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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Absolutely. I think that's beautiful, Lewis! Is there anything else you would like to share or mention before we sign off? Lewis: Maybe buy my book. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Curious if you can do a brief self-introduction and highlight what brought you to Hugging Face? Lewis: What brought me to Hugging Face was transformers. In 2018, I was working with transformers at a startup in Switzerland. My first project was a question answering task where you input some text and train a model to try and find the answer to a question within that text. In those days the library was called: pytorch-pretrained-bert, it was a very focused code base with a couple of scripts and it was the first time I worked with transformers. I had no idea what was going on so I read the original ‘Attention Is All You Need’ paper but I couldn’t understand it. So I started looking around for other resources to learn from. In the process, Hugging Face exploded with their library growing into many architectures and I got really excited about contributing to open-source software. So around 2019, I had this kinda crazy idea to write a book about transformers because I felt there was an information gap that was missing. So I partnered up with my friend, Leandro (von Werra) and we sent Thom (Wolf) a cold email out of nowhere saying, “Hey we are going to write a book about transformers, are you interested?” and I was expecting no response. But to our great surprise, he responded “Yea, sure let’s have a chat.” and around 1.5 years later this is our book: NLP with Transformers. This collaboration set the seeds for Leandro and I to eventually join Hugging Face. And I've been here now for around nine months. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Had no idea that that was the way O'Reilly's covers came about. They don't tell you and just pull context from the book and create something? Lewis: It seems like it. I mean, we don't really know the process. I'm just sort of guessing that maybe the illustrator was trying to get an idea and saw a few animals in the book. In one of the chapters we have a discussion about giraffes and zebras and stuff. But yeah I'm happy with the parrot cover. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | That's incredible. I remember when they released those examples for GPT-2, and one of my favorites (that almost gave me this sense of, whew, we're not quite there yet) were some of the more inaccurate mentions like “underwater fires”. Lewis: Exactly! Britney: But, then something had happened with an oil spill that next year, where there were actually fires underwater! And I immediately thought about that text and thought, maybe AI is onto something already that we're not quite aware of? | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Can you talk about the work you've done with the transformers library? Lewis: One of the things that I experienced in my previous jobs before Hugging Face was there's this challenge in the industry when deploying these models into production; these models are really large in terms of the number of parameters and this adds a lot of complexity to the requirements you might have. So for example, if you're trying to build a chatbot you need this model to be very fast and responsive. And most of the time these models are a bit too slow if you just take an off-the-shelf model, train it, and then try to integrate it into your application. So what I've been working on for the last few months on the transformers library is providing the functionality to export these models into a format that lets you run them much more efficiently using tools that we have at Hugging Face, but also just general tools in the open-source ecosystem. In a way, the philosophy of the transformers library is like writing lots of code so that the users don't have to write that code. In this particular example, what we're talking about is something called the ONNX format. It's a special format that is used in industry where you can basically have a model that's written in PyTorch but you can then convert it to TensorFlow or you can run it on some very dedicated hardware. And if you actually look at what's needed to make this conversion happen in the transformers library, it's fairly gnarly. But we make it so that you only really have to run one line of code and the library will take care of you. So the idea is that this particular feature lets machine learning engineers or even data scientists take their model, convert it to this format, and then optimize it to get faster latency and higher throughput. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | That is super interesting and powerful. Lewis: Maybe one thing to mention is that the whole evaluation question is a very subtle one. We know from previous benchmarks, such as SQuAD, a famous benchmark to measure how good models are at question answering, that many of these transformer models are good at taking shortcuts. Well, that's the aim but it turns out that many of these transformer models are really good at taking shortcuts. So, what they’re actually doing is they're getting a very high score on a benchmark which doesn't necessarily translate into the actual thing you were interested in which was answering questions. And you have all these subtle failure modes where the models will maybe provide completely wrong answers or they should not even answer at all. And so at the moment in the research community there's a very active and vigorous discussion about what role benchmarks play in the way we measure progress. But also, how do these benchmarks encode our values as a community? And one thing that I think Hugging Face can really offer the community here is the means to diversify the space of values because traditionally most of these research papers come from the U.S. which is a great country but it's a small slice of the human experience, right? | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | That's a good one! Don't over-engineer. Something I always try to keep in mind. Lewis: Exactly. And it's a natural thing I think as humans especially if you're nerdy you really want to find the most interesting way to do something and most of the time simple is better. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | That's very cool. Have there been, any standout applications of transformers? Lewis: I think there are a few. One is maybe emotional or personal, for example many of us when OpenAI released GPT-2, this very famous language model which can generate text. OpenAI actually provided in their blog posts some examples of the essays that this model had created. And one of them was really funny. One was an essay about why we shouldn't recycle or why recycling is bad. And the model wrote a compelling essay on why recycling was bad. Leandro and I were working at a startup at the time and I printed it out and stuck it right above the recycling bin in the office as a joke. And people were like, “Woah, who wrote this?” and I said, “An algorithm.” I think there's something sort of strangely human, right? Where if we see generated text we get more surprised when it looks like something I (or another human) might have written versus other applications that have been happening like classifying text or more conventional tasks. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Please do, I look forward to that! Lewis: In fact, there is one meme in the book. We tried to sneak this in past the Editor and have the DOGE dog inside the book and we use a special vision transformer to try and classify what this meme is. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | How you see the world of model evaluation evolving? Lewis: That's a great question. So at Hugging Face, one of the things I've been working on has been trying to build the infrastructure and the tooling that enables what we call 'large-scale evaluation'. So you may know that the Hugging Face Hub has thousands of models and datasets. But if you're trying to navigate this space you might ask yourself, 'I'm interested in question answering and want to know what the top 10 models on this particular task are'. And at the moment, it's hard to find the answer to that, not just on the Hub, but in general in the space of machine learning this is quite hard. You often have to read papers and then you have to take those models and test them yourself manually and that's very slow and inefficient. So one thing that we've been working on is to develop a way that you can evaluate models and datasets directly through the Hub. We're still trying to experiment there with the direction. But I'm hoping that we have something cool to show later this year. And there's another side to this which is that a large part of the measuring progress in machine learning is through the use of benchmarks. These benchmarks are traditionally a set of datasets with some tasks but what's been maybe missing is that a lot of researchers speak to us and say, “Hey, I've got this cool idea for a benchmark, but I don't really want to implement all of the nitty-gritty infrastructure for the submissions, and the maintenance, and all those things.” And so we've been working with some really cool partners on hosting benchmarks on the Hub directly. So that then people in the research community can use the tooling that we have and then simplify the evaluation of these models. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | It is so good! Lewis: [shows book featuring a parrot on the cover] Do you know the story about the parrot? | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Where do you want to see more ML applications? Lewis: So I think personally, the area that I'm most excited about is the application of machine learning into natural sciences. And that's partly because of my background. I used to be a Physicist in a previous lifetime but I think what's also very exciting here is that in a lot of fields. For example, in physics or chemistry you already know what the say underlying laws are in terms of equations that you can write down but it turns out that many of the problems that you're interested in studying often require a simulation. Or they often require very hardcore supercomputers to understand and solve these equations. And one of the most exciting things to me is the combination of deep learning with the prior knowledge that scientists have gathered to make breakthroughs that weren't previously possible. And I think a great example is DeepMind’s Alpha Fold model for protein structure prediction where they were basically using a combination of transformers with some extra information to generate predictions of proteins that I think previously were taking on the order of months and now they can do them in days. So this accelerates the whole field in a really powerful way. And I can imagine these applications ultimately lead to hopefully a better future for humanity. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | If you could go back and do one thing differently at the beginning of your career in machine learning, what would it be? Lewis: Oh, wow. That's a tough one. Hmm. So, the reason this is a really hard question to answer is that now that I’m working at Hugging Face, it's the most fulfilling type of work that I've really done in my whole life. And the question is if I changed something when I started out maybe I wouldn't be here, right? It's one of those things where it's a tricky one in that sense. I suppose one thing that maybe I would've done slightly differently is when I started out working as a data scientist you tend to develop the skills which are about mapping business problems to software problems or ultimately machine learning problems. And this is a really great skill to have. But what I later discovered is that my true driving passion is doing open source software development. So probably the thing I would have done differently would have been to start that much earlier. Because at the end of the day most open source is really driven by community members. So that would have been maybe a way to shortcut my path to doing this full-time. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | What have you been interested in lately? It could be a movie, a recipe, a podcast, literally anything. And I'm just curious what that is and how someone interested in that might find it or get started. Lewis: It's a great question. So for me, I like podcasts in general. It’s my new way of reading books because I have a young baby so I'm just doing chores and listening at the same time. One podcast that really stands out recently is actually the DeepMind podcast produced by Hannah Fry who's a mathematician in the UK and she gives this beautiful journey through not just what Deep Mind does, but more generally, what deep learning and especially reinforcement learning does and how they're impacting the world. Listening to this podcast feels like you're listening to like a BBC documentary because you know the English has such great accents and you feel really inspired because a lot of the work that she discusses in this podcast has a strong overlap with what we do at Hugging Face. You see this much bigger picture of trying to pave the way for a better future. It resonated strongly. And I just love it because the explanations are super clear and you can share it with your family and your friends and say, “Hey, if you want to know what I'm doing? This can give you a rough idea.” It gives you a very interesting insight into the Deep Mind researchers and their backstory as well. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | What are some of your favorite Machine Learning papers? Lewis: Depends on how we measure this, but there's one paper that stands out to me, which is quite an old paper. It’s by the creator of random forests, Leo Breiman. Random forests is a very famous classic machine learning technique that's useful for tabular data that you see in industry and I had to teach random forests at university a year ago. And I was like, okay, I'll read this paper from the 2000s and see if I understand it. And it's a model of clarity. It's very short, and very clearly explains how the algorithm is implemented. You can basically just take this paper and implement the code very very easily. And that to me was a really nice example of how papers were written in medieval times. Whereas nowadays, most papers, have this formulaic approach of, okay, here's an introduction, here's a table with some numbers that get better, and here's like some random related work section. So, I think that's one that like stands out to me a lot. But another one that's a little bit more recent is a paper by DeepMind again on using machine learning techniques to prove fundamental theorems like algebraic topology, which is a special branch of abstract mathematics. And at one point in my life, I used to work on these related topics. So, to me, it's a very exciting, perspective of augmenting the knowledge that a mathematician would have in trying to narrow down the space of theorems that they might have to search for. I think this to me was surprising because a lot of the time I've been quite skeptical that machine learning will lead to this fundamental scientific insight beyond the obvious ones like making predictions. But this example showed that you can actually be quite creative and help mathematicians find new ideas. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Welcome, Lewis! Thank you so much for taking time out of your busy schedule to chat with me today about your awesome work! Lewis: Thanks, Britney. It’s a pleasure to be here. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Rapid Fire Questions: | [
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CO2 Emissions and the 🤗 Hub: Leading the Charge | sasha | April 22, 2022 | carbon-emissions-on-the-hub | community, guide | https://huggingface.co/blog/carbon-emissions-on-the-hub | # CO2 Emissions and the 🤗 Hub: Leading the Charge | [
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CO2 Emissions and the 🤗 Hub: Leading the Charge | sasha | April 22, 2022 | carbon-emissions-on-the-hub | community, guide | https://huggingface.co/blog/carbon-emissions-on-the-hub | How to Report Your Carbon Emissions with transformers If you're using transformers, you can automatically track and report carbon emissions thanks to the codecarbon integration. If you've installed codecarbon on your machine, the Trainer object will automatically add the CodeCarbonCallback while training, which will store carbon emissions data for you as you train. So, if you run something like this... python from datasets import load_dataset from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments ds = load_dataset("imdb") model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2) tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", truncation=True) small_train_dataset = ds["train"].shuffle(seed=42).select(range(1000)).map(tokenize_function, batched=True) small_eval_dataset = ds["test"].shuffle(seed=42).select(range(1000)).map(tokenize_function, batched=True) training_args = TrainingArguments( "codecarbon-text-classification", num_train_epochs=4, push_to_hub=True ) trainer = Trainer( model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset, ) trainer.train() ...you'll be left with a file within the codecarbon-text-classification directory called emissions.csv. This file will keep track of the carbon emissions across different training runs. Then, when you're ready, you can take the emissions from the run you used to train your final model and include that in its model card. 📝 An example of this data being included at the top of the model card is shown below: For more references on the metadata format for co2_eq_emissions see the hub docs. | [
