Speaker 1: Welcome to the deep dive, where we slice through the
Speaker 1: information clutter to bring you the clearest, most important insights. Today,
Speaker 1: we're taking a bit of a shortcut to becoming well
Speaker 1: informed about a really powerful tool in natural language processing NLP.
Speaker 2: It's called Spacey, that's right, and it's an interesting one.
Speaker 2: If you think of those huge language models, you know,
Speaker 2: like chat, GPT, maybe is a big powerful food processor. Okay,
Speaker 2: then Spacey is more like your practical, really well optimized
Speaker 2: kitchen knife. It's a library that's specifically designed to help
Speaker 2: you get actual work done.
Speaker 1: So moving beyond just theory.
Speaker 2: Exactly beyond just academic concepts and do efficient practical application.
Speaker 2: And we're going to uncover some surprising depth today. I
Speaker 2: think from you know, basic text processing right up to
Speaker 2: integrating with the latest AI stuff.
Speaker 1: Sounds good, And our mission for this deep dive is
Speaker 1: basically to give you a comprehensive but still really accessible
Speaker 1: understanding of what Spacey can do. We're drawing from quite
Speaker 1: a few sources, including the excellent book Mastering Spacey. Okay,
Speaker 1: let's untack this. So to kick us off, what's the
Speaker 1: absolute core thing our listeners should get about Spacey.
Speaker 2: Well at its heart. Spacey is this incredibly fast open
Speaker 2: source Python library, and it's really built for production ready
Speaker 2: NLP applications.
Speaker 1: Production ready. That sounds important.
Speaker 2: It is, a lot of its speed comes from using
Speaker 2: Python for the really performance critical bids, so it's highly
Speaker 2: optimized but still easy to use within Python.
Speaker 1: Aha. So it's not just another like academic tool set.
Speaker 1: It's built for real world stuff from the.
Speaker 2: Get go precisely. That's a key difference compared to maybe
Speaker 2: something like NLTK, the Natural Language Toolkit, which historically at
Speaker 2: least was often more focused on students researchers. Spacey, you're
Speaker 2: hitting the ground running for deployment.
Speaker 1: You mentioned it's built to get work done? Is that
Speaker 1: like the official philosophy pretty much?
Speaker 2: Inus Montani? What are the core? Creators? Often talks about this.
Speaker 2: The goal is genuinely to help people do their work efficiently.
Speaker 2: They're not trying to build some massive do everything system.
Speaker 2: Oh okay, it's more about providing these sharp, reliable tools
Speaker 2: like that knife, to fit nicely into whatever you're already doing.
Speaker 1: Got it and getting started? Is it complex?
Speaker 2: Not? Really? It works with modern Python runs on you know,
Speaker 2: the usual operating systems, Windows, Mac, Linux.
Speaker 1: And best practice is probably virtual environments. Right.
Speaker 2: Keep things clean, Oh, absolutely, always a good idea for
Speaker 2: any Python project. Keeps your dependency sorted.
Speaker 1: Now you mentioned something important. The language models aren't built
Speaker 1: in correct.
Speaker 2: That's a key point. Spacey itself is the framework, the tools,
Speaker 2: but for the statistical smarts, things like tagging parts of
Speaker 2: speech or finding named entities, you need to download a
Speaker 2: language model separately.
Speaker 1: Like Encore websism, that kind of thing exactly like encore
Speaker 1: webdism for English.
Speaker 2: This quick command line thing Python dash M, Spacey download
Speaker 2: oncre webism that downloads the small English model, gets you
Speaker 2: the core pipeline components.
Speaker 1: Okay, and once you've got that, how do you sort
Speaker 1: of of see what it's doing.
Speaker 2: Oh well, that's where displacy comes in. It's Spacey's built
Speaker 2: in visualization tool, and it's fantastic. How So, it just
Speaker 2: makes really complex linguistic concepts much easier to grasp visually.
Speaker 2: You could see dependency parses how words connect, or see
Speaker 2: named entities highlighted right in the text. It helps you
Speaker 2: spot patterns almost.
Speaker 1: Instantly, so you can actually see the analysis.
Speaker 2: Yeah, you can try it online. There's a demo, or
Speaker 2: you can run it locally from your code. Even in
Speaker 2: Jupiter notebooks. It's super helpful for understanding what's going on
Speaker 2: into the hood.
Speaker 1: Okay, so set up, done, model, downloaded, visualization. Ready, let's
Speaker 1: talk about the core processing. You mentioned a pipeline.
Speaker 2: Yeah, I think of it like an NLP assembly line.
Speaker 2: When you load a model, say using spacey dot load,
Speaker 2: you get back this NLP object, right, And when you
Speaker 2: feed text into that object like doc NLP, this is
Speaker 2: some text. It runs the text through a sequence of
Speaker 2: processing steps.
Speaker 1: The pipeline components exactly.
Speaker 2: The default pipeline usually include. It's a tokenizer, a tagger
Speaker 2: for part of speech, a dependency parser for sentence structure,
Speaker 2: and an entity recognizer or any R component. Each does
Speaker 2: its specific.
Speaker 1: Job, and the output is this doc object.
Speaker 2: Right. The doc object holds the result. It's not just
Speaker 2: the text, it's the text broken down into tokens, and
Speaker 2: each token is enriched with all the linguistic features found
Speaker 2: by the pipeline.
Speaker 1: Let's break down that pipeline. First up, Tokenization and sentence
Speaker 1: segmentation sounds simple, just splitting words, Ah.
Speaker 2: Well, it's a bit more nuanced than just splitting on spaces.
