🔥 NEW LLama Embedding for Fast NLP💥 Llama-based Lightweight NLP Toolkit 💥

Summary

The video introduces Word Lama, a lightweight NLP toolkit that enhances efficiency in natural language processing tasks. It utilizes components from large language models to create compact word representations, significantly reducing model size while maintaining performance. Key features include similarity scoring, document ranking, and fuzzy duplication, making it ideal for business-critical tasks. The video also demonstrates a Gradio application for interactively exploring Word Lama's capabilities.

Takeaways

• 🔍 The script introduces 'Word Lama', a new lightweight NLP toolkit designed for efficiency and compactness in natural language processing tasks.
• 🌟 NLP tasks such as finding sentence similarity, fuzzy duplication, and semantic search are crucial for various business applications and data science projects.
• 📦 Word Lama utilizes components from large language models to create efficient and compact word representations, like GloVe and fastText.
• 🚀 Word Lama's model is substantially smaller compared to other models, with a 256-dimensional model being only 16MB, making it highly efficient for resource-limited environments.
• 🏆 The toolkit improves on the MTB Benchmark, which evaluates embedding models, showcasing its effectiveness against popular models like Sentence-BERT and GloVe.
• 📈 Word Lama offers various functionalities like similarity scoring, re-ranking, and de-duplication, which are essential for tasks such as IT service management and e-commerce.
• 🛠️ The script demonstrates practical applications of Word Lama through a Gradio demo deployed on Hugging Face Spaces, allowing users to interact with the model.
• 📝 The model's small size and speed make it suitable for real-time applications, where quick processing of NLP tasks is necessary.
• 🔧 The script provides insights into the model's training process, mentioning that it was trained on a single A100 GPU for 12 hours, highlighting the model's optimization.
• 🌐 The creator encourages viewers to experiment with the model through the provided Gradio application and share their feedback, promoting community engagement with the project.

Q & A

What is Word Lama and what does it offer?

Word Lama is a lightweight NLP toolkit that provides a utility for natural language processing and word embedding models. It recycles components from large language models to create efficient and compact word representations.

What is the significance of NLP in the context mentioned?

In the context, NLP (Natural Language Processing) involves tasks like finding similarity between sentences, fuzzy duplication, and other language-related tasks. It's crucial for efficiency and accuracy in business-critical applications.

How does Word Lama improve upon existing models?

Word Lama improves upon existing models by offering a smaller, more efficient model that performs well on MTB Benchmark evaluations. It is substantially smaller in size compared to other models like GloVe, making it more suitable for business use cases that require speed and nimbleness.

What is the size difference between Word Lama and GloVe 300D?

Word Lama's model is just 16 MB for 256 dimensions, whereas GloVe 300D is greater than 2GB, making Word Lama significantly smaller and more lightweight.

What are some of the tasks that Word Lama can assist with?

Word Lama can assist with tasks such as similarity finding, semantic search, reranking, classification, clustering, and fuzzy duplication, which are essential for various applications like IT service management and e-commerce.

How does Word Lama utilize components from large language models?

Word Lama extracts the token embedding codebook from a state-of-the-art language model and trains a small contextless model in a general-purpose embedding framework, resulting in a compact and efficient model.

What is the role of embeddings in Word Lama?

Embeddings in Word Lama serve as numerical representations of words that can be used for various NLP tasks. They are created from recycled components of large language models and are optimized for efficiency and compactness.

How can users interact with Word Lama through the Gradio application?

Users can interact with Word Lama through a Gradio application that allows them to perform tasks like calculating similarity scores between sentences, ranking documents, and performing fuzzy duplication, all within an easy-to-use interface.

What is the significance of the benchmark scores mentioned in the script?

The benchmark scores are significant as they indicate how well Word Lama performs compared to other models in various NLP tasks. These scores help users understand the model's effectiveness and suitability for their specific use cases.

How does Word Lama's model size impact its practical applications?

Word Lama's small model size allows for faster processing and lower resource requirements, making it ideal for real-time applications and environments with limited computational resources.

Outlines

00:00

🤖 Introduction to Word Lama

The paragraph introduces Word Lama, a new lightweight NLP toolkit designed to efficiently perform critical business tasks such as similarity matching and fuzzy duplication. It emphasizes the importance of efficiency in NLP tasks and how Word Lama aims to improve upon existing large language models (LLMs). The creator of the video discusses the significance of NLP in various applications and mentions that Word Lama is built to be compact and efficient, recycling components from large language models to create word representations. The video also mentions the creation of a Gradio demo deployed on Hugging Face Spaces for interactive exploration.

