey, you won't need your cart tonight, Mr. Reinholds. You're coming with us.


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[Assistant] What is this, a kidnapping? Nearly a little social visit. This way, please.


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[Assistant] Then I shall go alone. I do not sleep with any woman, especially in public.


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[Assistant] Of course, I will admit I'm one of the greatest little chemists that ever balanced a test tube. Sergio. Where'd you go last night? Another merry-go-round? I'm still going around in circles. Boy, you ought to see what I found for myself. That cutest little refugee. From what? Well, now you guess. Now I bet she has the cutest little accent. You're not kidding. Where'd you meet her? Some bar?


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[Assistant] Say listen, Tom, why don't you cut out all this foolishness and come down to Earth? Someday you're going to get yourself into a jam you won't be able to get out of. Go ahead, go ahead, lecture me. I can take it. Oh, I'm not trying to tell you what to do, but...


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[Assistant] Everybody can't be as lucky as you, Bob. Oh, by the way, have you and sis set the date yet? Can't be laughing at war now.


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[Assistant] Yes. Yes, he's here, but a little worse for wear and tear. But he'll probably survive.


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[Assistant] What you say is true. The situation is serious.


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[Assistant] What? Not even just a little touch on that beautiful face of yours?


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[Assistant] Herr Gimmler, was it wise to let him go? I never make mistakes.


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[Assistant] Well. Oh, hello Bob. Say, what's the idea of showing me up by getting to work in the middle of the night? I was just making up for yesterday morning. That a boy. What about the refugee? Oh, skip it. Wrong number.


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[Assistant] So that process check on 311. Did you take it?


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[Assistant] Well, what would I do with it? Nothing, I guess. Well, it's not important. I can work it out again.


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[Assistant] Maybe he's been unlucky in love. You know he's got a new girl, a refugee.


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[Assistant] Good morning, Bob. Good morning, Mr. Baskett. Hi, Curtis. Good morning, Norton. Pull up a chair and sit down, Bob. Thank you.


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[Assistant] I didn't notice anything unusual. He was always a bit grumpy in the morning.


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[Assistant] He did mention something about a dinner engagement last night.


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[Assistant] Oh, he didn't say. Did he say with whom? Oh, he didn't tell me, but I'm sure he'd be glad to tell you himself.


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[Assistant] I looked all over the floor before I went to Prescott's office. Maybe you're getting blind in your old age. Somebody put those notations there while I was gone. You, you mean I did it? Yes. Have it your way. Look, why didn't you tell me about the refugee? I told you before to skip it. Where did you go last night? Around, places. What places, for instance? That is absolutely none of your business.


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[Assistant] Oh, I don't know. Maybe it is my business. The man who was assigned to protect you last night was murdered.


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[Assistant] Curtis Hatt is both shattered. My man reported in, alive.


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[Assistant] But we can't duck it, Tom. Don't you see this is bigger than you or I? It means trouble for Uncle Sam. Oh Tom, we're pals and I, I hate this just as much as you do. But don't you see how important all this is? Now what about this girl, this refugee you met? Where did you go? All right, I'll tell you.


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[Assistant] Oh, hello, Bob. Oh, you're going out? Well, I had to get some cigarettes. Oh, wait a minute. I have plenty of cigarettes. So let's go in and sit down. I, I'd like to talk to you. All right, Bob.


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[Assistant] Why didn't you tell me all that happened at the Alpine Grill? I told you everything. What do you mean? Why didn't you tell me those birds offered you a hundred thousand for a little like treason? That's not so.


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[Assistant] Boy, I just stopped in to go over some figures with Tom.


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[Assistant] Oh, come on, be yourself. Let's, let's sit down and talk this thing over sensibly.


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[Assistant] I guess that's about all, Mr. Curtis. She offered me a hundred thousand dollars and told me she'd contact me again. Oh, uh, she warned me to keep my mouth shut when she left. And believe me, it didn't take me long to get out of there myself.


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[Assistant] I see. Did you bring the formula? Why, yes. May I have it, please? What about the hundred thousand? All cash, now.


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[Assistant] Pardon me, Mr. Martin. But you will not mind, I'm sure, if I take the precaution of not giving the money to you yet.


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[Assistant] Test? This formula has to be a test. Sure, by all means. Go ahead, test it yourself. Someone will come and make a test. After that, you will be paid. Who'll make the test with me? One of the most marvelous scientists in the whole world. Professor Carl Schmidt. Professor Schmidt? Why, I'll be very happy to work with the professor.


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[Assistant] What do you mean by that, Norton? Tom has a bottle of 3B11. If he mixes it the way I think he will, you'll see what I mean.


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[Assistant] I know. What a glorious day for the fatherland. Heil Hitler!


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[Assistant] In a moment later, she comes back with a glass of ginger ale, and she goes, what's that? I said, the gentleman in that other seat said I was to give you this, and that's chocolate.


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[Assistant] of Sport America, which is thou shall not speak out unless thou is playing at a championship level.


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[Assistant] They had a very traditional home and religion was important but it was mixed with social obligation that this should be a better country.


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[Assistant] But he'd have a hard time coming down and he finally went to Wooden and asked for permission.


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[Assistant] To smoke some dope, because it helped bring him down. And finally, it wouldn't say, yes, but don't tell your teammates.


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[Assistant] He was a great athlete, body, quickness, eyesight, vision, coordination. He has all these great assets and he has your grandmother's feet.


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[Assistant] Everybody's watching and everybody's trying to look like him. What Bill is and what most of the culture of Oregon was at that time is a pretty natural thing.


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[Assistant] It was like this miracle that happened in Portland.


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[Assistant] That they didn't really understand, had come there, and in a relatively short time they were the champions.


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[Assistant] It's about a young man not accepting entirely his own responsibility.


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[Assistant] Bill was angry. He was depressed. They'd taken away what must have seemed like his life. And the consequences, instead of being a championship, had all been negative.


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[Assistant] It was love of game and a sense of purity that


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[Assistant] Carried him almost in spite of the enormous vulnerabilities of that body.


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[Assistant] In his personal life he was the explorer and the counterculture guy. And in his basketball life, he was the ultimate orthodox person.


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[Assistant] He seemed to have so many views off and counter to mainstream U.S. kind of thinking. And so he thought, wait, what's this guy all about? Why didn't he just play basketball, you know, and just shut up?


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[Assistant] I'm talking to Bill and I said, what are you up to now? I gotta go to the hospital and get some bone spurs removed from my ankle. Holy smoker, they didn't announce that one to the


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[Assistant] The franchise would have thrived in San Diego if Bill had had 10 healthy seasons, uh, down there. But without that, it was such a disappointment.


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[Assistant] Good morning, darling. Good morning, honey. Looks like your walk was successful.


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[Assistant] Maybe he could find a quotation from Aristotle.


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[Assistant] You're the nicest guy in the world. Gentile, polite.


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[Assistant] What do you see in Donald Trump? To me, he's not just the most vulgar politician we've ever had. To me, he's the most vulgar human being. Vulgar. Why do you?


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[Assistant] She said one stupid thing, and that justifies...


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[Assistant] Keeping them out of office. And that would include the massive amount of lying that I hear from your side. Uh, and the justification.


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[Assistant] Is that true, am I, is that the real truth? You think they're feckless and they're fuzzy-headed and they will sell this country out.


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[Assistant] And you think that's, it's all because she said Blasket of Deplorables. No, I think it's been building up. It came out today that Mike Pence was using a private server.


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[Assistant] You know. Okay, but you used to be the free trade people. You, I mean... Not always, you know. Yeah, right. 1890.