Speaker 2: Tokenization is breaking the text into its smallest meaningful parts,
Speaker 2: the tokens, words, numbers, punctuation. They all become tokens. Okay,
Speaker 2: But here's a surprising detail. Unlike most other pipeline components,
Speaker 2: the default tokenizer doesn't rely on a statistical model.
Speaker 1: Oh which does it use?
Speaker 2: It uses really carefully crafted language specific rules, which makes
Speaker 2: it very fast and predictable. And you can even customize it.
Speaker 2: You can add special cases like telling it how to
Speaker 2: handle slang or specific abbreviations.
Speaker 1: Let's teach it lemmey should be lemon me exactly.
Speaker 2: That kind of thing gives you fine grain control.
Speaker 1: And sentence segmentation, finding sentence boundaries that's.
Speaker 2: Actually often more complex than tokenization. Think about abbreviations like
Speaker 2: misder or complex punctuation. Spacey has a unique approach here.
Speaker 2: What's that It often uses the dependency parser, which understands
Speaker 2: sentence structure to help figure out sentence boundaries really accurately.
Speaker 2: It's quite a sophisticated design choice.
Speaker 1: Interesting. Okay, Next step lematization getting the root word yep.
Speaker 2: The lemma is the base or dictionary form. So like
Speaker 2: you said, eating eats eat tape, they all boil down
Speaker 2: to the lemma eat.
Speaker 1: How useful is that in practice?
Speaker 2: Oh, incredibly useful. Think about a chatbot for booking flights.
Speaker 2: A user might say I want to fly, or show
Speaker 2: me flights or I flew yesterday.
Speaker 1: Right, different forms of the same core idea exactly.
Speaker 2: Lemonization reduces fly flights flu all down to fly, so
Speaker 2: your system only needs to look for that one base
Speaker 2: form to understand the core intent. It simplifies things massively.
Speaker 1: Makes sense, and you could use it for other things too,
Speaker 1: like place names.
Speaker 2: Definitely maybe sometimes angel Town when they mean Los Angeles.
Speaker 2: You can actually add custom rules using something called an
Speaker 2: a tribune ruler to map Angeltown to the canonical Los
Speaker 2: Angeles lemma. During processing insures consistency.
Speaker 1: So Spacey processes the text, applies these steps and stores
Speaker 1: the results you mentioned. Container objects, doc, token span.
Speaker 2: Right, these are your main ways of accessing the processed information.
Speaker 2: The doc object represents the whole processed text. Okay, if
Speaker 2: you loop over a doc like for token and doc,
Speaker 2: you get individual token objects.
Speaker 1: And each token knows things about itself.
Speaker 2: Loads of things. A token object holds the original word,
Speaker 2: it's lemma, it's part of speech tag, it's dependency relation.
Speaker 2: It also has boolean flags like token dot is punk,
Speaker 2: token dot is currency token dot like earl, token dot
Speaker 2: latham wow.
Speaker 1: Okay, So you can check if a token looks like
Speaker 1: a URL or a number easily yep.
Speaker 2: And it knows it's entity type if it's part of
Speaker 2: one like token do type might be person or or worg.
Speaker 2: It even has a token dot shave attribute that gives
Speaker 2: you a kind of abstract representation of the words orthography,
Speaker 2: like is it capitalized, is it all digits, et cetera.
Speaker 2: Really useful for rule base matching.
Speaker 1: And span What does that fit in?
Speaker 2: A span? Is just a slice of the dock representing
Speaker 2: multiple tokens. Sentences are span objects. You can get them
Speaker 2: via doc dot sense. Named entities are also span objects,
Speaker 2: accessible via doc dot NZ. So doc token span or
Speaker 2: how you navigate and use the process.
Speaker 1: Text got it. Let's move into some of those linguistic
Speaker 1: features part of speech tagging pos tagging. That's identifying nouns, verbs, adjectives.
Speaker 2: Exactly, categorizing words by their grammatical role in the sentence.
Speaker 1: And how does space you figure that out? Is it
Speaker 1: just a dictionary look up?
Speaker 2: Oh no, it's much smarter than that. It looks at
Speaker 2: the word in context. The surrounding words heavily influence the tag.
Speaker 2: It uses sequential statistical models trained on large amounts of texts.
Speaker 1: So the same word could get different tags.
Speaker 2: Absolutely. Think of the word book, I read a book
Speaker 2: noun versus I want to book a flight verb. The
Speaker 2: context tells the tagger which role it's playing.
Speaker 1: And why is this useful beyond just grammar?
Speaker 2: Well, it's really important for understanding meaning, especially for word
Speaker 2: sense disambiguation, figuring out which meaning of a word is intended.
Speaker 1: Can you give an example?
Speaker 2: Sure, take the word beat. It can mean many things,
Speaker 2: But if the pos tagger confidently tags it as an
Speaker 2: adjective adj, as in I'm totally beat, you know, it
Speaker 2: almost certainly means exhausted. Ah.
Speaker 1: I see. The tag helps narrow down the meaning.
Speaker 2: Precisely, even if the verb or noun tags might still
Speaker 2: be ambiguous. Beat the drum versus follow the beat. The
Speaker 2: adjective tag is often quite specific. It adds a layer
Speaker 2: of understanding, even if lamonization kind of flattens out things
Speaker 2: like verb tense.
Speaker 1: Okay, that makes sense. Next up, dependency parsing. This sounds
Speaker 1: a bit more complex. Mapping sentence relationships.
Speaker 2: It is complex but incredibly powerful. Dependency parsing represents the
Speaker 2: grammatical structure of a sentence not just as a flat sequence,
Speaker 2: but as a tree of relationships. It shows how words
Speaker 2: depend on each.
Speaker 1: Other head and dependent exactly each.
