Shadowing-Übung: Analyze SEC filings with Transformers for fun and profit - Englisch Sprechen Lernen mit Video

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Hi everybody, this is Julian from Hugging Face.

Processing financial documents is a very strong use case for natural language processing and transformers.

In fact, most organizations in financial services and beyond need to work with a mountain of documents from company filings

to analyst reports and more in order to extract information, classify the documents,

run sentiment analysis and summarize documents etc etc so there are really

so many things you could do here

so in this video i'm going to work with company filings

that i download from the sec website and we'll use a pretty cool sdk

that i found to do this and then we'll process the filings

and we will run a few transformer models namely summarization

and emotion detection or sentiment analysis okay so let's get started so let's take a look at the raw material the documents

so starting on the sec website we go to filings

and we select edgar which is the name of the database

that includes company filings

and we could look for any particular company just to take a look maybe at one of those documents before

we start processing them so why not pick amazon

and so we see different filing types 10k

which are the annual reports 10q quarterly reports and 8k current reports

and we'll be able to work with all of these in our example

so let's maybe take a look at the latest quarterly report

the structure of this document is is well defined the table of contents is always the same

so we'll find lots of financial information then we'll find this section called management discussion

and analysis of financial condition and results of operation

and this is uh usually very interesting

because you get you know the management's opinion on how things are going

and and they highlight you know stuff that's going well stuff that's not going well

etc etc so for financial analysis this is pretty pretty interesting

and we'll we'll take a look at this section again

when we start uh processing documents okay so uh you can go

and have fun and find the information that you need here.

But of course, what we want to do is automate that process, download the documents, process them,

extract the text sections that we're interested in, and run transformer models on them.

And there is an API for Edgar documents, but as it turns out,

I found

recently i should say that some of my former colleagues at aws have actually built an sdk

that lets you easily retrieve scc filings and uh

and break down those filings into different sections

that we can save in a csv file and process so this is a huge time saver i mean

Personally, I'm not a big fan of text processing and regular expression,

so these people have done the heavy lifting for us.

So thank you very much, and I'll be happy to reuse what you've built.

They've written also a couple of blog posts on the SDK and how to use it for different use cases.

and the notebooks are actually integrated in SageMaker Jumpstart, which lets you easily deploy machine learning solutions on SageMaker.

So I'll put all the links in the video description and you can go and read about it.

I'll just show you how to use the SDK.

But yeah, thank you for building this.

It's quite useful.

And of course, this SDK is on GitHub and it's called the Jumpstart Industry SDK,

although it's only about financial documents for now.

But maybe we'll see other industries integrated there.

Okay.

All right.

So that's what we're going to do.

Grab some filings from the SEC website,

process them with this SDK so that we get a data frame with individual document sections in different columns,

and then we can apply some transformer magic to that.

Okay, so let's get started.

I've broken down my example in two notebooks.

So the first notebook is data prep.

Okay, so downloading, processing, and saving to CSV files.

And the second notebook is about running transformers.

It makes it a little easier to keep track of everything.

All right, here we go.

So I include for reference the name of the Jumpstart solution that I grabbed some code from, and it's called Dashboarding SCCText for Financial NLP.

And again, that's the SDK we're using.

So maybe let me zoom in a little bit.

So of course we need to install the SageMaker SDK and the Jumpstart Industry SDK.

Okay, so import them.

Create some objects that we're going to need, like an S3 bucket to download filings, the IAM role for permissions,

I mean the usual stuff.

Okay.

And next we can configure the downloading job, so to speak.

And of course, this is based on SageMaker.

And SageMaker users will easily guess that this is based on SageMaker processing,

which is an easy way to run batch jobs on SageMaker.

And it's really pretty easy to set up.

So we need a bucket to store the process documents.

So it's downloading the raw documents to one bucket, saving the process documents to another bucket.

And okay, this is the file name

that we're going to save the documents too

and the first step is to create this edgar dataset config object the first line here is

which tickers or company ids do we want to include so here i'm only fetching amazon documents

and these are the stock tickers right so

if you wanted google you'd say you know gog and

if you wanted tesla you'd have you know tsla etc etc and you can have as many as you want form types.

