🔥 NEW LLama Embedding for Fast NLP💥 Llama-based Lightweight NLP Toolkit 💥
Summary
TLDRThe 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
🤖 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.
🔍 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.
🌟 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.
Mindmap
Keywords
💡NLP (Natural Language Processing)
💡Word Embedding
💡Efficiency
💡Business-Critical Tasks
💡Word Lama
💡Gradio
💡Hugging Face Spaces
💡Benchmarks
💡Lightweight Model
💡Fuzzy Duplication
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
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