Speaker 2: Word except usually the main verb. The root has a
Speaker 2: head word it modifies or relates to, and a specific
Speaker 2: dependency label describes that relationship, like N subject phenomenal subject,
Speaker 2: or dubject for direct object. Why go to all this trouble, Well,
Speaker 2: what's fascinating here is that sentences aren't just sequences of tokens.
Speaker 2: They have this deep, inherent structure, and understanding that structure
Speaker 2: is absolutely crucial for many real world NLP tasks, like
Speaker 2: what think about chatbots or a machine translation? You need
Speaker 2: to know who did what to whom. Consider I forwarded
Speaker 2: you the email versus you forwarded me the email.
Speaker 1: Same words, totally different meaning exactly.
Speaker 2: Dependency parsing helps the system figure out that I is
Speaker 2: the subject the one doing the forwarding in the first sentence,
Speaker 2: and you as the subject in the second. It disambiguates
Speaker 2: the roles based on the grammatical structure unsubject, DUBJIOJ relationships.
Speaker 2: Without that, I understand user intent would be much much.
Speaker 1: Harder, right, That makes the importance clear. Okay, what about
Speaker 1: named entity recognition any R spotting real world objects?
Speaker 2: Yep. A named entity is basically anything that can be
Speaker 2: referred to with a proper name or a quantity. So
Speaker 2: people's names, company names, locations, dates, monetary values, percentages.
Speaker 1: The categories seem pretty standard person or or GPE geopolitical entity.
Speaker 2: Those are common ones, yes, but the specific set of
Speaker 2: entity types is actually quite flexible and often depends on
Speaker 2: the data of the model was trained on or the
Speaker 2: specific task you have in mind. How so, Well, if
Speaker 2: you're analyzing financial news, entities like money and percentage might
Speaker 2: be way more important and frequent than say, work of art.
Speaker 2: The model needs to be tailored or chosen based on
Speaker 2: the domain.
Speaker 1: And how good as any are these days.
Speaker 2: It's gotten incredibly good. The state of the art methods
Speaker 2: often use those transformer architectures we mentioned earlier. They're very
Speaker 2: effective at understanding context to identify entities accurately.
Speaker 1: Okay, And sometimes the default tokenization or entity spans might
Speaker 1: not be quite right. Can you fix them?
Speaker 2: Yes? Absolutely. Spacey provides a really neat mechanism called doc
Speaker 2: dot retokenize it lets you merge multiple tokens into one,
Speaker 2: or split a single token into several.
Speaker 1: Why would you need to do that?
Speaker 2: Well, maybe an entity like New York City got split
Speaker 2: into three tokens, but you want to treat it as
Speaker 2: a single unit for analysis, you can merge them. Or
Speaker 2: maybe a typo resulted in San Francisco being one token
Speaker 2: and you want to split it.
Speaker 1: Ah okay, So for cleanup and normalization.
Speaker 2: Exactly, merging is usually simpler. Splitting can be a bit
Speaker 2: more involved because Spacey then needs to figure out the
Speaker 2: linguistic features and dependencies for the new tokens you've created.
Speaker 2: But it's a very powerful tool for practical adjustments.
Speaker 1: Let's shift gear slightly to rule based matching. You mentioned
Speaker 1: regular expressions can be tricky. What Spacey's alternative.
Speaker 2: Spacey offers the matriclass, and it's designed to be a well,
Speaker 2: a much cleaner, more readable, and definitely more maintainable alternative
Speaker 2: for finding patterns and text compared to rejects.
Speaker 1: Why is rejects problematic?
Speaker 2: Regular expressions can just become incredibly dense and hard to read,
Speaker 2: especially for complex patterns. They're also easy to get subtly wrong,
Speaker 2: which can lead to bugs that are hard to track down,
Speaker 2: and they operate purely on.
Speaker 1: Strings, and the match is different how.
Speaker 2: The matcher works with token objects and their attributes. You
Speaker 2: define patterns not as strings, but as lists of dictionaries,
Speaker 2: where each dictionary specifies the attributes.
Speaker 1: A token must have like low to match the word
Speaker 1: hello regardless.
Speaker 2: Of case precisely, or is punched true to match any
Speaker 2: punctuation mark or liken them true for number. Like tokens,
Speaker 2: you're matching based on linguistic features, not just character sequences.
Speaker 1: That sounds much more robust.
Speaker 2: It is, and you can use extended syntax too. You
Speaker 2: can match based on token length length check off a
Speaker 2: token is in a list I note or use boolean
Speaker 2: flags like east digit I, sulfa I supper great for finding, say,
Speaker 2: emphasized words in all cans.
Speaker 1: Does it have rejects like operators like optional parts.
Speaker 2: Yes, you can use operators like bunds to make a
Speaker 2: token pattern optional. Think about matching names like Barack Obama
Speaker 2: but also Barack Hussein Obama. The middle name token can
Speaker 2: be marked as optional, and you have operators like plus
Speaker 2: one or more and zero or more for specifying occurrences,
Speaker 2: similar to rejects. There's even a really useful online demo
Speaker 2: on the Spacey website where you can build and test
Speaker 2: matcher patterns interactively.
Speaker 1: Okay, that covers matching specific patterns. What if you have
Speaker 1: like a huge list of things to find, say thousands
Speaker 1: of product names, right.
Speaker 2: Creating individual matcher patterns for thousands of specific phrases would
Speaker 2: be well, not very efficient or practical.
Speaker 1: So what's the solution for that?
Speaker 2: Spacey provides the phrase matcher. It's optimized specifically for efficiently
Speaker 2: scanning text against large lists of multi word phrases or dictionaries.
Speaker 1: How does that work?