So here I'm grabbing 10

Ks and 10 Qs.

The SDK actually support other document types, and you'll find the information in the SDK, right?

The time range, so the start date, end date.

So then I'm grabbing old docs for 2019, 2020.

And this is just a bogus email that we need to retrieve documents.

You don't need a user account, by the way, to go and grab at your documents, although throttling is a thing,

so don't expect that you can pull 2 million documents in 5 minutes and not being throttled.

So you have to be considerate here.

Okay, so that's what we want to get.

Now that's the infrastructure setup, And this is where you can tell it's based on SageMaker processing.

So we'll run this on one ML C5 to Excel instance, which should be large enough.

No problem here.

And then we just launch that data loading job with the buckets,

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the file name to save everything to.

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And that's it.

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Right?

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And so you run this and, you know, I hidden the log.

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It runs for a little bit.

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It's based on PySpark, which SageMaker Processing supports.

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And funny enough, the internal code name for this was Gecko.

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And if you've seen the movie

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Wall Street with Michael Douglas, you know why.

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Okay, so good name for this.

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Okay, and let's skip the log, which is really, really long.

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And so at the end of that job, all I have is this CSV file in S3.

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Okay, so I can go and grab it, read it, and this is what I get.

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The ticker code, the form type, some numbers and dates, the text,

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the full text of the filing, and the management discussion and analysis,

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which is broken down as a separate section, because as I've said before, this is probably the most interesting part of the document.

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Now, if we want to go one step further, we can actually break down this text column into individual sections.

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So, going back to my example here, let's go to the contents.

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Okay, we want to break down every single item here as a separate column, because we may want to run some analysis on that.

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And this is where you really have to like the fact that it's all done for you, right?

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Because you don't want to run those regular expressions.

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And so they've included that code in a notebook, which is called SCC functions, whatever.

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So I cleaned it up a bit because it included other stuff that I didn't need for visualization,

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etc. But all that code is also a part of the SageMaker Jumpstart solutions.

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So they extract all the items, and you can see the regex here is pretty intense.

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Good thing I didn't have to write that.

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Yeah, let's not spend too much time on that.

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Basically, we'll just use those functions and map the column names to the right names,

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etc. So it's all done here.

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and this is the only bit that I wrote or adapted.

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I just put together a simple function where I can pass a data frame with filings, the form type we want to process,

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and then just do everything automatically, right?

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And then it applies all that regexp and returns a data frame with one section in each column, okay?

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So that's what we have for data prep

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And the only thing we need to do, starting from this initial data frame, is this, which is pretty nice.

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So just pass this data frame, say, hey, process all the 10K here.

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And then it returns another data frame.

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And now I can see in this data frame

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that the individual sections of the filing have been broken down into individual columns,

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which makes it easier to zoom in and examine and process particular things here,

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except of that huge blob of text.

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So I do the same for the 10Q filings.

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So it's a different data frame, different names.

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And then I just save those two process data frames to CSV files.

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Simple enough.

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Okay.

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So now I've got my two files, right?

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I could open one of them, I guess.

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And we'll see exactly the same thing, right?

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All nicely broken down into text sections.

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Perfect.

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So now we can move on to data processing.

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And the original blog post runs some NLP functions on the text,

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so you may want to read about that, although it feels more like traditional

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statistics

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and not there's no machine learning at work i think there's there's a bit of a hugging face summarization somewhere

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but generally they're just using traditional techniques here but still

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and that's part of the sdk so if you want to go

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and compute those uh those scores those nlp scores yeah you can go

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and do that but i've i've ignored that bit okay so i installed some dependencies

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pandas of course i'm going to need nltk i'm going to need to break down text sections into individual sentences

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and of course transformers for modeling okay fine

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so this is the tokenizer i'm going to use to break down text into individual sentences

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i'm loading my two data frames i should say

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i'm loading my two files into data frames here

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we just extracted very few documents to keep the demo very short you can go

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and extract much more okay so i have two 10ks

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because 2019 2020 and i have six 10qs

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because there are three quarters in between the the 10ks right for each year makes sense

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so um which model are we gonna use here so um

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and this is where you can start adapting my code of course here

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i just figured hey let's do a classification so i'm gonna analyze

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uh individual sentences in the uh in the the management analysis section and i'm gonna keep

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the most significant ones so the ones that are either highly positive or highly negative and ignore everything else.