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[Assistant] William McKinley, Calvin Coolidge, Abraham Lincoln. Okay, I'm just saying, you turned on a dime on, on so many of the, used to be the budget responsibility people and he just proposed a budget where no entitlement cuts, uh, defense buildup.


[User] Please transcribe this text

[Assistant] Uh, infrastructure spending of a trillion dollars and massive tax cuts for both the rich people and the middle class people.


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[Assistant] What happened to the budget responsibility? Well, I think you do have to, you know,


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[Assistant] But let me, before we run out of time, let me ask about Russia. Because that's the big story on everyone's lips. I'm sure you would say, nothing to see here, because that's the Republican line. But then why all the lying?


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[Assistant] Why did the National Security Advisor lie? Why did we just catch Jeff Sessions?


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[Assistant] I'm just asking, Nancy Pelosi? Yes, she said she never met with this guy, and now we have a picture of him. Well, let's have an investigation of her, because I'm sure that... But the Russians didn't hack Nancy Pelosi's election. They hacked our election, all of our intelligence agencies said.


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[Assistant] But this is the assessment of the intelligence agencies. You have no respect for them? No, no, that's not the assessment. The intelligence agencies- What was it?


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[Assistant] That's what they said. That's what the intelligence agency said.


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[Assistant] Vladimir Putin, before Trump came along, had a 12% approval rating among Republicans. Now it's 32.


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[Assistant] Then I have done you a service, and I am glad of it.


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[Assistant] It's true. I do have land there. I think I have.


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[Assistant] Ah, they don't get rid of Lucy Diver that easy. I saw blind it was mistaken identity. And they couldn't find anything on me or on the road. For you had gone off with a bundle and kept it for yourself. So there was no case to answer. But I think you got a case to answer, madam.


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[Assistant] Bless you, no. You helped to get me off. You was meant to do what you did. I'm very pleased to meet you, Mrs. Molly. My governess here never stops singing your praises. I was sorry to hear you haven't been well. I had a turn like that after I was taken up that day you saw me. I was in such a state I couldn't go out for three weeks solid.


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[Assistant] I have to say it because it's true. And if you can learn more, I can teach you. So what do you say? Give it a try? If you don't like it, we can kiss and part. But say yes, because I do need a good steady pal. It's twice the fun and half the danger. I can promise you that.


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[Assistant] I've never forgotten you, Mol. I think you were my first true love. I know I was yours.


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[Assistant] I heard every word you and that book-taught farmer said. You told you probably prayed that I'd be struck down so as I could try out your crazy schemes. You're a Jezebel! That's what you are, a Jezebel!


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[Assistant] I'm just glad that I've got more space on the table now to be able to lean forward.


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[Assistant] Oh, do you know at that point, I'm just happy to pass it on to someone else who deserves their own chance. Yeah, absolutely.


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[Assistant] Well, I mean, it certainly used to be the way that, um, if you think global, I think more than 1.5 million people.


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[Assistant] Listen, I had a hit tweet. Did you? I had a hit tweet yesterday, and what I've learned from that is people have opinions. So I basically, I tweeted the uhm, when the Shields music hit, it was, WDB had posted the Shields, you know, whatever, Sierra, Hotel, all of that stuff.


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[Assistant] And it was speaking to that moment, that match, this, this.


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[Assistant] So many replies, and in those replies, obviously you have the majority of them being like, yeah, I actually thought this too, I was the same, oh my god, it's amazing.


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[Assistant] Being this way against each other. Yeah. So they'll always be there. Oh, of course, yeah.


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[Assistant] All of a sudden these people come out. It's fandom, in general, I think, and especially in the rest of the- Of the Ghostbusters fandom. Well, I mean, that's because there are only six of you. In the fandom!


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[Assistant] It's a shame that, like, who's gonna be allowed to be there and people will cross at Ricky Starks. It's like, some people are gonna hate fun. What we got on this episode of Roar, though, and what you had with Triple H coming out and-


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[Assistant] Ah, someone's come down to be technical. In the meantime, I'm going to finish my sentence so that I can- It wasn't me!


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[Assistant] So with this, well now an extra two minutes in the podcast, we're not necessarily talking about exactly what's going on in the podcast, but that's because this weekend has kind of been, it doesn't happen without


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[Assistant] I think the vast majority of people though, this is the thing, the vast majority of people are...


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[Assistant] I like a big stage. I don't like a wall of screens all the time. Like I actually, I'm beloved of the sort of Smackdown Fist or the, or the, um, the Raw with the, the...


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[Assistant] Uh, scaffolding kind of thing. I liked all of those. Uhm.


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[Assistant] Basically saying the same thing as you, I love it when a set is this small because I can see all of you, you're all here, we're packing this place out. It's nice to see that they can do that.


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[Assistant] Well, I think WrestleMania 17 had a couple of little matches in there as well. So, like-


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[Assistant] Sure, but that was only one night of WrestleMania, so if you're looking at it as a collective, I don't mean to get into this debate, I'm just saying that like, you look at it as a whole if you want to, every show has a down moment, you're not seeing, I said I was the one on the livestream saying I didn't enjoy that match.


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[Assistant] And I got a bit tired during the main event of night one. But either way, the way night two was, it was one of the greatest wrestlers of all time. And the reason I made a point of saying that was because Triple H wants to come out and have a victory lap.


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[Assistant] It's very simply that he's walked the walk. Like he's someone who's done it and we've followed it and not only this little lovely video package that they made for him, uhm, but the one that came before.


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[Assistant] I think there was something about the acknowledgement of that again, which points to this new era of being like, yeah, it's all part of it. We're not gonna pretend.


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[Assistant] We built Cody to this level. Cody built Cody to this level. And so when, which is a very different approach, uhm, and when


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[Assistant] Have someone like Cody, no amount of schmaltz or babyfacery, blue-eyed babyfacery can really affect him as much as it would with a Lex Luger or even a Roman Reigns because he...


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[Assistant] Uniquely in the wrestling industry has earned it, has proven it. You deserve it are weak words for Cody Rhodes, right? Like, it's, it's, so, so when he's like, yes, Samantha, say, say my name again.


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[Assistant] And she's like, I've got you. Because she knows, not only does she have an outstanding weekend, one of the MVPs of the whole thing, she knows, and we know, that he's waited his whole life, and in many ways his father's whole life, to hear these words.


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[Assistant] Even so, now that I know it's happened, and I was sick and nervous going into that match, like I genuinely was like, I want to know the result of this match so that I can enjoy this match, so when I then went and watched it the second time, literally the second- I woke up on Monday and went, alright, play, and I went back and watched the whole show again. I was so excited. It-


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[Assistant] Seeing the full picture of it, and you know, then seeing him having a little cuddle with, with Heyman, knowing that when he gave Heyman the hug at the, all the fame, he's wearing the watch that Cody gave him, it's a special, special rain.


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[Assistant] We had people say like, you know, he has to have the belt because the story, otherwise he's not the head of the table. You have failed to understand storytelling, my friend, because now we are in for an interesting story.


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[Assistant] This is, this is it, and this is what, so, like, you know, we, uh, uh,


[User] Please transcribe this text

[Assistant] Anyone who's been around my personal channel knows that my life has been this one long realm of like, how do I just fake- I'm trying to navigate my 20s and now my 30s and figure out how I'm gonna contribute and what I'm gonna do with my life and all that stuff.


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[Assistant] Say something like that, and seeing, you know, the very authentic, real reaction the fans have to Cody, his coworkers have, the way they react to each other, it gives me life. And then I think, oh, what do you do next? That energises me.