Speaker 2: You give it a list of doc objects representing the
Speaker 2: phrases you want to find, like Angela Merkele, Donald Trump,
Speaker 2: Alexis ceparus. It then uses a really efficient algorithm to
Speaker 2: find all occurrences of those exact phrases in your target text,
Speaker 2: much faster than running thousands of individual rules.
Speaker 1: Very useful for terminology lists or gazetteers exactly.
Speaker 2: And it can even match based on token attributes, not
Speaker 2: just the exact words. For instance, you could match based
Speaker 2: on the shape attribute, which is handy for finding structured
Speaker 2: data like IP addresses or specific code patterns and log files.
Speaker 2: Even if the exact digits change.
Speaker 3: So you have the matcher for flexible patterns and phrase
Speaker 3: matcher for large lists. How do you integrate these findings
Speaker 3: back into the main spacey doc. That's where the span
Speaker 3: ruler comes in. It's a pipeline component that lets you
Speaker 3: use rules to find very similarly to matcher patterns, to
Speaker 3: directly add span objects to your doc add themwhare to
Speaker 3: doc dot sense. You can configure it to add them
Speaker 3: to doc dot en, so effectively adding rule based named entities.
Speaker 3: Or you can have it add them to a custom
Speaker 3: span group like doc dot spans my custom patterns, so.
Speaker 1: You added to the pipeline like other components.
Speaker 2: YEP, NLP, dot X a pipe span ruler. Then you
Speaker 2: provide it with your patterns. For example, you could define
Speaker 2: a pattern to find every instance of the word chime
Speaker 2: and label it as an OARG entity.
Speaker 1: What if the regular ner model also finds entities? Do
Speaker 1: they clash?
Speaker 2: Good question. You can configure the span ruler. You can
Speaker 2: tell it whether your rule based entities should overwrite entities
Speaker 2: found by the statistical ner model, overrit true or not
Speaker 2: overwrite falls. You can also set it up so that
Speaker 2: statistical entities don't overwrite your rule based ones gives you
Speaker 2: control over which source of entities takes precedence.
Speaker 1: Okay, this rule based stuff seems really practical. Can we
Speaker 1: talk about some specific recipes like real world extraction examples?
Speaker 2: Absolutely, here's where it gets really interesting, showing Spacey's power.
Speaker 2: So you can easily build patterns to extract things like ibands,
Speaker 2: international bank account numbers, or phone numbers, these highly structured
Speaker 2: numeric things.
Speaker 1: Okay, what else?
Speaker 2: Think about? Social media? Could create patterns to find mentions
Speaker 2: expressing opinions, like matching the sequence business name plus iswaz
Speaker 2: bay plus Maybe an adverb plus an adjective.
Speaker 1: Like finding cafe X was really great.
Speaker 2: Exactly that pattern structure cafex was a adverb adjective. Could
Speaker 2: pick up cafe x is good, Cafe y was very slow,
Speaker 2: restaurant z will be amazing. Helps you gauge sentiment clever.
Speaker 1: Other examples.
Speaker 2: Hashtags are easy. You can match the hashtag symbol followed
Speaker 2: by tokens that meet certain criteria like IC or ICEULFA
Speaker 2: to reliably pull out things like hashtag deep learning or
Speaker 2: hashtag weekend fun.
Speaker 1: And what about slightly more complex entities?
Speaker 2: You can even use patterns to refine entities. For example,
Speaker 2: maybe the ner just picks up Smith as a person.
Speaker 2: You could use a match or pattern to look for
Speaker 2: a preceding title like mister AM's doctor nump, and then
Speaker 2: retokenize to merge the title and the name into a single,
Speaker 2: more complete entity span Miss Smith.
Speaker 1: Wow. Okay, that's quite granular.
Speaker 2: Control, it really is. These rule based tools, combined with
Speaker 2: the linguistic features, give you a lot of power for
Speaker 2: precise information extraction.
Speaker 1: Let's push deeper now into understanding meaning and intent. How
Speaker 1: does spacey help with semantic parsing figuring out what a
Speaker 1: user actually wants a great.
Speaker 2: Way to explore this is with data sets like eighty
Speaker 2: zis the airline travel information system. It contains thousands of
Speaker 2: real user requests about.
Speaker 1: Flights like show me flights from Boston to Denver exactly?
Speaker 2: Or what's the cheapest flight? What meals are served on
Speaker 2: flight x? Analyzing these requires understanding not just the words,
Speaker 2: but the underlying goal.
Speaker 1: Where do you even start with something like that?
Speaker 2: Well, a really crucial first step, honestly, is just looking
Speaker 2: at the data yourself. Read through a sample of the utterances,
Speaker 2: get a feel for the common patterns. The types of
Speaker 2: entities involved the grammar people use.
Speaker 1: What kind of things would you look for in the
Speaker 1: eightiest data.
Speaker 2: You'd quickly notice people specifying origins and destinations. But it's
Speaker 2: not enough just to spot Boston and Denver. You need
Speaker 2: to capture the relationship from Boston to Denver. You'd see
Speaker 2: the importance of prepositions like from to in Those little
Speaker 2: words carry a lot of semantic.
Speaker 1: Weight, So you need more than just finding keywords.
Speaker 2: Definitely, you need to understand the relationships between the words.
Speaker 2: And that's where Spacey's dependency matter.
Speaker 1: Comes in another matcher. How's this one different?
Speaker 2: Well, the matcher looks for sequences of tokens based on
Speaker 2: their attributes. The dependency match looks for patterns based on
Speaker 2: the syntactic dependency relationships between tokens.
Speaker 1: Ah. Using that dependency parstry we talked about earlier.
Speaker 2: Precisely, it lets you find patterns like a verb connected
Speaker 2: to a noun with a direct object relationship dub J.