05:02

🔍 Word Lama's Features and Use Cases

This paragraph delves into the features of Word Lama, highlighting its ability to create embeddings and perform tasks like similarity matching, ranking, and duplication. It discusses how these features can be beneficial for businesses, particularly in automating processes like filtering similar tickets in IT service management systems. The video creator demonstrates the use of Word Lama through a Gradio application, showing how to calculate similarity scores between sentences and rank documents based on given criteria. The paragraph also touches on the model's performance in benchmarks compared to other models like GloVe and all-Mini LM.

10:03

🌟 Conclusion and Encouragement to Explore

The final paragraph wraps up the video with a summary of Word Lama's potential impact on a wide range of applications beyond text generation. It commends the developers for their innovation and encourages viewers to explore the model further. The video creator also mentions plans to share the Gradio application and the repository for hands-on experience, inviting viewers to share their thoughts and experiences with the tool.

Keywords

💡NLP (Natural Language Processing)

NLP is a field of artificial intelligence that focuses on the interaction between computers and human languages. It involves understanding, interpreting, and generating human language in a way that is both meaningful and useful. In the context of the video, NLP is the core theme, as the project 'Word Lama' is designed to improve NLP tasks such as finding similarity between sentences and fuzzy duplication. The video discusses how Word Lama can be utilized to enhance the efficiency of these tasks, which are crucial for various business applications.

💡Word Embedding

Word embedding is a technique in NLP where words or phrases from the vocabulary are mapped to vectors of real numbers. These vectors represent the words in a continuous space and are used to capture semantic meanings. In the video, Word Lama is described as a utility for NLP that recycles components from large language models to create efficient and compact word representations, which are essentially word embeddings. The script mentions 'glove' and 'word2vec' as examples of such representations.

💡Efficiency

In the context of the video, efficiency refers to the ability to perform NLP tasks quickly and with minimal resource usage. The video emphasizes the importance of efficiency in business-critical NLP tasks, where speed and low resource consumption are highly valued. Word Lama is highlighted for its efficiency, being able to provide fast and lightweight solutions for tasks like similarity matching and fuzzy duplication.

💡Business-Critical Tasks

These are tasks that are essential to the operation and success of a business. The video mentions that many NLP tasks, such as similarity matching and fuzzy duplication, are business-critical because they can have a significant impact on operations, such as in customer support systems where filtering similar tickets can improve efficiency.

💡Word Lama

Word Lama is a new lightweight NLP toolkit introduced in the video. It is designed to be an efficient and compact tool for creating word representations by recycling components from large language models. The video discusses how Word Lama improves upon existing models in terms of size and performance, making it suitable for various NLP tasks that require speed and low resource usage.

💡Gradio

Gradio is a Python library for building customizable and easy-to-use interfaces around machine learning models. In the video, the creator has used Gradio to create a demo deployed on Hugging Face Spaces, allowing users to interact with the Word Lama model through a user-friendly interface. This demonstrates the practical application of the Word Lama toolkit in a real-world setting.

💡Hugging Face Spaces

Hugging Face Spaces is a platform that allows users to deploy and share machine learning models easily. In the video, the Gradio demo created for Word Lama is deployed on Hugging Face Spaces, providing a platform for users to experiment with the model's capabilities without needing to install anything themselves.

💡Benchmarks

Benchmarks are standardized tests used to evaluate the performance of models or systems. In the video, the Word Lama model is compared against other embedding models using the MTB Benchmark, which assesses the quality of embeddings. The video discusses how Word Lama performs on par with other models in terms of accuracy and efficiency.

💡Lightweight Model

A lightweight model is a type of machine learning model that is designed to be small in size and fast in execution, often at the cost of some accuracy. The video highlights Word Lama as a lightweight model, emphasizing its small size (16 MB for 256 dimensions) compared to other models, which makes it suitable for environments with limited computational resources.

💡Fuzzy Duplication

Fuzzy duplication refers to the process of identifying and removing duplicate or near-duplicate data in a dataset. This is an important task in data cleaning and preprocessing. The video mentions that Word Lama can be used for fuzzy duplication, which is a testament to its versatility in handling various NLP tasks beyond just text generation.

Highlights

Introduction to Word Lama, a new lightweight NLP toolkit.

NLP stands for natural language processing and involves tasks like finding sentence similarity and fuzzy duplication.

Word Lama is designed for efficiency in business-critical NLP tasks.

The project offers a new Python package to assist with NLP tasks.

Word Lama recycles components from large language models to create compact word representations.

Examples of word representations include GloVe and FastText.

Word Lama improves on MTB Benchmark, a standard for evaluating embedding models.