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[Assistant] I wanna get my hands in. I wanna play in that world. Because like, the idea of


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[Assistant] This documentary that's coming out on Wednesday is going to be such a fascinating thing to watch because when you do have a moment where you're like...


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[Assistant] Well we have these plans, and we'll talk about it in a second because there's something with the rock here, but when you have a plan, and the plan goes wrong, you can either look at that as an oh no, what am I gonna do, or you can look at it and go,


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[Assistant] What am I gonna do? What's next? What's next? We've mentioned the West Wing already. The first episode of the West Wing ends with him saying what's next. Those were the words running through my mind when I rewatched the ending of WrestleMania Night 2 yesterday. What's next? It's such a joyous problem to have. Yeah, it's a great, yeah.


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[Assistant] That was my line for commentary. I'm genuinely flabbergasted that Michael Cole, I mean, maybe he just was feeling his emotions, but the line is that, and then the American dream has come true for Cody Rhodes. Like that's the line that we're in my head. Like it's, it's a brilliant, put it on a t-shirt, I'll buy it.


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[Assistant] It is, and yet, uh, in that delicious way, I couldn't look away.


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[Assistant] I've also seen the theory that it's the SD card with the footage from All In. I've seen a theory that it was just a drug push, like the Rocks just selling some drugs. I don't know. And so I've got two things on this whole angle, as it were.


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[Assistant] The first one, I'll go with what was in the hand. I am fuming. I don't know what that is. That's made me really cross because I wanted to know what that is. And that's why this worked. I want to tune in next week. That's what writing is. It makes me want to keep watching to find out, you know, I'll watch on Friday. Will Cody be there? Will he say something? I'll watch, you know, where's this going to go? Am I going to get a response? And in, in times gone by, um,


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[Assistant] That, that not only was an under delivery in the case of the anonymous general manager, it was a straight up.


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[Assistant] Booking weeks a week, dunno, we'll figure that out later. And then you didn't, it just went away. It's the gold egg. We'll figure this out later. It is deeply irritating to not be able to trust in your, in the show. I think they have earned a level of goodwill and a level of trust over the course of this pivot and storyline and then specifically the end. One of the reasons I was so nervous about the


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[Assistant] Reward me for my investment. You know what I mean? There's a give and take.


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[Assistant] There's the, the, the swerve for the sake of swerving. There's the, oh no, they've figured me out, shall we change course so that they can, you never know what's gonna happen. Sometimes it's important to know what's gonna happen. Sometimes as an audience you wanna be rewarded with that catharsis. I think that they have plotted and delivered a good story over the past few months that makes me believe you'll do something with this. And I appreciate that.


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[Assistant] Yeah, in the same way that like, you know, Cody has mentioned what R.O.P. whispers to him, but said, I'll let the R.O.P. say it kind of thing. That's somewhere in there as well, if you want to play with that. Do you remember when he came out and said, I don't want to come for that mania?


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[Assistant] So then the other thing that came out of this was the belt situation, which the chance of this is awkward or hilarious, but like the, uh, specifically the, um,


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[Assistant] Handing over of the bell and who looked. When Cody came out.


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[Assistant] I wasn't sure if Triple H was there to do a new belt thing for a start, because that belt for me is Roman Reigns now. It's like, not even just because they gave him the golden one to be like, congratulations, you got both these belts, you've had the parades, you can have a golden championship. But specifically because


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[Assistant] That belt symbolizes an era, and I think that era is now over.


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[Assistant] I'm now almost like, well leave it be because there's something about the belt and it's entirely possible that what I'm about to say is-


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[Assistant] Big, cumbersome, burdensome, ill fitting. He came out and his suit was off. He couldn't figure out how to hold it.


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[Assistant] It was sort of tied up and he had it over and it looked stiff. I don't know if it was just a new belt. Becky wrote in her book that I've read, I don't know if I've mentioned it, that when she won the SmackDown Women's Championship, the belt wasn't loose or anything like that, so it feels difficult to hold.


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[Assistant] Uhm, John Cena mentioned I found this hilarious downer on the Pat McAfee show.


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[Assistant] But I saw that in Cody, like at one point the strap was folded over and like it didn't look right on him.


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[Assistant] And then when the rock held it, it matched his vest, it was the perfect size for his peck, it looked natural on him. And I was like...


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[Assistant] Oh, that's, that's an interesting visual. Now, the idea that I'm putting out there, that I'm putting in this story is Cody's never done this before. You have, it's all well and good winning the belt.


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[Assistant] Now you have to be the champion. Now you have to write your own story. Now you've got to meet the moment.


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[Assistant] How are you gonna do that? And that's not me saying I don't think Cody can, because obviously I'm the biggest crybaby in the world.


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[Assistant] But it was a really interesting visual to have.


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[Assistant] Someone of the level of the rock hold that belt and show him the way it should be held. I just thought that was fascinating.


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[Assistant] Yeah, I mean, I will say that they're not displaying those belts every week in the NFL. Like, you know, when you give next year's Super Bowls winner a belt, it can be the new one. Like, there's, you know, I'm thinking this situation with Big Eagle.


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[Assistant] With the blue, but you just take that logo, the WF logo, and put it there. Like, if you want to, you can still have a big logo. Yeah, like, I think there's a way to do, I, uhm, we had the whole belt discourse and the logos, I think, I think the AW belt with the massive logo in the middle of it looks quite classy. So I think, and I know you don't, uhm, I,


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[Assistant] I think you can put a logo in the middle of it and still make it look classy. I think it's the pop of colour. I think the, the, the World Heavyweight title, the Earth is on an axis, and I don't know why, just make it centred and neat. Uhm, but like, you can, you can have both. So...


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[Assistant] But now that I've seen this, I almost don't want them to change it. Because it was such an interesting visual. I'd like to see Cody one day.


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[Assistant] That's because I wanted to see what his schedule actually is. He's got Fast X coming out, uh, in 2025. He's got Moana coming out in 2025. Those need to be filmed. He's also got an A24 movie with Benny Zafdie being filmed. These things are gonna take him through, I believe his schedule is till the start of August.


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[Assistant] Follow the flow of the river, and see where you're gonna end up, because...


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[Assistant] Cody Punk is a great idea. Cody Sammy is a great idea. There are so many great ideas now. See, let's see where we end up.


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[Assistant] And yeah, I kind of, I had worked myself into thinking it was going to be Austin, even though thinking about it, Austin doesn't actually make any sense in the storyline. It does, it does. With The Rock. Like, and that's the person he's having a stare down with. But, The Undertaker makes more sense with everybody. Because The Undertaker's had its own feuds with The Rock.


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[Assistant] The Undertaker was managed by Paul Heyman. Paul Heyman and Roman Reigns were the two in 23 and 2.


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[Assistant] Or however many matches he actually had, I can't quite remember. But like, he, he, he had, uh, he's had feuds with everyone in the bloodline kind of thing. He's also got history with Rikishi if you want to go down that road. Like, like Undertaker makes narrative sense. And I don't even need to bend over backwards to try and make it make sense. It's all just there. Like, okay, here's my thing.


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[Assistant] I think it did make sense and I think the other one you could have done for me is like, if I'm in that room, I'm going.


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[Assistant] That's it, it's, it's, we, we as fans need to know that we got, even, even in WrestleMania 30 when we got Bryan.


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[Assistant] I, you then don't trust that it's gonna be followed through on any point. Like, this, this, this regime...


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[Assistant] Feels like it will. And look, it was a whole weekend of people putting him over.


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[Assistant] Paul Heyman, I will always be a Paul Lavac guy. Like, the idea of the leadership of him, what you hear people talk about him. On a creative standpoint, I think we've seen the fruit of that. And that as a fan is all we really need. I said AEW had this, especially in the early days.