Speaker 2: This is key for identifying intent.
Speaker 1: Can you give a quick linguistic primer on that objects?
Speaker 2: Sure? So? Very Basically, you have transitive verbs which need
Speaker 2: an object to act upon, like I bought flowers flowers
Speaker 2: is a direct object, and in transitive verbs which don't
Speaker 2: like I slept okay. And sometimes there's an indirect object too,
Speaker 2: like I gave him the book book is direct him
Speaker 2: as direct. The dependency matcher lets you specify these relationships
Speaker 2: in your patterns.
Speaker 1: How does that help find intent in the flight examples.
Speaker 2: Well, you could define a pattern looking for a verb
Speaker 2: like show or find that has a direct object TOBJ
Speaker 2: like flights. That pattern defined using dependency relations would match
Speaker 2: show me flights, find flights, I need you to show flights, etc.
Speaker 2: Capturing the core intent regardless of the exact phrasing.
Speaker 1: That seems much more robust than just keyword spotting.
Speaker 2: It is, and you can build more complex patterns. What
Speaker 2: if someone says, show all flights and fares. The dependency
Speaker 2: matcher can use the conjunct dependency link between flights and
Speaker 2: fares to recognize that the user has two related intents
Speaker 2: connected by and.
Speaker 1: Okay, that's powerful, But this raises a question. Once you've
Speaker 1: used these matchers to figure out the intent, say book flight,
Speaker 1: how do you store that information with the doc?
Speaker 2: Great question. You don't want that information just floating around
Speaker 2: Spacey has a mechanism for this extension attributes exten attributes. Yeah,
Speaker 2: you can define your own custom attributes on doc token
Speaker 2: or span objects. So you could create an attribute called
Speaker 2: say doc dot intent. The underscore indicates it's a custom
Speaker 2: extension and how.
Speaker 1: Do you set that attribute?
Speaker 2: You typically create a custom spacey pipeline component use a
Speaker 2: special decorator at language dot factory to define it. Inside
Speaker 2: this component's call method, which processes the doc. You'd run
Speaker 2: your matcher or dependency matcher, figure out the intent and
Speaker 2: then set doc dot intent.
Speaker 1: Book flight so you can tailor the pipeline to extract
Speaker 1: and store exactly what you need exactly.
Speaker 2: It makes spacing incredibly flexible and extensible for specific tasks.
Speaker 1: Now we touched on performance earlier. What about processing large
Speaker 1: data sets like the full eight is corpus with thousands
Speaker 1: of utterances. Doing them one by one sounds slow.
Speaker 2: It would be processing doc NLP text for each of
Speaker 2: the four nine and seventy eight utterances individually would take
Speaker 2: quite a while.
Speaker 1: So what's the efficient way?
Speaker 2: The key is the NLP dot pipe method or language
Speaker 2: dot pipe. If you're using the base class.
Speaker 1: How does pipe help?
Speaker 2: It processes the text as a stream and crucially it
Speaker 2: buffers them internally and processes them in batches. This allows
Speaker 2: Spacey to leverage optimizations and parallel processing much more effectively.
Speaker 1: And the speed difference is noticeable.
Speaker 2: Oh, absolutely dramatic. The sources mentioned going from something like
Speaker 2: twenty seven seconds for processing the eighties data set one
Speaker 2: by one down to under six seconds using NLP dot pipe.
Speaker 2: It's the standard way to process large volumes of text efficiently.
Speaker 1: Okay, essential for any real world application. Let's pivot now
Speaker 1: to the really cutting edge stuff, transformers and large language
Speaker 1: models LMS. The transformer architecture kind of kick things off right.
Speaker 1: The attention is all you need paper.
Speaker 2: Yes, that twenty seventeen paper was a landmark. Transformers really
Speaker 2: revolutionized NLP.
Speaker 1: What problem were they trying to solve?
Speaker 2: Well, Previous models like LSTMs process texts sequentially. This meant
Speaker 2: they could struggle with long range dependencies for getting information
Speaker 2: from the beginning of a law text, and they weren't
Speaker 2: easily parallelizable, which limited training speed.
Speaker 1: And transformers fix this how with attention exactly.
Speaker 2: The core innovation is the self attention mechanism, often implemented
Speaker 2: in a multi head attention block. Instead of just looking
Speaker 2: at the immediately preceding words. Self attention allows the model
Speaker 2: to weigh the importance of all words and the input
Speaker 2: sequence when calculating the representation for a single word.
Speaker 1: So it looks at the whole context at once.
Speaker 2: Sort of yeah. It calculates a words embedding its representation
Speaker 2: by taking a weighted average of the embeddings of all
Speaker 2: other words in the sequence, where the weights the attention
Speaker 2: scores indicate relevance. This lets it understand language much more
Speaker 2: deeply in context.
Speaker 1: What was the big aha moment with this?
Speaker 2: A major one was that transformers could generate dynamic word vectors.
Speaker 2: Older methods like word two VAC gave the same vector
Speaker 2: for bank every time, but a transformer can understand a
Speaker 2: context and give a different vector for bank and riverbank
Speaker 2: versus bank in investment bank.
Speaker 1: That's a huge leap in understanding nuance.
Speaker 2: It really was. And libraries like Hugging Faces Transformers Library
Speaker 2: now provide access to literally thousands of these pre trained
Speaker 2: transformer models.
Speaker 1: How does Spacey integrate with these? Can you use transformers
Speaker 1: within a Spacey pipeline?
Speaker 2: Yes? Absolutely. A great example is text classification. Let's say
Speaker 2: you want to classify Amazon product reviews as positive or negative.
Speaker 2: You can use Spacey's text categorizer component.