The model is substantially smaller than traditional models, with significant space and resource savings.

Word Lama offers APIs for tasks like similarity calculation, ranking, and deduplication.

The model is trained on a single A100 GPU for 12 hours, demonstrating its lightweight nature.

Use cases for Word Lama include IT service management and e-commerce document ranking.

The model can be used for clustering, ranking, classification, deduplication, and fuzzy similarity matching.

Word Lama's primary advantages are its speed and efficiency in production environments.

The presenter has created a Gradio demo deployed on Hugging Face Spaces for interactive testing.

Word Lama is built from components of models like Llama 2, showcasing innovation in model development.

The model's performance is decent on par with other state-of-the-art models in various benchmarks.

The project demonstrates the practical application of large language model components in diverse use cases.

The presenter encourages viewers to explore and compare Word Lama with other models.

The video concludes with an invitation to try the Gradio application and further explore Word Lama.

Transcripts

find it pretty fascinating when I see

some projects that use a particular part

of llm and then make some improvements

in overall science like in this

particular case this is a new project

called word Lama this is a lightweight

NLP toolkit and for those who do not

know NLP in this particular context NLP

stands for natural language processing

involves a lot of different things for

example sometimes you have to find

similarity between two sentences

sometimes you have to do fuzzy D

duplication so but one of the most

important thing than accuracy is how

efficient you can do these tasks and

these are business critical tasks a lot

of these tasks have real world impact

I'm not saying that llms don't have

impact but these tasks power a lot of

things that you do not know a lot of

data science teams behind the scenes are

using this so one such project that I

recently came across is word llama which

is releasing a new tool a new python

package that is going to help you do

these kind of tasks and the way World

llama built is exactly why I've made

this video If you were to install this

is just simply install word Lama and to

make it further easier I've created

gradio demo deployed it on hugging face

spaces so I'll share the link in the

YouTube description for you to play with

but if you were to see the theory of how

somebody has built it it's pretty

fascinating so let's start by one by one

word Lama is utility for NLP and word

embedding model that recycles components

from large language models to create

efficient and compact word

representation such as glove fast Tex I

think is from Facebook word tock 5 years

before anybody who wanted to build an

embedding you might have heard people

using word to W you can go see a lot of

kaggle competitions like Kora similarity

matching Kora duplication I'm not sure

how many of you are deep into kagle but

if you are into ml then you should

definitely take it into kagle so these

are like the word representations that

people used to use to create embeddings

and from that embeddings people used to

use lot of different things like

similarity finding and you know semantic

search and a lot of other things one

such library that might come into your

mind is esir

and uh you sentence Transformers so in

this particular case word Lama Begins by

extracting the token embedding code book

from a state-ofthe-art LM in this case

llama 370 billion example but the one

that we are going to use in this video

is based on llama 2 as far as I know and

training a very small contextless model

in a general purpose embedding framework

so Baseline this is an embedding model

word llama improves on all MTB Benchmark

so this is a benchmark that evaluates

these embedding models so you would have

heard like lot of these models for

example a very popular Model A lot of

people use from sentence Transformer is

all mini LM L6 V2 which is smaller in

size glow is another we saw word to W

which is a legend in this particular

space so you might have heard about like

this king plus man minus female is equal

to Queen something like that I'm not

saying it properly now what this model

the biggest Advantage here is this is

substantially small so if you compare it

with glove 300 Dimension model I think

the here is Dimension anyways glove

300D if you compare it this model is

just 16 MB 16 MB for 256 Dimensions

versus glove which is 2 GB greater than

2GB and it has a lot of advantages here

like uh matrioska representation just

low CPU low resources uh requirements

you don't need a lot of computer and

it's an ump only inference that means

it's lightweight and simple and a lot of

other things in short this is a model

that has been created from a bunch of

let's say models uh that is uh I think

llama 2 compatible models and the

training nodes L2 Supercat which is what

we're going to use here there is an L3

Supercat which we are not going to use

at this point it has been trained from a

batch size of 512 on a single a100 for

12 hours so how do you use it all you

have to do is simply you have to load

the model okay after you load the model

then you can just use all these API

endpoints available here so uh methods

to be precise word wl. similarity you

can give two sentences it can give you

similarity score then you have got the

query then you have got the candidates

then you can rank it based on the

ranking system reranking is extremely

popular these days this is may not be

like a cross encoder reranking that's

what mostly like when you see coh

reranking and a lot of other re ranking

users I'm not doing a comparison here

especially with reranking but you can

see some numbers here so for example uh

they've got different sizes WL 64 World

Lama 64 128 256 which is kind of the