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[Assistant] Starting here, like you know when you do a new book and you've waited a year for the new Harry Potter book to come out and then it starts again and it has to hit you up the whole way through. That's what's happened here. And actually, certain absences on the show and certain presences on the show make me go, mmm, I like where this is going.


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[Assistant] I definitely, I liked the idea of the redemption tour going around giving opportunities to people who didn't, who specifically were screwed over. Uhm, some of those people though, Drew McIntyre, have gone on to


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[Assistant] He's over there with a massive pile of money, Triple H is paying me to save us. Every ounce of WWE at the moment feels like there are so many different options and they've got plans for them all. And it makes me just want to relax.


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[Assistant] I think the reason that a few people wanted it is because that, that, and it was a, we got a reminder of who Bayley is at WrestleMania. She's one of the best to ever do it. Specifically because that character, that other character was the sweetest girl he's ever met.


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[Assistant] She wouldn't think twice to kick your head in, right? She was so good at that. She was such a good character in NXT.


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[Assistant] And so we are, as, as people who are invested in this character, ah, that's, that's crushing. It's crushing. And it never got the opportunity. We trust this regime with Triple H in charge knowing how he feels about Bailey to do that character right. But also, we have moved on.


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[Assistant] I think, I think I'm team Bailey EO. I do. Uh, Bailey EO was my match of the weekend, not including main events. I thought it was absolutely outstanding on so many different levels. I loved Becky and Rhea. Yeah, the match was great. And knowing especially that Becky did that with a strep throat and 102 degree fever and Rhea Ripley had a panic attack seconds before she left. Oh my God. The fact that the two of them pulled off that match is unreal.


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[Assistant] It's like an hour before the show. And it's things like that not to ruin the entire industry that we're in and then, you know, Wizard of Oz pull back the curtain. It's stuff that makes people like us that are like out of shape dads and, and, you know, whatever chatting about


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[Assistant] Giving critiques on whether or not this dragon sleeper was done particularly well. Like, I'm sorry, those are the real superstars. D'you know what I mean? I thought both matches were great. Becky, Eo...


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[Assistant] Maybe it was because of Bailey Eo, because it did, it reminded us of who Bailey is, and it reminded us of who Eo is, and how actually criminal it is that Eo had only previously defended that belt against Meachin.


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[Assistant] In January, and then like another time since October, criminal. And that's why it got the extra tick for me.


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[Assistant] And it not being this, here's a big name that's just arriving for a pop and we don't know what we're gonna do with them. It's, it was a lovely like, hey, it was a showcase is what it was. It's exactly what it was. This is who Ili Draganov is.


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[Assistant] Ava, so there was a moment later on in the show where we had Ava, uh, Aldis and Piers all together talking about the draft. And Ava said, you never know who's going to be coming to NXT. So she kind of inferred there, they are getting a draft hit.


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[Assistant] Uhm, I know what you mean. I did feel that. I also was not of the mind. I thought that the booking of the cash-in was excellent. So great. I think what we saw later on in the night was excellent.


[User] Please transcribe this text

[Assistant] I think Priest is and looks excellent. I think it was too soon for him to feel like the big star. That said, go on. Well, standing next to Ririflu with her matching belt.


[User] Please transcribe this text

[Assistant] Yeah, I uhm, actually, this was a good opportunity for Damien Priest to assert a level of power and dominance. Almost being like...


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[Assistant] You guys deal with this now, I'm the world champion, like Sod off, this is JD, you do something, I don't care, I'm leaving kind of thing.


[User] Please transcribe this text

[Assistant] I think that would have been a really good opportunity to show- to step him away from Gooba Squad of the Judgment Day and make him feel like more import- more important than this R-Truth tag team nonsense.


[User] Please transcribe this text

[Assistant] Uhm, they didn't do that, but that's whatever. But then when the match itself is going down, the second he says someone you can't see and you think about little Jimmy and how he'd said on like, Pat McAfee's show or ESPN or something like that, he was like, I used to watch him do the open challenge every Saturday morning or something and it's hilarious.


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[Assistant] Yeah, it was, it's very good. And Sino looked like he was having fun in the moment as well. We were all having fun. That crowd who were hot all night were having fun. It was great.


[User] Please transcribe this text

[Assistant] You know when, you know when The Rock pulled off his Chippendale trousers and, and wearing his just wrestling gear?


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[Assistant] And it looked like, oh, you just don't make sense. Sina in old Sina gear, like the jorts and the brightly colored t-shirt. I'm like, yeah, but you are like a 40, 50 year old man. Like you don't look as cool and young as you used to do. Cause he's got the bald patch on the back of his head. But like also, also I don't, also I don't care. Like crack on. But that's it. It's raw after


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[Assistant] I loved this far more than them having one corner of the backstage area where they film everything. You know, it was, it was, it's, you're trying new things, keep trying them. This is great.


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[Assistant] Yeah. I, I, again, I'm still in the same boat I was with this kind of story. I'm all for it. Just stop telling it in the wrong order. Yeah. But now, now at the very least, this is the right order.


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[Assistant] I thought it was good. Lovely, lovely, lovely. We're after Mania match. Chad gets the ball, which was also a good choice. Uhm, uh, more interestingly though, is what went down. Oh, by the way, which I know we want beach ball and then go fuck themselves.


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[Assistant] Bringing a beach ball to the raw after mania in the year 2024.


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[Assistant] I bet you're really boring at parties. Like, I bet, like, you tell the same joke over and over again. Like, it's just, that was so ten years ago, and even then it was disrespectful. Like, this is just not, this is not the time for that. Let's move on. Uhm, I hate fun.


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[Assistant] It's like a, it's like a, I'll give you the opportunity, but it's on my home to situation, which was quite interesting.


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[Assistant] I am not sure where this goes. I saw Chad and Sammy as a great first story to fuck, because for me the question is always what next? Theme of the show.


[User] Please transcribe this text

[Assistant] What do you do after Gunther's reign to solidify the work Gunther's done? Sammy and Chad Gable as a storyline is a really good, ehm,


[User] Please transcribe this text

[Assistant] Fascinating. Have you seen it? I have not seen it, no. I encourage you to go and watch it. It's all a 50 seconds long. But it is a full heel promo.


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[Assistant] He basically says, you asked for this, so don't be, you, don't be surprised, and I'm, I'm almost quoting directly, slight paraphrasing, don't be surprised when it's not only his wife, his son, but also the entire city of Montreal crying when I walk out as champion. He basically said, I'ma make, I'ma make your little boy cry. It was, yep, there it is, it was


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[Assistant] Real good heel talk. And it was also the best Chad Gable promo I've really sort of seen. I liked all of the It Means More stuff that we'd had. I liked, I've liked this business with Sammy, but there was so, I was like, lean in, like this is interesting. So where do we go from here? Does Sammy lose the belt?


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[Assistant] To Gable, and then Gable doesn't grant him a rematch and instead goes a little bit heel. Does Seamus come back and interfere and then we can extend the Sami and Chad story a little bit longer? Personally, I'd have had Sami always have a perfectly


[User] Please transcribe this text

[Assistant] Tonight, for instance, I was gonna give you an opportunity, but then Imperium got in, so we tagged instead. Next week, for instance, you can have MacIntyre absolutely, you know...


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[Assistant] Because I'm going to be great in the match. If it's just a match, it's going to be a great match.


[User] Please transcribe this text

[Assistant] I feel like I got enough of it to be satisfied in that program with Gunther, and something about this, it's 50 seconds long, but something about this made me go, that's more interesting than, I thought I wanted that, I don't, I want this now.