Speaker 1: Which is trainable.
Speaker 2: Right, it's a trainable pipeline component. You'd prepare your training
Speaker 2: data the reviews labeled as positive or negative using Spacey's
Speaker 2: example object, and then serialize it efficiently using doc ben.
Speaker 1: How do you manage the training process itself?
Speaker 2: Spacey has a really nice configuration system. Instead of hard
Speaker 2: coding parameters, you define everything the pipeline components, model settings,
Speaker 2: hyper parameters, data paths in a single configuration file configured
Speaker 2: on CFG.
Speaker 1: Why is that better?
Speaker 2: It makes your experiment incredibly reproducible. Yeah, there are no
Speaker 2: hidden de faults. Everything is explicit in the config file.
Speaker 2: You can then train your pipeline directly from the command
Speaker 2: line using spacey train.
Speaker 1: And can you include a transformer in that pipeline for
Speaker 1: text classification?
Speaker 2: Yes, you can configure the pipeline to include a transformer component.
Speaker 2: This component generates those context to wear embeddings we talked about,
Speaker 2: which are then fit into the text categorizer. Often, adding
Speaker 2: a transformer significantly boosts the accuracy of the classifier because
Speaker 2: it has a richer understanding of the text's meaning and sentiment.
Speaker 1: Okay, so let's name some names. What about famous transformer
Speaker 1: models like Bert and Roberta? What makes them special?
Speaker 2: Right? Bert bi directional encoder representations from transformers was a
Speaker 2: huge step. Its key innovation was being bi directional during
Speaker 2: pre training.
Speaker 1: Meaning it looked forwards and backwards in the text simultaneously.
Speaker 2: Yeah. Previous models were often unidirectional or combined separate left
Speaker 2: to right and right to left models. Bert used a
Speaker 2: technique called masked language modeling predicting hidden workds to learn
Speaker 2: context from both directions at the same time. This gave
Speaker 2: it a deeper understanding.
Speaker 1: And it produced those dynamic word vectors.
Speaker 2: Yes. It also used some special tokens like cls at
Speaker 2: the beginning of sequences and stap to the separate sentences,
Speaker 2: and it used word piece tokenization, breaking words into common
Speaker 2: subword units like playing might become play and hashtag in.
Speaker 2: This helps it handle large vocabularies and even words it
Speaker 2: hasn't explicitly seen before.
Speaker 1: What about Roberta? How did that improve on? Burt?
Speaker 2: Roberta developed by Facebook AI basically took the Bert architecture
Speaker 2: and optimized the training procedure. They used things like dynamic masking,
Speaker 2: changing the masked words during training, trained on much more
Speaker 2: data for longer, and removed one of Burd's training objectives
Speaker 2: next sentence prediction finding. It didn't always help. These changes
Speaker 2: generally led to better performance on downstream tasks compared to
Speaker 2: the original Bert models.
Speaker 1: Okay, so transformers are powerful pretrain models. What about the
Speaker 1: even bigger ones, The large language models are llms? How
Speaker 1: do they fit in?
Speaker 2: Lms are essentially an evolution or maybe a scaling up
Speaker 2: of those pre trained language models like bird. We're talking
Speaker 2: models with vastly more parameters two three is one hundred
Speaker 2: and seventy five billion, for instance, trained on absolutely enormous
Speaker 2: amounts of text.
Speaker 1: And code, and they can do well almost anything text related.
Speaker 2: They're incredibly versatile. Yeah, translation, summarization, question answering, code generation,
Speaker 2: creative writing. They've shown promise in specialized field like medicine,
Speaker 2: law education too.
Speaker 1: But they're not perfect, right, What are the downsides?
Speaker 2: Definitely not perfect? There are key limitations. One is this
Speaker 2: year computational cost training and even running them requires massive resources.
Speaker 2: They can also be slower to generate responses compared to
Speaker 2: smaller models, and crucially, they have this tendency to hallucinate.
Speaker 1: Hallucinate meaning they mix stuff up.
Speaker 2: Essentially, Yes, they can generate responses that sound perfectly plausible
Speaker 2: and grammatically correct, but are factually incorrect or nonsensical. They
Speaker 2: don't inherently know things. They're predicting probable sequences.
Speaker 1: Of words, so you need to be careful how you
Speaker 1: use them.
Speaker 2: Very careful. A lot of work goes into prompt engineering,
Speaker 2: carefully crafting the input prompt to guide the LLM towards
Speaker 2: the desired accurate output.
Speaker 1: How does space help manage interactions with llms.
Speaker 2: There's a package called spacelm. It provides a structured way
Speaker 2: to integrate llms into spacey workflows. It treats interactions with
Speaker 2: an LM as defined tasks like summerization or entity.
Speaker 1: Extraction, and it uses prompts.
Speaker 2: Yes, it uses GINGA templates to define the prompts for
Speaker 2: these tasks. You can use built in tasks, or you
Speaker 2: can define your own custom tasks.
Speaker 1: Custom tasks like what.
Speaker 2: For example, the sources mentioned creating a custom task to
Speaker 2: extract specific quotes from a text and the surrounding contact sentences.
Speaker 2: You define the prompt template to ask the LLM for
Speaker 2: this specific output, and you also define how to parse
Speaker 2: the llm's potentially messy response back into a structured format
Speaker 2: that Spacey can use.
Speaker 1: So spacelm provides a bridge and some structure for using
Speaker 1: llms within a more controlled space environment.
Speaker 2: Exactly, it helps make using lllms more systematic and reproducible.
Speaker 1: Let's circle back to training your own models. We talked
Speaker 1: about NR. When would you actually need to train a
Speaker 1: custom ANYR model instead of using a pre trained one.