optimal here 512 1024 and then glove and

the com no and you have got all mini LM

which is the smallest in s bird series

now if you see re ranking this model

scores 52 while the best one probably in

this particular case scores 58

classification this scores 58 and this

one scores 63 clustering this one scores

33 and uh the all mini LM scores 42 so

across all the benchmarks you would see

that this is a model that is decent on

par and a lot of business use cases

require these models to be uh small

Nimble and extremely fast and that is

exactly why I decided to cover this

model you can also create embeddings and

you can store the embeddings for example

you can take the embedding have the

embeddings in particular shape in this

particular case 2x 64 and then use the

embeddings later on for whatever reason

that you want so you can unpack the

embedding here and then do like

similarity matching and a lot of things

so if you're are a company let's say you

want to do um something a similarity

matching um then you can use this and

then store the embeddings every day like

a batch process and then use it and then

do the similarity and help certain

departments now enough of talking I'm

going to get into my gradio application

I'm not going to explain the code here

the code is fairly simpler um almost

like this except gradio elements so what

we're going to see is we're going to see

a couple of examples first one we're

going to do a similarity one of the

important thing is for you to calculate

similarity you need two sentences and

you might wonder what kind of business

use cases where people would do

similarity one very useful case is if

you are in an itm system um it support I

don't know what is itm I almost forgot

what is icsm so it's like uh service now

so where there's ating system Zen disk

all these companies one of the most

important thing that you have to do is

you have to filter similar tickets and

then maybe close it automatically with

the previous solution this way your

support Engineers are not swamped and

kagle had multiple competitions for that

so sentence one sentence two I'm going

to give an example so I need a coffee I

need I'm looking for coffee shop so this

is sentence one this is sentence two

we're going to calculate similarity and

this is running on free hugging face

spaces just CPU not even GPU calculate

similarity and as you can see here it

has got 67 now I'm to type something

completely random I make YouTube videos

technically if this model works fine the

similarity should be much lesser than .5

and here you go yeah 01 that means it

doesn't have any similarity at all like

it's rarely similar so I can now say I

make YouTube videos while drinking

caffine let's see it's ideally supposed

to increase because I need coffee and

I've added caffine here so as you can

see this works pretty f find with

similarities now you have got ranking

documents I've added examples for you to

use it so if you're coming to this

application uh grade your application

for the first time you don't have to be

worried so I've got best programming

languages and candidates here so rank it

and it is going to score the um the

documents so this is basically like your

ranker here and uh it's it's just

ranking JavaScript Java python C++ no

offence to uh Java audience but uh I

would never put Java above python so

that's another thing looking for a

restaurant you want to re rank it what

are the candidates you have got I need

food I I'm hungry I want to eat let's

find a place to eat this is extremely

helpful um sometimes let's say you have

got a pool of documents you are

retrieving 10 documents after you

retrieve 10 documents sometimes it's

very important for you to find the most

similar or the most ranked document and

you want to show like everything in a

descending order so not just one

similarity score but you want rank them

and then show and this has a lot of

impact in e-commerce how people show

things and a lot of things this is D

duplication uh you have got like a bunch

of other things like for example apple

apple orange banana if you are in India

of course one thing you know people use

this example a lot New Delhi I've got

Delhi and then New Delhi without space

this is always a pain if you work with

surveys and you can D duplicate it so

you can see that it D duplicated

everything and then gave you new Del

this is what fuzzy D duplication is it's

very hard to do with regular expression

and sometimes people just like build

smaller models to do it but fuzzy D

duplication like using these models can

be extremely helpful I'm not going to

I'm not going to go into other examples

you can do it yourself but this is an

extremely helpful embedding I would I

would call it embedding model I'm not

sure what exactly technically would call

it but wherever you want to do embedding

and do something out of it this model is

going to be extremely helpful or even if

you do not want to do embedding just you

want to do classical NLP task like for

example you want to do clustering you

want to do ranking you want to do

classification you want to do um uh D

duplication you want to do um fuzzy

similarity matching in all these cases R

ranking as well in all these cases this

model could be extremely helpful I'm not

going by the benchmarks for me the

primary objective of using this model in

any production case is speed and um yeah

you are welcome to compare it with uh

other SBD models and uh let me know what

you think about it I'm not sure if this

video is going to get any of the views

but I love this project I've love to see

how people can take one faet of what we

do in large language model and apply it

to use cases not necessarily text

generation but that can have like wide

range of impact so kudos to the

developers St the repository I'll also

link the gradio application that I built

for you to play with this see you in

another video Happy prompting