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[Assistant] I didn't know who it was going to be. I quite like the idea of it being Bayley and like her just getting a sort of run out match as a champion, get a win straight away kind of thing, another one.


[User] Please transcribe this text

[Assistant] The storm was coming and the blue light, I was like, oh, and that.


[User] Please transcribe this text

[Assistant] Is more evidence of how Jay Cargill is just everything.


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[Assistant] Not everybody in the Hall of Fame is gonna want a world title. Not that the Hall of Fame is necessarily the most real thing in the world, but like, I feel very much that I'm someone who like wants a, I'm a Lindsay Lohan and then a Mean Girl kind of guy with a bit of crown for everybody, you know.


[User] Please transcribe this text

[Assistant] Uhm, the idea that, I like the idea that everyone gets an opportunity. I'm joking about being my late twenties, but I am not joking about being younger than Luke Owen. I'm not your age.


[User] Please transcribe this text

[Assistant] The uhm, I like the idea of everybody, good work deserves reward, I like the idea of everyone getting an opportunity, uhm, if the work and story and the character creates the right moment. Absolutely. That's why I'm sat there going like, I kinda want Natalia to have a world title reign of some form, I want to get her there because I believe she has


[User] Please transcribe this text

[Assistant] The skills to do it if she's booked correctly. Uhm, but also, you are right, not everybody needs a world title reign. It's about playing your part. Too cool, we're in a main event on Raw, uh, on a, on a, one I rewatched the other day.


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[Assistant] Nobody in their right minds believes The Two Cooler Main Event starts, right? But they are such important parts of what makes that show good. Chelsea Green is a massively important part of what makes Roar an entertaining show to watch.


[User] Please transcribe this text

[Assistant] Well, it depends on what you want to do with Clash, right? And now I- I- I- Over the Western Mania weekend, uh, Tempest was-


[User] Please transcribe this text

[Assistant] We're requested to be a few years less if you want to, or whatever. Like for me, I don't- if I'm Drew McIntyre's agent, I'm telling him to stay where he is.


[User] Please transcribe this text

[Assistant] I feel like you can either have him win it at Clash,


[User] Please transcribe this text

[Assistant] Which is due, it's before Money in the Bank, so that's June. Or, uhm, he's the champ going in.


[User] Please transcribe this text

[Assistant] I don't know how you do that, but I think he's in the championship. Unless it's, it's punk back in time. I don't know. But, uh, yeah, the idea of the money in the bank thing is super fun, but that's far enough away that I don't know if it, if there's...


[User] Please transcribe this text

[Assistant] I mean, you do need to do, it's the same with the Royal Rumble where it's like...


[User] Please transcribe this text

[Assistant] I don't wanna not see the Royal Rumble, but it hasn't been amazing for a few years now. Money in the bank, I like money in the bank as a concept. It hasn't been amazing for a few years now. You need to do something fresh with it. Agreed so.


[User] Please transcribe this text

[Assistant] It was really funny, he was like, he did some weird noises during this match. It was a great little bit of booking because everyone had been placed and then ricocheted up and he's sort of looking around the ring ready for someone, he's like, there's no one around, like, I'm the one standing, I could win this, but everyone's, oh, there's someone there on that table.


[User] Please transcribe this text

[Assistant] This is the most brilliant bit of punk being punk. Uh, grabbing the foot and not, I was not seeing it until the camera sort of pans out a little bit and he's just there with a massive grin on his face, like ahhhh. Like it was really, really funny. Adding to yesterday,


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[Assistant] Uh, sorry, Sunday, where, where like the two of them together had a really big, you know, that, that booking of that cash in, as I said, was brilliant. Specifically when Priest comes out, and Punk has an almost like a, oh this couldn't be more


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[Assistant] Excellent way to- I mean, Jayan Priest is a really good thing, you know, that's- that's gonna be a good match. Priest hopefully to get a few wins up to build him into the main event status.


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[Assistant] You need- the After Mania shows, especially in this now interregnum period between now and the draft, are a complicated beast because you don't- you have to address the Fallout Mania, there has to be Fallout, ergo some of the people you've been seeing a lot have to be on the show.


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[Assistant] And you want to be able to start planting new seeds and then going off in new directions. Uhm, I wouldn't be surprised if these next few weeks do feel like showcases more than anything else.


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[Assistant] If you look at the picture of him at the press conference, he looks it. If you look at him with the Archer thing, the new music, he looks it. You gotta do the


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[Assistant] Yeah, I think we're gonna have to wait and see its posterity, but it- having watched it twice in 24 hours, I just thought it was brilliant.


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[Assistant] Liam, look, you're right, but you are comparing it to one of the greatest women's matches of all time, as decreed by Cameron on Top o' North.


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[Assistant] Yeah, I didn't know, there was a little part of me, I mean he's wrestled two nights in a row, marathon matches each, but I was like...


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[Assistant] Have him come out and have a challenger, like I don't know who it is, someone who can lose straight away, but like, have him be like, well I'm a fighting champion so I'll have a go. But I don't know who his first program should be.


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[Assistant] This is why, in my head, I had like a Sammy, where Sammy said, you once said that you believed I could do it. And now Sammy's way too involved elsewhere, uhm, it's gonna need a while before it becomes Sammy. Uhm, I don't know, and Orton is a nice choice, because he-


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[Assistant] You want to strike while the iron is hot. Yeah. A lot of the, the idea of holding things back necessarily is, is, I understand why you would, but you don't want to deal with everything. Yeah, so.


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[Assistant] But that said, uh, ag-again,


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[Assistant] In, in the history of NXT, when you think about what Kairi Sane represents, when you think about what Asuka represents, when you think about what Eoskai represents, it's not the case to se- recent history is different, I, I appreciate that, but


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[Assistant] I have absolutely no idea, and I want to keep it that way. I think I want to go very sensitively around this because it is obviously


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[Assistant] Let's see what happens with Uncle Howdy. Yea, Bray's vision without Bray is going to be an interesting thing.


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[Assistant] Yeah, uhm, I don't know if I, I don't, I don't know if I'd consider it actively punching down in the same way as I would. I don't know, yeah.


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[Assistant] I disagree also, I mean, I'm a, this is, this is me being a liberal, but I disagree with the constant, the necessity for a constant grind. I think there's definitely a-


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[Assistant] It's not even a WWE mindset exclusively. It's a, it's a wrestling mindset from years gone by. So as a result, it baked into WWE. It's baked into the industry. So it is, you know, it's what punk with the whole idea of guaranteed contracts, which I don't agree with. I do agree with incentivization, but I don't agree with, um, baked in, you know, like removing


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[Assistant] Definitely, he's been MIA, he's been in the Japanese forests for months, um, hunting


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[Assistant] I think the thing for me is, I love wrestling so much because it's about story and storyline. That's what I love in professional wrestling. I think it is the most unique


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[Assistant] Storytelling medium in the world. I think it's incredible the fact that it's so layered.


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[Assistant] The fact that it's about the match, but also about the story that brings to the match, but also about what those competitors have been doing over the course of the year, then over the course of their careers, the reality, the fiction. I just think it's the most, like I, I love storytelling and I always have in film, TV, theatre. Wrestling is just the most unique part of it. It's why, it's why I'm here.


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[Assistant] Yep, that's exactly it. When you hear the, the, the, you deserve it. It's again, it's not just necessarily because he changed the wrestling industry. It's the humanitarian stuff he does. It's the, the wrestling club stuff. Like it's just, I don't see how you can not like him.