Speaker 2: That's a common question. The rule of thumb is, if
Speaker 2: a pre trained Spacey model like Encore welding performs reasonably
Speaker 2: well on your data, maybe gets say seventy five percent
Speaker 2: accuracy or higher on the entities you care about, you
Speaker 2: might not need full custom training.
Speaker 1: What would you do then?
Speaker 2: You could potentially find tune the existing model, or more often,
Speaker 2: you'd use other Spacey components like the matcher or span ruler.
Speaker 2: We discussed to add rules that catch the specific cases
Speaker 2: the pre train model misses or gets wrong, kind of
Speaker 2: like augmenting it.
Speaker 1: But when is custom training unavoidable?
Speaker 2: It's usually necessary when your domain has many important entity
Speaker 2: types that are just completely absent from the pre trained models.
Speaker 2: Think about highly specialized fields, specific financial instruments, unique biological
Speaker 2: gene names, custom product codes very niche legal terms. If
Speaker 2: the pre train model doesn't even know these categories exist,
Speaker 2: rules alone won't cut it. You need to teach a
Speaker 2: model from scratch or significantly fine tune one, and that.
Speaker 1: Involves getting data and labeling it exactly.
Speaker 2: Data collection is the first step. Then comes annotation, manually
Speaker 2: labeling examples of your text with the entities, parts of speech, dependencies,
Speaker 2: whatever your model needs to learn.
Speaker 1: Are there tools for that? Annotation sounds tedious?
Speaker 2: It can be, but there are great tools. Prodigy, also
Speaker 2: from the makers of Spacey, is a very modern annotation
Speaker 2: tool that often uses active learning to be more efficient.
Speaker 2: It suggests labels you can firm or correct. They are
Speaker 2: also open source options like Nertwig, which integrates with Jupiter Notebooks.
Speaker 1: Okay, so you annotate your data using one of these tools,
Speaker 1: then what then.
Speaker 2: You convert that annotated data into Space's efficient binary format
Speaker 2: doc ben. You typically split your data into training and
Speaker 2: evaluation sets. Then you use Space's training system Spacey Trained
Speaker 2: with a config file to train your custom and eer component,
Speaker 2: and finally use spacey Evaluate to see how well your
Speaker 2: trained model performs on the unseen valuation data.
Speaker 1: What's fascinating here is the possibility of combining models. Can
Speaker 1: you use your custom trained ner model alongside one of
Speaker 1: Spacey's pre trained ones.
Speaker 2: Yes, and that's often a very powerful approach. You get
Speaker 2: the best of both worlds.
Speaker 1: How does that work technically?
Speaker 2: First, you'd package your custom trained pipeline component, maybe the
Speaker 2: one that recognizes fashion brand entities, into an installable Python
Speaker 2: package using the spacey package command.
Speaker 1: Okay, so it's like distributing your own mini model exactly.
Speaker 2: Then use another command, spacey assemble with a special configuration file.
Speaker 2: This config file tells Spacey how to build a new
Speaker 2: pipeline by sourcing components from different places.
Speaker 1: So you could say, take my custom fashion brand component
Speaker 1: and also take the GPE location and money components from
Speaker 1: the standard encore webs as a model.
Speaker 2: Precisely, Spacey assemble pulls these components together into a single,
Speaker 2: unified a pipeline that can recognize entities from both your
Speaker 2: custom training and the general purpose pre trained model. It's
Speaker 2: a very neat way to create highly specialized, yet broadly
Speaker 2: capable NLP systems.
Speaker 1: Very cool. Let's touch on entity linking. That's about connecting
Speaker 1: mentions in text to actual entries in a knowledge base
Speaker 1: right disambiguating Washington.
Speaker 2: Exactly is Washington referring to George Washington, the person, Washington, DC,
Speaker 2: the city, or Washington state. Entity linking aims to resolve
Speaker 2: that ambiguity by linking the mention to a unique identifier,
Speaker 2: often in a knowledge base like Wikidata or a custom
Speaker 2: company database.
Speaker 1: How does space handle this?
Speaker 2: Spacey has an entity linker component. It's architecture basically involved
Speaker 2: three main parts. First, you need a knowledge base KB.
Speaker 1: What's in the KB?
Speaker 3: It stores information about the entities.
Speaker 2: You want to link to their unique IDs like Wikidata qids, names, descriptions,
Speaker 2: and aliases. Spacey provides tools to create this. For example,
Speaker 2: and in memory lookup KB, you'd add entries for say,
Speaker 2: Taylor Swift, the singer, Taylor Lautner, the act Taylor Fritz,
Speaker 2: the tennis player, each with your unique ID and maybe
Speaker 2: a short description.
Speaker 1: What else is needed besides the KB?
Speaker 2: Second, you need a way to generate candidate entities from
Speaker 2: the KB. For a given mention in the text. If
Speaker 2: the text says tailor, the system needs to know that Swift, Latner,
Speaker 2: and Fritz are all potential candidates. You also add aliases
Speaker 2: with prior probabilities. Maybe Taylor Swift the alias has a
Speaker 2: one hundred percent probability of linking to the singer's ID,
Speaker 2: while just Taylor has an equal chance for all three
Speaker 2: initially and the third part. The third part is a
Speaker 2: machine learning model which is trained to look at the mention,
Speaker 2: its context in the sentence, and the information about the
Speaker 2: candidate entities from the KB and then predict the most
Speaker 2: likely correct link or predict nil if none of the
Speaker 2: candidates seem right.
Speaker 1: Does training this require special data?
Speaker 2: Yes. When you train the entity linker component, your training
Speaker 2: data needs to clearly specify which mentions should link to
Speaker 2: which kb IDs. You often need a custom Corpus reader
Speaker 2: to handle this specific data format during training.
