Train and use a NLP model in 10 mins!
Summary
TLDRThe video script outlines the process of fine-tuning a language model for specific tasks using Hugging Face's Transformers library. It demonstrates training a model on NVIDIA's Twitter data to generate tweets in their style, highlighting the efficiency of transfer learning. The script also showcases the Hugging Face Model Hub's capabilities, including model training, inference, and integration into products. Examples of tasks like summarization, token classification, and zero-shot topic classification are provided, emphasizing the library's versatility and community contributions.
Takeaways
- 🎵 The presenter demonstrates how to fine-tune a language model for specific tasks, setting a mood with music.
- 💻 The environment setup involves leveraging a community project called 'hugging tweets' to train a model to write tweets in a unique voice.
- 🚀 The training process is expedited by using an existing model like GPT-2 from OpenAI, showcasing the efficiency of transfer learning.
- 🌐 The training data is sourced by scraping tweets from NVIDIA's official Twitter account, resulting in a dataset of 1348 tweets.
- 🔧 The presenter thanks Google for提供免费GPUs and tools like Weights & Biases for tracking model performance.
- 📈 The model is trained quickly, emphasizing the accessibility and speed of fine-tuning language models.
- 🌐 The trained model is uploaded to the Hugging Face Model Hub, making it publicly available for others to use.
- 🔗 The model's performance on generating tweets is showcased, demonstrating its alignment with NVIDIA's brand voice.
- 🔎 The script highlights the Model Hub's extensive library of pre-trained models that can be used for various NLP tasks.
- 📊 The presenter explores different NLP tasks such as summarization, token classification, and zero-shot topic classification, emphasizing the versatility of the models.
- ☕️ An example of long-form question answering is given, where the model pulls information from various sources to generate comprehensive answers.
Q & A
What is the purpose of the project 'Hugging Tweets' mentioned in the script?
-The purpose of the project 'Hugging Tweets' is to train a language model to write new tweets based on a specific individual's unique voice.
Why was the NVIDIA Twitter account chosen for the experiment?
-The NVIDIA Twitter account was chosen because Jensen, the CEO of NVIDIA, does not have a personal Twitter account, so the generic NVIDIA account was used instead.
How many tweets were kept from the NVIDIA Twitter account for the dataset?
-Only 1348 tweets from the NVIDIA Twitter account were kept for the dataset.
What model was used as the language model for fine-tuning in this experiment?
-The language model used for fine-tuning was GPT-2, created by OpenAI.
How long does it take to train the model with transfer learning?
-With transfer learning, it takes just a few minutes to train the model.
Who provided the free GPUs used for the compute in this experiment?
-Google provided the free GPUs used for the compute in this experiment.
What tool was mentioned for tracking the loss and learning rate during training?
-Weights & Biases was mentioned as a tool for tracking the loss and learning rate during training.
Where is the trained model hosted after training?
-The trained model is hosted on the Hugging Face Model Hub.
What is the inference time for generating tweets with the trained model on CPUs?
-The inference time for generating tweets with the trained model on CPUs is just over a second.
How can the predictions from the model be integrated into products?
-The predictions from the model can be integrated into products either by using the API provided or by hosting the model and running the inference oneself.
What other types of tasks are showcased in the script besides tweet generation?
-Other types of tasks showcased include summarization, token classification, zero-shot topic classification, and long-form question answering.
What is the significance of the Hugging Face Model Hub mentioned in the script?
-The Hugging Face Model Hub is significant because it allows users to use their own models or any of the thousands of pre-trained models shared by the community, filtered by framework, task, and language.
What is the role of the GitHub repositories mentioned in the script?
-The GitHub repositories mentioned, including 'transformers', 'tokenizers', 'datasets', and 'metrics', provide open-source tools for NLP, tokenization, finding open datasets, and assessing models.