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And then I'm going to concatenate all the positive sentences and summarize them and concatenate all the negative sentences and summarize them.

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But again, feel free to explore.

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You have this data frame.

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You can easily work with it.

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So for summarization, I'm going to use the T5 model, T5 base.

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And for classification, I've tried a couple of models.

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I've tried FinBert, which is, as the name implies, a BERT version fine-tuned on financial documents.

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And it can do sentiment analysis with three classes.

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But I've found that this one is also very interesting.

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It's a distilled BERT model fine-tuned on emotion detection.

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And it has a wider range.

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Anger, disgust, fear, joy, sadness, surprise.

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And it does pick up interesting things.

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We'll give it a go.

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So just use a summarizer, use a classifier, and you can see I'm using the pipeline API from the Transformers library,

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so it's really a one-liner to go and grab the model.

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And here's how I'm going to process those docs.

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So I have the first function called find emotional sentences where I pass text section,

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and it's going to tokenize that text section into sentences, okay, break it down into individual sentences.

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And then for each sentence, it's going to classify it.

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And if it's not neutral, and if the score is actually higher than a certain threshold,

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okay, so the really negative ones or the really positive ones or the really angry ones, maybe, I'm going to keep, okay?

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And I just append them to a list stored in a dictionary, right?

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So I'll have a list for positive sentences, a list for negative sentences, and so on.

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So I'm just grouping those, okay?

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And printing a summary and returning that dictionary.

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So that will return a dictionary with a list of sentences for each emotion, right?

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And then I have a summarization function where starting from that same dictionary,

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I just join all the sentences for each emotion and I summarize them and print the summary, okay?

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Just, you know, you could elaborate on that.

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You could certainly improve this function here

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because if you concatenate many sentences that actually exceed the max sequence size of the model,

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then it gets truncated and you lose information.

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So if you want to chunk this bit of text into,

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let's say, 512 tokens, that would certainly yield a better summary.

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Okay, exercise for the reader.

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Okay, so that's what we do here.

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So let's just try it.

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And let's just start from...

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We'll take one of those 10 Qs and point at the management discussion section.

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And we'll try to find...

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Let's run this.

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We'll try to find the non-neutral sentences with a score higher than .95.

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Okay?

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So we can see even this section is already quite long, right?

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over 40k characters and 174 sentences.

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So that's a lot of text, right?

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And I've got nine highly positive sentences and nine highly negative sentences.

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Okay, so maybe we could just take a look at those, by the way.

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So why don't we do this?

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Let's look at the negative ones.

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Whoops.

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Here we go.

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Okay, so yeah, these assumptions about future disposition of inventory are inherently uncertain.

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Yeah, that's all good.

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Liquidity is also affected by restricted cash balances, blah, blah, blah.

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Changes in foreign currency exchange rates impacted net sales by 814 million.

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Okay, so these are all pretty negative things, okay?

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So now, as I've said before, we're going to concatenate all the negative sentences, all the positive sentences, and we're going to summarize.

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So let's just go and run this.

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And here I'm running this on a GPU instance in SageMaker Studio,

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because that will just run faster.

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Okay, and it's done.

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So the positive summary is, as we utilize our federal net operating losses and tax credit, we expect cash paid for taxes to increase.

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So you could argue that's actually positive.

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Yeah, they're using credits, which is probably good, but taxes increase, right?

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Nothing's free.

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AWS sales increased 37%.

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Okay, so I'm guessing that's good news.

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And the negative summary is change in foreign exchange rates impacted sales.

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Decrease in North America operated income is primarily due to increased marketing expenses.

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Ha ha!

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Marketing expenses.

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Increase in international operating loss is due to increase in marketing expense at sales.

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Okay.

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All right.

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So these are negative things.