Speaker 1: Okay, we've built all these amazing models and pipelines. How
Speaker 1: do we actually put them into the hands of users
Speaker 1: or other systems. Let's talk deployment. Building apps in APIs right, moving.
Speaker 2: From the lab to the real world. Two popular Python
Speaker 2: frameworks are great for this, with spacey for building interactive
Speaker 2: web applications quickly, especially for not a front end expert.
Speaker 2: Streamlet is fantastic.
Speaker 1: Streamlt How does it work?
Speaker 2: It lets you build web apps purely in Python. You
Speaker 2: can create widgets like textboxes that textavia and buttons very easily.
Speaker 2: There's even a specific package Spacey streamlet that provides ready
Speaker 2: made components to visualize Space's analysis like anyr results directly
Speaker 2: in your streamlet app, so you.
Speaker 1: Could build a quick demo tool for your Spacey pipeline exactly.
Speaker 2: And a key feature is streamlets caching at ffon cash
Speaker 2: or at s don cash data. This prevents your Spacey
Speaker 2: models from having to reload every single time a user
Speaker 2: interacts with the app, which makes it much faster and
Speaker 2: more responsive.
Speaker 1: What if you need something more robust, like a back
Speaker 1: end API that other services can call.
Speaker 2: Then fastpi is an excellent choice. It's a modern, high
Speaker 2: performance Python framework specifically designed for building APIs.
Speaker 1: What makes fast to PI good.
Speaker 2: It's known for its speed. It also leverages Python type
Speaker 2: hints heavily. You define the expected data types for your
Speaker 2: API inputs and outputs, which faster PI uses for automatic
Speaker 2: data validation, gadging errors early, and also for automatically generating
Speaker 2: interactive API documentation using Swagger UI.
Speaker 1: So it makes development faster and more reliable.
Speaker 2: Yes, significantly, you use pidanic models to define your data
Speaker 2: structures and faster PI handles the validation and serialization. You
Speaker 2: could easily build an API in point that takes some text,
Speaker 2: runs it through your spacey ner pipeline, and returns the
Speaker 2: extracted entities as structured Jason data. The autogenerated documentation makes
Speaker 2: it super easy for others or yourself to understand and
Speaker 2: test the API.
Speaker 1: Okay, building models, deploying apps, the whole process can get complex.
Speaker 1: How do you manage the entire end to end workflow,
Speaker 1: especially for reproducibility and collaboration.
Speaker 2: That's where workflow management tools come in. Spacey has a
Speaker 2: companion tool called Weasel. Weasel Yeah, Weasel helps you structure
Speaker 2: your entire NLP project. You define your workload steps like
Speaker 2: downloading data, preprocessing, training, evaluating, along with any data assets
Speaker 2: and custom commands, all within a configuration file project dot EML.
Speaker 2: It makes your project reproducible and easier for others to
Speaker 2: understand and run.
Speaker 1: What about managing the data itself and the models? They
Speaker 1: can get large and change often for that.
Speaker 2: Data Version controlled DVC is an increasingly popular tool, especially
Speaker 2: in the mL world. It works alongside geit. How does
Speaker 2: DVC help it tackles several common problems. First, sharing large
Speaker 2: data sets and models is hard with get alone, DVC
Speaker 2: lets you version your data end models, storing them in
Speaker 2: remote storage like s three year Google Cloud storage, while
Speaker 2: keeping small metaphiles in GIT. This makes collaboration much easier.
Speaker 2: What else, it helps make your data processing and model
Speaker 2: training pipelines relyable and reproducible. You define the steps and
Speaker 2: their dependencies, and DVC can tract everything. It also crucially
Speaker 2: helps with tracking model metrics over time, so you can
Speaker 2: see how performance changes as you modify data or code.
Speaker 2: It really embraces giops principles for mlops, making your mL
Speaker 2: workflows version, automated and continuously reconciled.
Speaker 1: So it brings better engineering practices to data science exactly.
Speaker 2: It helps manage the whole life cycle, and tools like
Speaker 2: DVC studio even provide features like a model registry for
Speaker 2: managing and sharing your train models effectively across a team.
Speaker 2: Weasel and DVC together provide a really solid foundation for
Speaker 2: managing serious NLP projects.
Speaker 1: Wow from the core concepts and pipeline through advanced analysis
Speaker 1: like dependency parsing and NR rule based matching, transformers LMS,
Speaker 1: training custom models, and finally, deployment and workflow management. That
Speaker 1: was an incredibly thorough deep dive into the Spacey ecosystem.
Speaker 2: It really covers a lot of ground, doesn't it.
Speaker 1: It absolutely does. It really reinforces that idea of space
Speaker 1: see being that practical, well optimized kitchen knife, precise, powerful
Speaker 1: and adaptable for so many different NLP tasks.
Speaker 2: Yeah. And you know, if we connect this to the
Speaker 2: bigger picture, really understanding tools like Spacey empowers you not
Speaker 2: just to build things, but also to critically evaluate how
Speaker 2: these language AI systems work, how they understand or misunderstand
Speaker 2: the world.
Speaker 1: That's a great point.
Speaker 2: It kind of raises an important question for anyone listening.
Speaker 2: I think, now that you have this deeper insight, how
Speaker 2: will you use this knowledge, maybe to refine your own analysis,
Speaker 2: or perhaps even to build something new and impactful.
Speaker 1: A fantastic thought to end on. We definitely encourage you,
Speaker 1: our listeners, to keep exploring these fascinating topics and think
Speaker 1: about how you might apply this knowledge, whether it's in
Speaker 1: your work, your studies, or just your own curiosity about
Speaker 1: language and AI.