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[Assistant] I absolutely considered Roman a candidate for a babyface run now. There's something about the appreciation from the fans and knowing that Rock could possibly beat the ever living crap out of him for letting the family down. I really would be interested to see what they do with that.


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[Assistant] Yeah, I mean Seth's at least four weeks is what I've read, but I mean the guy deserves a break. Sure does. Like his back, uh, and knees must be hurt from carrying the company or whatever, like the line is, uhm, Becky, eh,


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[Assistant] I don't wanna, I don't know about family stuff, but their daughters are pretty


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[Assistant] Look, they've both done incredible stuff with their career, and if they want to go a bit more of a part-time schedule, I actually think it's a better use of them. I've always, always had a champion for an off-season for certain wrestlers. Cycle them in and out, give people some time off if they want it.


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[Assistant] Not only for healing their bodies, but for making the audience want to see them again after a while. So yeah. Yeah, hope so.


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[Assistant] So uhm, to get started, I thought I'd just share how we're going to do the questions today. So if it was your first time joining us, then what you need to do is go to the Hugging Face forums.


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[Assistant] And if you click on the course category, you'll see that we have a bunch of topics for each of the speakers today.


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[Assistant] And so for example, today, I think I'm going first. So you can go to this topic and then you can ask, you know, whatever question you want. Doesn't just have to be about the, the, the talk. Go, go ahead. Ask whatever you want.


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[Assistant] And then, um, we'll base the voting on the number of likes per question, just in case there are lots of questions that people want to ask.


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[Assistant] G'day, this is Lewis from Hugging Face, and today I'm going to be talking to you about the trainer in the Transformers library.


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[Assistant] So as you can probably guess by the name, the trainer is for training models.


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[Assistant] So in addition to a model, our datasets, you provide some hyperparameters, some metrics, and it takes all of this and then gives you some methods that let you train, evaluate, and make predictions using the same object.


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[Assistant] So there's a couple of times where maybe you don't want to use a trainer. The first one is that the trainer is based on PyTorch.


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[Assistant] So if you're not using PyTorch, you're going to need to check out, for example, Matt's video where he shows you how to train transformers using Keras and TensorFlow.


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[Assistant] The other cases where sometimes your logic can be very specific where you need some fine grained control over the training loop and in those cases you should use the Accelerate library which Sylvain is also talking about today. So you should check out his talk as well.


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[Assistant] Okay, with that, let's dive in and look at how the trainer works.


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[Assistant] To get started, we need a data set. And the data set I've picked today is called the emotion data set, which is a data set of tweets which have been given or labeled with a different set of emotions ranging from things like joy to anger or disgust.


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[Assistant] So we can pull this dataset from the hub using the load dataset function. And you can see here that we've got, uhm, three splits.


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[Assistant] Now, if we look inside the first element of the training set, uhm, you'll see that in the datasets library, every kind of row is represented as a dictionary, uh, by default.


[User] Please transcribe this text

[Assistant] And the values or let's say the columns of this data set are represented as the keys and the values are the actual values of the rows.


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[Assistant] So the cool thing about the datasets library is it lets us switch easily between these different formats. And one way to do that is to just apply the to pandas function to the dataset. And voila, we now have data frame.


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[Assistant] So in Dataframe we've got some tweets and we've got some labels which are


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[Assistant] So a very quick way to figure out what's going on is to inspect the features of the data set. So the features will tell us what data types are being used for each column in the data set. So you can see here that the text column is basically a string as we would expect.


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[Assistant] So this class label data type, uhm, it has some special features. It has some attributes. For example, we can see that already there are the names of the emotions here.


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[Assistant] Data type has a function called intString, which does that for us.


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[Assistant] So here it says the label ID zero is corresponding to sadness. And so what I can do is I can create a mapping that just relates every single label ID to the corresponding label name. So here I've just created dictionary using this into string function.


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[Assistant] And we, while we're at it, we can also go the other way around and provide the relationship of the name to the ID.


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[Assistant] And the reason I'm creating these mappings is because when we do, um, text classification with transformers.


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[Assistant] It's really useful if we provide these mappings to the model so that then when we want to do inference, instead of just getting a prediction of a 0, a 3, or a 2, we actually get a prediction for the actual name of the emotion. So this is a really common thing that we're going to need to provide to the model, and it's always good to do it, uhm, straight up.


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[Assistant] Now the other thing I do in text classification problems is I just inspect the distribution of classes, like what are the frequencies. So in Pandas you can do this with the value counts method. And what you can see is that the data set is very imbalanced. So for example,


[User] Please transcribe this text

[Assistant] The, uhm, label, or the class zero, uhm, which is sadness, is represented roughly 30% uhm, through the data.


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[Assistant] Uhm, the same is true for Joy, so that's another third of the data, uhm, has been labelled, uhm, as Joy. But then, like, the last one, which is Surprise, has only, you know, been given the label, or there are only roughly 4% of the tweets are actually given the Surprise label.


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[Assistant] So if we train a model just kind of naively on this distribution, one problem that can happen is that the model will just


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[Assistant] I get very good at predicting these majority clauses.


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[Assistant] But it will struggle a lot on these rare classes. And so if these classes are important for some application that you're building.


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[Assistant] Now there's a couple of techniques that people use to handle this. Uhm, the first one is to kind of modify the data itself.


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[Assistant] So for example, we could upsample the rare classes by just duplicating


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[Assistant] Uhm, but the downside with that is that deep learning models like transformers are really good at memorizing or, you know, discovering patterns in the data. And so if we duplicate a lot of examples,


[User] Please transcribe this text

[Assistant] Then probably the model is going to kind of memorize those duplicates. And when it sees examples from that class in production, it's actually not going to generalize very well.


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[Assistant] Uhm, of the model during training, uhm, and this will allow us to then, uhm, kind of introduce a bias directly at the level of loss function.


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[Assistant] Uhm, which indicates, you know, these are the way that the classes are distributed and hopefully this will encourage the model to pay more attention to these rare classes.


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[Assistant] That the first thing you need to do in most NLP problems is tokenize the data so that it gets into a format that's suitable for training. And the model I'm going to use today is called MiniLM.


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[Assistant] Now, if you go to the model hub, there are thousands of models. Uhm, here you can see that there's roughly 32,000 models. This is not all the public models, it includes private ones as well. But in any case, you can see that Burt Bay is currently the most popular model in the hub.


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[Assistant] And that's maybe not so surprising because BERT was like the first transformer that really, you know, took over NLP. Uhm, but you know, the field moves fast and this model may be kind of outdated. And to kind of show you that.


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[Assistant] There's a really interesting paper called MiniLM by Microsoft.


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[Assistant] And they showed that you can take a kind of BERT model and you can compress it into a smaller version called MiniLM. And so here you can see that BERT has roughly 110 million parameters and they were able to reduce this to 66 million parameters, so almost half the size. But the cool thing is that if you look at the performance, uhm, MiniLM


[User] Please transcribe this text

[Assistant] Averaged over all these kind of poor NLP benchmarks only really drops one percentage point, uhm, compared to BERT.


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[Assistant] Uhm, you know, which is quite impressive. It's roughly twice as fast because it's twice as small, but it barely loses any performance.


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[Assistant] So, my advice is maybe don't use BERT, don't reach for BERT the first time. Maybe pick one of these, uh, faster and, you know, models like Mini-LM or to steal BERT. And you'll typically find that you can then iterate much faster in your experiments because, you know, they're faster to train.


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[Assistant] So I'm going to use the Minilin model and I just pick the pre-trained checkpoint from the hub.


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[Assistant] Now, once we have our tokenizer, we can feed in an example and we can see that, okay, we're now getting input IDs. So these are like the sort of tokenized inputs that we're going to feed to the model.