Outlines
🚀 Fine-Tuning Language Models for Custom Tasks
The speaker begins by demonstrating how to fine-tune a language model for specific tasks, such as writing tweets, using the 'hugging tweets' project by community contributors Boris and Dima. They set up the environment in Google Colab and leverage an existing model, GPT-2 by OpenAI, to train a new model on tweets from NVIDIA's Twitter account. The training process is expedited by transfer learning, taking only a few minutes. The speaker also mentions the use of Google's free GPUs and the 'Weights & Biases' tool for tracking the training process. Once trained, the model is uploaded to the Hugging Face Model Hub, where it can be accessed and used to generate new tweets.
📚 Exploring the Hugging Face Model Hub
The speaker discusses the Hugging Face Model Hub, which hosts over 3000 pre-trained models shared by the community. These models can be filtered by framework, task, and language. They provide examples of different tasks that can be performed using these models, such as summarization, token classification, and zero-shot topic classification. The speaker also demonstrates long-form question answering, showcasing the model's ability to pull information from various sources to generate comprehensive answers.
🌟 Open Source Contributions and Resources
The speaker concludes by highlighting the open-source nature of the project, mentioning the 'transformers' library on GitHub, which is widely used by companies and has over 34,000 stars. They also mention other repositories like 'tokenizers' for fast tokenization and 'datasets' for accessing open datasets. The speaker thanks the audience and emphasizes the community's contributions to the development of these resources.
Mindmap
Keywords
💡Fine-tune
💡Language Model
💡Transfer Learning
💡GPT2
💡Hugging Face
💡Inference
💡API
💡Token Classification
💡Zero-Shot Learning
💡Question Answering
💡Open Source
Highlights
Introduction to fine-tuning a language model for specific tasks.
Setting up the environment in Collab to leverage the Hugging Tweets project.
Training a language model to write new tweets based on a unique voice in just five minutes.
Using NVIDIA's Twitter account as a dataset for training.
Downloading tweets by scraping Twitter to create a dataset.
Starting the training of the neural network using an existing model with pre-trained weights.
Using OpenAI's GPT-2 as the language model for fine-tuning.
The importance of transfer learning in reducing training time.
Google's提供免费GPUs for compute resources.
Weights & Biases as a tool for tracking model performance during training.
Model training completion and uploading to Hugging Face.
Accessing the trained model on Hugging Face Model Hub.
Generating new tweets with the fine-tuned model.
Low inference time on CPUs for model predictions.
API integration for predictions in products.
Hosting the model for local inference with a few lines of code.
Information on model pages for understanding and selecting the right model.
The Hugging Face Model Hub's collection of over 3,000 pre-trained models.
Filtering models by framework, task, and language on the Model Hub.
Example of summarization using a model trained on CNN datasets.
Demonstration of token classification to extract entities from text.
Zero-shot topic classification without prior training on specific labels.
Long-form question answering from multiple sources.
Open source libraries like Transformers, Tokenizers, and Datasets for NLP.
The popularity and impact of the Transformers library in the industry.
Transcripts
all right now i'm going to give you an
example of how
easy it has become to fine-tune a
language model on your specific
tasks and and i'm gonna
run some music if that's okay to get in
the mood
all right so uh by uh setting up the
environment here in collab um
for this experiment we're going to
leverage the project of
fantastic community contributor boris
dima
called hugging tweets as described the
goal is to
train a language model to write new
tweets
based on your own unique voice in just
five minutes here you can see that the
environment has been set up uh correctly
so we'll
pick a twitter handle for the data set
unfortunately
jensen the ceo of nvidia doesn't have a
twitter account so we'll use
the generic nvidia account
for this experiment now it's uh
starting to download the nvidia tweets
by scrapping twitter
all right here we got some of the
tweets from the nvidia
twitter account and it's created a
data set only keeping 1348
tweets from it so now that we gathered
the data set
we'll be able to start training the
neural network
keep in mind that we're not initializing
weights from scratch
but leveraging an existing model coming
with own weights
and just fine tuning them here we're
using the great gpt2
created by openai as the language model
learned a lot from pre-training two
thousand three
hundred tweets or are necessary also
that's why it's extremely fast it just
takes a few minutes to
to train that's the power of transfer
learning that we were
talking about earlier for the compute we
have to thank google who's providing the
free gpus in
good apps also thanks to
tools like weights and biases you can