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And you can control the output, of course, with the minimum length and the maximum length of the summarizer.

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Okay, so now let's try the emotion detection model and see what it picks up.

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So just remove this one now.

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Okay, just run this one, run this one.

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Yes.

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yeah this is a very high threshold with uh six or seven emotions so maybe i need to uh

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lower it a little bit let's see what it picks up

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yeah it didn't pick up much so let's just say maybe point i don't know 0.7

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is there any angry sentence in here oh yeah all right oh

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we have a lots of joyful sentences who thought

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we could have thought

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you know amazon reports would be joyful interesting uh yeah let's look at the angry ones now i'm curious

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We are also currently subject to tax controversies in various jurisdictions.

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Okay.

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Developments in an audit investigation or other tax controversy could have a material effect on our operating results.

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Okay.

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I will refrain from any comment here.

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But this is pretty funny, I think.

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Okay, so why don't we summarize that stuff and see what happens?

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Okay, so summarizing anger.

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Yeah, we only have two sentences, so I'm guessing we'll see.

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It does look like a summary.

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Tax controversy could have a material impact on our results.

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Yeah, it's actually not in here.

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Yeah, okay, so that's a good summary.

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yeah so joy um what do we have uh so fear and joy and sadness

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okay

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so see we have yeah the input for fear is all

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is higher than the maximum token length for the model

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so this is where you know we'd want to chunk that fear bit into uh 512 blocks and summarize

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each one of them but you know i didn't go

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that far okay yeah so joy again is probably too long to be properly summarized

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and sadness gives us this guidance anticipates an unfavorable impact of approximately 30 basis points for foreign exchange rates okay

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so that's yeah that's the good one that's a good one that's definitely not a happy fact okay

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so as you can see you know you need to tweak this a little bit

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and probably do a little more processing on the text um

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and and and chunk it to get a better summary but this isn't too bad for just a few lines of code.

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So that's pretty much what I wanted to show you today.

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So if you thought it was crazy difficult to go and build a solution that analyzes SSD filings,

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it's actually not.

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a lot of the again a lot of the heavy lifting is done by this jumpstart SDK

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and then you get your CSV file and you can just grab models

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and these are off the shelf I didn't fine-tune anything here

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and just start processing that stuff right

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and of course you could you could even train your own language model on that right

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if you download enough enough data about it yeah you could do I have to say it gets big really quickly.

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I've downloaded, I think, a full year of 10Ks and 10Qs for all S&P 500 companies.

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So that's about, yeah, that's a few thousand documents, and it's almost already a gigabyte of data.

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So if you want to go back in time and download, I don't know, 20 years, it's going to be a long download,

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which you probably need to break into

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different jobs because you'll be throttled

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and yeah it's going to be a lot of data

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but yeah this data is out there you can process it very easily and

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so you can build pretty cool models maybe we'll see more

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financial models on the hogging face up thanks to this right and

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if you need help ping me okay all right

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i'll put all the links in the video description i hope this was informative and fun who knows for ICC filings.

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I'll see you soon with more content.

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Until next time, have fun, keep learning.

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Bye bye!

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Wortschatz und Sprechhinweise zu dieser Lektion

Diese Sprechübung basiert auf dem Video „Analyze SEC filings with Transformers for fun and profit“. Diese Wörter kommen am häufigsten vor: document, sentence, filing, section, text. Dieses Video enthält 310 Sätze und 3365 Wörter zum Nachsprechen. Der gesprochene Teil dauert 25:46. Der Sprecher spricht gleichmäßig mit etwa 131 Wörtern pro Minute – ein angenehmes Tempo fürs Shadowing. 82 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen.