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[Assistant] We have token type IDs, which are kind of common with BERT models to indicate whether we have a, um, you know, sentence one or sentence two. And then there's the attention mask, which is used to indicate, you know, which tokens correspond to padding or not.


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[Assistant] But this is all covered in part one of the course, so go check that out if this is unfamiliar.


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[Assistant] But the thing that we want to do is we want to apply this tokenizer to all the examples in the dataset.


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[Assistant] And the datasets library provides a map method, which is a powerful way of just providing a function and applying that function to all the examples.


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[Assistant] So here what I'm doing is I've defined a function called tokenize text, which will take some inputs. It will tokenize them just as we did before.


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[Assistant] And the other thing I'm gonna do is I'm gonna truncate, uhm, any of the long examples, uhm, because every transformer model has a maximum sequence size it can process.


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[Assistant] A tokenized data set, and you can see that indeed we've now added some new columns, sorry, the input IDs and token type IDs, which correspond to the objects that we want to use, you know, during training.


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[Assistant] So the way we could do this is we can say, okay, the data, there's some frequency distribution and I'm going to then introduce some kind of weights or let's say coefficients for the loss function, which will multiply each one of those classes by an amount reflected in the data.


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[Assistant] And if we demand that these coefficients range from zero to one, then intuitively we


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[Assistant] Because the trainer is based on PyTorch, we ultimately want all of these weights to be Torch tensors. So I'm just going to convert this from a NumPy array into a Torch tensor.


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[Assistant] And just one last thing, uhm, to sort of, uh, take note of is that, uh, the trainer, you need to indicate kind of which column, um, corresponds to the targets, in this case the labels. And in particular, if we look at the, um, documentation, we can see that


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[Assistant] Your model can compute the loss if a labels argument is provided. So what I'm going to do is I'm going to rename the label column to labels, and then we'll be good to go.


[User] Please transcribe this text

[Assistant] Okay, so we now have the ingredients we need to define our trainer. And what I'm doing here is I'm going to create a custom trainer so that we can do this kind of custom loss computation.


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[Assistant] So in the documentation, what we're told is that if you want to inject custom behavior into the training loop, you can subclass any of these methods here and


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[Assistant] And I just need to then input my logic to compute this weighted loss. So we can do this is we can feed.


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[Assistant] I suggest we, uh, we just pick up where I was and I'll just, uh, wing it.


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[Assistant] Because the trainer is based on PyTorch, we ultimately want all of these weights to be TorchTensors. So I'm just going to convert this from a NumPy array into a TorchTensor.


[User] Please transcribe this text

[Assistant] And just one last thing to sort of take note of is that the trainer, you need to indicate kind of which column corresponds to the targets, in this case the labels. And in particular, if we look at the documentation, we can see that


[User] Please transcribe this text

[Assistant] Your model can compute the loss if a labels argument is provided. So what I'm going to do is I'm going to rename the label column to labels and then we'll be good to go.


[User] Please transcribe this text

[Assistant] Okay, so we now have the ingredients we need to define our trainer. And what I'm doing here is I'm going to create a custom trainer so that we can do this kind of custom loss computation.


[User] Please transcribe this text

[Assistant] So in the documentation, what we're told is that if you want to inject custom behavior into the training loop, you can subclass any of these methods here and then input any sort of logic that you want. And so the one that we're interested in is the compute loss method because we want to compute our own loss function.


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[Assistant] And I just need to then input my logic to compute this weighted loss. So the way we can do this is we can feed the inputs to the model to get the outputs.


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[Assistant] We can then extract from these outputs the logits, which is what we ultimately wanted to compare with the labels. We get the labels.


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[Assistant] And then in PyTorch there's a loss function called cross-entropy loss where you can provide an actual weight corresponding to these class weights that we computed earlier.


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[Assistant] And once we have our loss function, we can then compute the loss directly.


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[Assistant] And we just need to return, uhm, you know, the loss if the function just returns a loss by itself or in some cases we also need to return the model outputs and that's indicated here by this return outputs flag. So now we've got our trainer, we can just go ahead and fill in the missing pieces that we need. And those missing pieces are kind of described


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[Assistant] So we can instantiate a model using the from pre-trained method.


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[Assistant] And because we're dealing with text classification, we're going to treat this as a sequence classification task.


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[Assistant] And here you can see that in addition to the pre-trained checkpoint that we use for the tokenizer, I'm also providing the number of labels that are associated with the dataset.


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[Assistant] And also the mappings from the label IDs to the label names. So this will instantiate the model.


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[Assistant] Then we need some metrics. And here I'm using the F1 score from Scikit-Learn. And the reason for that is that if you're dealing with imbalanced data, the accuracy will be biased towards the majority class and give you kind of like a false or inflated score.


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[Assistant] Whereas the F1 score is sort of like the mean of precision and recall, and this is a better way of kind of trading off, uhm, these, these are imbalances.


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[Assistant] So this function you would have seen in part one, but basically we extract the labels and the predictions. We feed them into our metric and then we just return a dictionary which has that metric. So that's pretty straightforward.


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[Assistant] And then the last thing we need to provide are the hyper parameters. So here, the main thing you need with the training arguments is you need to provide an output directory, which is going to save all the checkpoints of the model, the tokenizer, the configuration, some other files.


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[Assistant] Uhm, but we also can specify other things, for example, the number of epochs we want to train, the learning rate, and one thing that's really good to know is, for example, whether you use Mixed Precision. So if your GPU supports Mixed Precision training, you should always use FP16 True to make it go fast.


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[Assistant] And the last thing we want to do is we want to push our model to the hub, um, during training. So all the checkpoints, we can just push them automatically to the hub with push to hub true.


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[Assistant] And you'll find that model basically in the same location on your account as the name of this output directory. And we'll see that in a minute or so.


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[Assistant] So once we've got the model, the hyperparameters, the metrics, the data sets, we feed all of these into the trainer.


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[Assistant] It will then clone it and then in our local directory, we'll use that to store all of these checkpoints during training.


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[Assistant] So now we can train the model and it's just a one-liner and this takes around five minutes on my GPU.


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[Assistant] And the F1 score has gone up. So you can see that after five epochs, we now get around 92% F1 score, which is great.


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[Assistant] And we can see that, you know, we've, uhm, we've got like an inference widget, so we can, for example, uh, inspect, you know, what are the outputs, uhm, for some inputs. So here's some test which says, you know, I like you, I love you, and indeed, we would expect this to be about love, that's what we see.


[User] Please transcribe this text

[Assistant] Um, and we also have some information about the training, um, which was, you know, stored in the trainer and then saved into the model card. So this is, this is all, you know, really useful information.


[User] Please transcribe this text

[Assistant] Uhm, now at the end of training, you might want to use this model in your own application. And so for that, you should use the pipeline function.


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[Assistant] And so here we just provide to the pipeline function the checkpoint that we've created.


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[Assistant] And once you have that, you can provide some text and see what the outputs are yourself.


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[Assistant] So indeed, if I'm really excited about part two of lagging face course, this should be joy.


[User] Please transcribe this text

[Assistant] And that's basically it. We've created our own trainer, our own custom trainer by overriding some functions.


[User] Please transcribe this text

[Assistant] Yes, you can. So uhm, basically you can uhm, actually use the datasets library to uhm, load your own datasets. Uhm, for example, if uhm, you have your datasets stored as a CSV file.


[User] Please transcribe this text

[Assistant] I mean Sylvan is the creator of the trainer, so he can always override me with true wisdom.