follow how your loss is involving your
learning rate
depending on the earpods it's a really
great
tool to use
all right let's wait for a minute or two
for the model to
finish training
all right it's uh finished training and
now it's
blowing the model to hugging face so
it's been
fast
all right the new network has been
successfully trained
now right away you can go into the
hugging face model hub and
you'll find the model right here
so keep in mind that the model is
trained on the tweets
from nvidia so maybe what we can try it
with
is uh the future
of tech is
all right here as you can see the model
hasn't
been loaded yet it might take a
little bit of time for this to happen
we'll just wait and see
if it gets loaded all right now the
model
has been loaded and as you can see
it's starting generating the future of
tech
is bright that makes sense i'm pretty
much on brand
let's uh start something else maybe
all right the future of tech is changing
a lot with tech giant such as tesla
nvidia read more about these three major
companies along with their plans for ai
and deep learning on our blog
pretty cool and as you can see the
inference time is pretty low with just
uh over a second
on cpus not only can you
test the predictions here but you can
also take advantage of our
api to integrate these predictions
into your products in a matter of
minutes will
run the inference and the predictions
for you
if you prefer to host it yourself
you can just use these a few lines
here changing the name of the model in
transformers to be able to run that in
in a matter of
minutes too in addition you'll find on
the model pages
all the informations you need to pick
and understand the right model
so here you see like how does it work
you can see like the training data
the training procedure and for example
useful things like intending
uses and limitations
the beauty of the hugging face model hub
is that not only can you use your own
model
but you can also use any
of the more than 3 000 models that have
been pre-trained
and shared by the community
you can obviously filter them by
framework
pi torch or tensorflow based on the
tasks
you're interested in text specification
token classification
question sharing multiple choice
civilization translation conversational
and also by uh languages
maybe i can show you another example of
a task here with
summarization using a model that has
been uploaded by
facebook and trade on the data sets
extracted from
cnn what it's doing is taking
a wikipedia page here of the efl tower
and generating a summary of it
it's pretty cool because it's a very new
task
and this model is state of the art on
this task
of summarization maybe let's take
another task that is a bit simpler
for example token classification
you can see here that this model is
extracting
entities and information from text here
it's detecting that wolfgang is a person
and that berlin is a location
also as you can see it's extremely fast
on
cpus i wanted to give you
a couple more examples of things that
are starting to work well
for example zero shot
topic classification and and maybe take
uh
a custom example here uh let's say you
incoming customer support emails and you
wanna
classify based on their priority what
you have to do is just
define these levels pretty high
priority low and right away even if the
model hasn't seen these labels
it's going to be able to try to play
predict the classification
so like for example a customer is
sending an email like that it's very
urgent i need
your help asp
here is going to try classifies as you
can see
it detects that the priority of such
a message is really high it's really
cool here that you can pick
any uh topic any label any
classification
that you want even if the model hasn't
been trained on that
another really really cool advancement
that we've seen over the past few months
is the ability to do long-form question
answering
especially in open domain and when you
need to pull up from different
forces so for example here to question
what's the best way to treat a sunburn
the model is taken from different
articles from wikipedia
and generating an answer itself and
maybe we can try
it with something else for example
how do you make coffee
all right
all right you take a cup of coffee grind
them put them unfiltered and you eat a
beans let them
keep the water process is called coffee
roasting
pretty accurate and you can see again
it's picking up
a couple of like different articles from
wikipedia
there are also links right
you can go further it's a great way to
give
explainabilities to your users
alright that's pretty much it just to
finish obviously all of it is
powered by our open source that you can
find
on github transformers
that is pretty well known today that is
the most popular
open source nlp library with more than
34 000
gita stars used by
thousands of companies and more than 500
contributors you'll also find on github
our other repositories
the two biggest ones being tokenizers
for
fast tokenization and data sets
to find open data sets that you can use
for free and metrics to access your
models all right that's pretty much it
thanks everyone
my
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