Wichtiger Wortschatz in diesem Video

15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:

Wort	Aussprache	Bedeutung
filing Substantiv	/ˈfaɪ.lɪŋ/	Span
summary Substantiv	/ˈsʌm.ə.ɹi/	Zusammenfassung
emotion Substantiv	/ɪˈməʊ.ʃən/	Gefühl, Empfindung f Emotion
bucket Substantiv	/ˈbʌkɪt/	Eimer, Kübel
extract Substantiv	/ˈɛkstɹækt/	Auszug
notebook Substantiv	/ˈnoʊtˌbʊk/	Notizbuch, Notizheft
column Substantiv	/ˈkɒləm/	Säule
dictionary Substantiv	/ˈdɪk.ʃəˌnɛ.ɹi/	Wörterbuch, Wärterbüach
tune Substantiv	/tjuːn/	Melodie
controversy Substantiv	/kənˈtɹɒv.ə.si/	Kontroverse, Meinungsverschiedenheit
expense Substantiv	/ɪkˈspɛns/	Ausgabe, Aufwand
detection Substantiv	/dɪˈtɛk.ʃən/	Detektion
sentiment Substantiv	/ˈsɛn.tɪ.mənt/	Gefühl
sadness Substantiv	/ˈsædnəs/	Traurigkeit
chunk Substantiv	/t͡ʃʌŋk/	Stück, Einzelteil

Phrasal Verbs, die du hören wirst

Wort	Aussprache	Bedeutung
break down Verb		versagen, den Geist aufgeben
pick up Verb		aufheben, aufnehmen
put together Verb		zusammenfügen
set up Verb	/ˌsɛt ˈʌp/	aufbauen, für den Einsatz vorbereiten
turn out Verb		sich herausstellen als/dass, sich erweisen als

Sätze zum Wiederholen

Kurze, vollständige Sätze aus dem Video, die du im Alltag wiederverwenden kannst:

I've broken down my example in two notebooks.
Because you don't want to run those regular expressions.
Good thing I didn't have to write that.
We'll give it a go.
So why don't we do this?

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

Struktur	Im Video
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist	have actually built · have done · you've built
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut	are actually integrated · is based · being throttled
Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache	edgar which is · log, which is · notebook, which is

Aussprache, auf die du achten solltest

Der Sprecher verwendet 90 Kurzformen und abgeschwächte Formen, zum Beispiel I'm, we'll, I've. Sprich sie in der kurzen Form, so wie du sie hörst.

Die „th“-Laute: threshold /ˈθɹɛʃ(h)oʊld/, throttle /ˈθɹɔ.tl̩/
Die Laute „sh“ und „zh“: emotion /ɪˈməʊ.ʃən/, dictionary /ˈdɪk.ʃəˌnɛ.ɹi/, detection /dɪˈtɛk.ʃən/, classification /ˌklæsɪfɪˈkeɪʃən/, threshold /ˈθɹɛʃ(h)oʊld/
Lange Wörter – auf die Betonung achten: dictionary /ˈdɪk.ʃəˌnɛ.ɹi/, controversy /kənˈtɹɒv.ə.si/, classification /ˌklæsɪfɪˈkeɪʃən/, primarily /ˈpɹaɪ.mə.ɹə.li/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/

Laute, die Deutschsprachigen schwerfallen:

/w/ — mit runden Lippen, nicht wie das deutsche „w“ (/v/): sequence /ˈsiː.kwəns/, liquidity /lɪkˈwɪdəti/, tweak /twiːk/, whoop /wuːp/
Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: hug /hʌɡ/, threshold /ˈθɹɛʃ(h)oʊld/, columns /ˈkɑləmz/, analyze /ˈæn.əˌlaɪz/, retrieve /ɹɪˈtɹiːv/
/æ/ — offener als „ä“: extract /ˈɛkstɹækt/, sadness /ˈsædnəs/, anger /ˈæŋɡə/, classification /ˌklæsɪfɪˈkeɪʃən/, adapt /əˈdæpt/

So übst du mit diesem Video

Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
Sprich Satz für Satz in normaler Geschwindigkeit nach und wiederhole jeden, bis dein Rhythmus zum Sprecher passt.
Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie filing, summary, emotion.

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Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

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Wortschatz und Sprechhinweise zu dieser Lektion

Wichtiger Wortschatz in diesem Video

Phrasal Verbs, die du hören wirst

Sätze zum Wiederholen

Grammatik in diesem Video

Aussprache, auf die du achten solltest

So übst du mit diesem Video

Was ist die Shadowing-Technik?