[User] Please transcribe this text

[Assistant] Yeah, that's a great question. So uhm, this is certainly something that I think happens almost any time you're working outside of the US and you have to deal with multiple languages. And, uh, for me personally, like a very strong baseline for this is XLM Roberta.


[User] Please transcribe this text

[Assistant] It's a, it's a model from Facebook that was trained on 100 languages in parallel and it gets quite good results and it does better than for example, multilingual BERT, which was one of the first multilingual transformers. So that's probably what I would use if I was starting out.


[User] Please transcribe this text

[Assistant] Well, you should go to chapter 8 of the course and watch the video where Sylvain walks you step by step on how to do this.


[User] Please transcribe this text

[Assistant] Uhm, but you know, just to give you a high level summary, I mean, it's a training kind of loop or pipeline and there's several places things can go wrong.


[User] Please transcribe this text

[Assistant] So the first thing that you should always check is the data in its right format. So a really common mistake that I myself make almost all the time is I forget to tokenize the data and then you're just trying to feed strings to the model and it gets confused.


[User] Please transcribe this text

[Assistant] Uhm, another thing that can go wrong is maybe the name of your labels column isn't right. So you saw in the tutorial I had to change this to make it work kind of out of the box. Uhm, and then of course there's all the CUDA hell


[User] Please transcribe this text

[Assistant] Of, um, you know, things like out of memory, um, side assert errors, all this garbage and, um, they're the great tip from Sylvain is to put the model on the CPU because then you'll actually get a trace back in Python that tells you where the, you know, which line of code the error is actually coming from.


[User] Please transcribe this text

[Assistant] So I would say those are kind of the main things. So check the data, uh, put it on CPU and then, you know, have fun.


[User] Please transcribe this text

[Assistant] Um, yes, so let me just like very quickly say what I was trying to do here. So, um, basically in the trainer can override various methods. And the thing that we wanted to do was to find a way to kind of weight the loss according to the distribution of classes in the data. So I had actually previously computed the class weights.


[User] Please transcribe this text

[Assistant] And what you can do then is just extract the outputs from the model.


[User] Please transcribe this text

[Assistant] Um, to get the logits, get the labels from the data and then just compute the loss using the standard cross entropy loss. And then basically this function will just return the loss and the model outputs, um, if that's needed or just the loss by itself.


[User] Please transcribe this text

[Assistant] And uhm, yeaah, this shows you kind of how simple it is to kind of customize the trainer to do the things that you really want it to do. Uhm, if you've got like a sort of task or application that isn't supported out of the box.


[User] Please transcribe this text

[Assistant] That's a great question. As far as I know, we don't have something like that in the Transformers docs.


[User] Please transcribe this text

[Assistant] That would actually be a great community contribution. So if the person who asked that knows the answer or wants to learn, uhm, it'd be really nice to have like a table in the docs, which explains like which loss function is kind of optimal for which task.


[User] Please transcribe this text

[Assistant] I would say that in general, like for most applications, uh, fiddling with the loss function is kind of the, the latter thing that you want to focus on. It's much more effective to switch the architecture, for example, or just improve the data set. If you, if you're really trying to squeeze performance out of, out of the task. But, um, yeah, that'd be a really nice community contribution.


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[Assistant] Yeah, that's a really deep question, because we actually know that the way that we initialise neural networks has a long history of people finding different ways to initialise it and get suddenly state of the art.


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[Assistant] Uhm, I would say that in the case of Transformers, because we're kind of building a head that is randomly initialized on top of a pre-trained body,


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[Assistant] Most of the time this isn't a concern. Uhm, you, you can just train it and you'll get some kind of fluctuation with each training run. But generally speaking, you'll see pretty stable results.


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[Assistant] The only time I've seen this actually be a problem is if you have a lot of classes. So imagine I have like a thousand classes or something like this and suddenly in, you know, some of the rare examples, my model has only got like three examples to


[User] Please transcribe this text

[Assistant] So, so Matt, uhm, you may have seen recently, he just tweeted like an hour ago that everyone comes to him complaining about TensorFlow bugs and I'm one of those people. And so.


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[Assistant] Today, uhm, Matt's gonna tell us, like, how amazing TensorFlow and Keras is. But what he actually does at Hugging Face is also maintains the TensorFlow side of the Transformers library.


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[Assistant] And uhm, he's a great speaker and has a lot of great humor. So I'm looking forward to plenty of jokes. Uh, no promises. All right. So let me get this stream.


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[Assistant] So thank you very much for that talk, Matt. That was super nice.


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[Assistant] We, we also appreciate the little dig at PyTorch.


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[Assistant] It's important to be clear where my loyalties lie.


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[Assistant] Cool, so we have a couple of questions. Uhm, so let's see.


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[Assistant] Yeah, I think what you can also do is use like natural language inference as the task, which is kind of like an elaborate classification task. If I remember correctly, that's actually how the zero shot pipeline is based on. So you could do that intensive flow pretty easily.


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[Assistant] Okay, so we have another question which is maybe coming also back from my talk. So imagine I have like an imbalanced data set. Do I need to worry about preserving the imbalance, like the class distribution per batch or is this not a problem?


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[Assistant] Cool, and maybe I can ask you a question. So I think one of the really exciting features in the TensorFlow API that you've been developing has been this kind of conversion of datasets into TensorFlow datasets.


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[Assistant] Uhm, at least myself, this was always a pain point in like bridging the gap from, you know, the hugging face ecosystem to the careless one.


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[Assistant] Thanks once again, Matt, and I'll hand over to Savon to introduce our next speaker.


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[Assistant] Cool. So a couple of questions that come to mind is when we're using the hub to share, say, models, for example, some of these models that we have now are getting quite large. Is there kind of a limit to what I can push to the hub? Can it be Upt3?


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[Assistant] Exactly. And I think it's, that's the key thing that like, it's not necessarily a question of either or it kind of really depends on your use case. And also maybe if you're, for example, using the TensorFlow Hub, you get some extra features like TensorFlow Extended and you know, this, you know, if you're, if that's what you're working in, it makes sense to use the best tools for that.


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[Assistant] And maybe maybe a more personal question. So, you know, right now we have quite a few different positions available in the open source team. And maybe it'd be cool to sort of tell people a little bit about what is it like being like a day in the life in the open source team, a hugging face and you know, maybe for yourself, you lead the team. So what does that also involve?


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[Assistant] Awesome. Thanks a lot for all that information. And I was just going to maybe a follow up thing would be like, you know, if I'm interested in one of the roles in the open source team.


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[Assistant] Uhm, probably the thing that like really helps if you want to sort of stand out from the pack is to have some open source experience yourself. And a great way to start is actually just to start looking at the issues on say the hugging face libraries. You can check out the data sets, transformers, hugging face hub library.


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[Assistant] And this is often a great way of kind of developing a kind of


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[Assistant] I don't know, unusual relationship with the maintainers. I find this was, I think, in my case, personally, my very first contribution was to the Datasets library. And this was where I interacted with Conson and some others. And I don't know, it's a nice way of getting a feeling of like what it would be like also to maybe work.


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[Assistant] Cool. So then one last question, which is similar to Matt's. Uhm, also you're heavily involved in the Transformers library. Uhm, what is on the horizon? We've now got vision, we've got audio and NLP. What's next?


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[Assistant] Awesome. So that means I can do like from pre-trained and it just works.


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[Assistant] Awesome. Uhm, so Vant, should we introduce the next speaker or should we keep asking Lissandra, uhm, and have a chat? I have a long list of questions.


[User] Please transcribe this text

[Assistant] So another question that uhm, maybe it would be interesting to hear about is uhm,