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CO2 Emissions and the 🤗 Hub: Leading the Charge | sasha | April 22, 2022 | carbon-emissions-on-the-hub | community, guide | https://huggingface.co/blog/carbon-emissions-on-the-hub | What are CO2 Emissions and why are they important? Climate change is one of the greatest challenges that we are facing and reducing emissions of greenhouse gases such as carbon dioxide (CO2) is an important part of tackling this problem. Training and deploying machine learning models will emit CO2 due to the energy usage of the computing infrastructures that are used: from GPUs to storage, it all needs energy to function and emits CO2 in the process. > Pictured: Recent Transformer models and their carbon footprints The amount of CO2 emitted depends on different factors such as runtime, hardware used, and carbon intensity of the energy source. Using the tools described below will help you both track and report your own emissions (which is important to improve the transparency of our field as a whole!) and choose models based on their carbon footprint. | [
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CO2 Emissions and the 🤗 Hub: Leading the Charge | sasha | April 22, 2022 | carbon-emissions-on-the-hub | community, guide | https://huggingface.co/blog/carbon-emissions-on-the-hub | How to find low-emission models using the Hugging Face Hub With the model now uploaded to the Hub, how can you search for models on the Hub while trying to be eco-friendly? Well, the huggingface_hub library has a new special parameter to perform this search: emissions_threshold. All you need to do is specify a minimum or maximum number of grams, and all models that fall within that range. For example, we can search for all models that took a maximum of 100 grams to make: python from huggingface_hub import HfApi api = HfApi() models = api.list_models(emissions_thresholds=(None, 100), cardData=True) len(models) >>> 191 There were quite a few! This also helps to find smaller models, given they typically did not release as much carbon during training. We can look at one up close to see it does fit our threshold: python model = models[0] print(f'Model Name: {model.modelId}\nCO2 Emitted during training: {model.cardData["co2_eq_emissions"]}') >>> Model Name: esiebomajeremiah/autonlp-email-classification-657119381 CO2 Emitted during training: 3.516233232503715 Similarly, we can search for a minimum value to find very large models that emitted a lot of CO2 during training: python models = api.list_models(emissions_thresholds=(500, None), cardData=True) len(models) >>> 10 Now let's see exactly how much CO2 one of these emitted: python model = models[0] print(f'Model Name: {model.modelId}\nCO2 Emitted during training: {model.cardData["co2_eq_emissions"]}') >>> Model Name: Maltehb/aelaectra-danish-electra-small-cased CO2 Emitted during training: 4009.5 That's a lot of CO2! As you can see, in just a few lines of code we can quickly vet models we may want to use to make sure we're being environmentally cognizant! | [
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CO2 Emissions and the 🤗 Hub: Leading the Charge | sasha | April 22, 2022 | carbon-emissions-on-the-hub | community, guide | https://huggingface.co/blog/carbon-emissions-on-the-hub | How to calculate your own CO2 Emissions automatically with Transformers Before we begin, if you do not have the latest version of the huggingface_hub library on your system, please run the following: pip install huggingface_hub -U | [
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CO2 Emissions and the 🤗 Hub: Leading the Charge | sasha | April 22, 2022 | carbon-emissions-on-the-hub | community, guide | https://huggingface.co/blog/carbon-emissions-on-the-hub | Further readings - Rolnick et al. (2019) - Tackling Climate Change with Machine Learning - Strubell et al. (2019) - Energy and Policy Considerations for Deep Learning in NLP - Schwartz et al. (2020) - Green AI | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Training / Fine-tuning a model with 🤗 Transformers and 🤗 Datasets In this section, we will jump into the technical details of how to fine-tune a model end-to-end to be able to automatically filter out very unsatisfied customer feedback messages. Cool! Let's start by installing all necessary pip packages and setting up our code environment, then look into preprocessing the dataset, and finally start training the model. The following notebook can be run online in a google colab pro with the GPU runtime environment enabled. | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Finding suitable datasets Having decided on the task, next, we should find the data the model will be trained on. This is usually more important for the performance of your use case than picking the right model architecture. Keep in mind that a model is only as good as the data it has been trained on. Thus, we should be very careful when curating and/or selecting the dataset. Since we consider the hypothetical use case of filtering out unsatisfied messages, let's look into what datasets are available. For your real-world use case, it is very likely that you have internal data that best represents the actual data your NLP system is supposed to handle. Therefore, you should use such internal data to train your NLP system. It can nevertheless be helpful to also include publicly available data to improve the generalizability of your model. Let's take a look at all available Datasets on the Hugging Face Hub. On the left side, you can filter the datasets according to Task Categories as well as Tasks which are more specific. Our use case corresponds to Text Classification -> Sentiment Analysis so let's select these filters. We are left with ca. 80 datasets at the time of writing this notebook. Two aspects should be evaluated when picking a dataset: - Quality: Is the dataset of high quality? More specifically: Does the data correspond to the data you expect to deal with in your use case? Is the data diverse, unbiased, ...? - Size: How big is the dataset? Usually, one can safely say the bigger the dataset, the better. It's quite tricky to evaluate whether a dataset is of high quality efficiently, and it's even more challenging to know whether and how the dataset is biased. An efficient and reasonable heuristic for high quality is to look at the download statistics. The more downloads, the more usage, the higher chance that the dataset is of high quality. The size is easy to evaluate as it can usually be quickly read upon. Let's take a look at the most downloaded datasets: - Glue - Amazon polarity - Tweet eval - Yelp review full - Amazon reviews multi Now we can inspect those datasets in more detail by reading through the dataset card, which ideally should give all relevant and important information. In addition, the dataset viewer is an incredibly powerful tool to inspect whether the data suits your use case. Let's quickly go over the dataset cards of the models above: - GLUE is a collection of small datasets that primarily serve to compare new model architectures for researchers. The datasets are too small and don't correspond enough to our use case. - Amazon polarity is a huge and well-suited dataset for customer feedback since the data deals with customer reviews. However, it only has binary labels (positive/negative), whereas we are looking for more granularity in the sentiment classification. - Tweet eval uses different emojis as labels that cannot easily be mapped to a scale going from unsatisfied to satisfied. - Amazon reviews multi seems to be the most suitable dataset here. We have sentiment labels ranging from 1-5 corresponding to 1-5 stars on Amazon. These labels can be mapped to very unsatisfied, neutral, satisfied, very satisfied. We have inspected some examples on the dataset viewer to verify that the reviews look very similar to actual customer feedback reviews, so this seems like a very good dataset. In addition, each review has a product_category label, so we could even go as far as to only use reviews of a product category corresponding to the one we are working in. The dataset is multi-lingual, but we are just interested in the English version for now. - Yelp review full looks like a very suitable dataset. It's large and contains product reviews and sentiment labels from 1 to 5. Sadly, the dataset viewer is not working here, and the dataset card is also relatively sparse, requiring some more time to inspect the dataset. At this point, we should read the paper, but given the time constraint of this blog post, we'll choose to go for Amazon reviews multi. As a conclusion, let's focus on the Amazon reviews multi dataset considering all training examples. As a final note, we recommend making use of Hub's dataset functionality even when working with private datasets. The Hugging Face Hub, Transformers, and Datasets are flawlessly integrated, which makes it trivial to use them in combination when training models. In addition, the Hugging Face Hub offers: - A dataset viewer for every dataset - Easy demoing of every model using widgets - Private and Public models - Git version control for repositories - Highest security mechanisms | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | # Supercharged Customer Service with Machine Learning In this blog post, we will simulate a real-world customer service use case and use tools machine learning tools of the Hugging Face ecosystem to address it. We strongly recommend using this notebook as a template/example to solve your real-world use case. | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Finding a suitable model Having decided on the task and the dataset that best describes our use case, we can now look into choosing a model to be used. Most likely, you will have to fine-tune a pretrained model for your own use case, but it is worth checking whether the hub already has suitable fine-tuned models. In this case, you might reach a higher performance by just continuing to fine-tune such a model on your dataset. Let's take a look at all models that have been fine-tuned on Amazon Reviews Multi. You can find the list of models on the bottom right corner - clicking on Browse models trained on this dataset you can see a list of all models fine-tuned on the dataset that are publicly available. Note that we are only interested in the English version of the dataset because our customer feedback will only be in English. Most of the most downloaded models are trained on the multi-lingual version of the dataset and those that don't seem to be multi-lingual have very little information or poor performance. At this point, it might be more sensible to fine-tune a purely pretrained model instead of using one of the already fine-tuned ones shown in the link above. Alright, the next step now is to find a suitable pretrained model to be used for fine-tuning. This is actually more difficult than it seems given the large amount of pretrained and fine-tuned models that are on the Hugging Face Hub. The best option is usually to simply try out a variety of different models to see which one performs best. We still haven't found the perfect way of comparing different model checkpoints to each other at Hugging Face, but we provide some resources that are worth looking into: - The model summary gives a short overview of different model architectures. - A task-specific search on the Hugging Face Hub, e.g. a search on text-classification models, shows you the most downloaded checkpoints which is also an indication of how well those checkpoints perform. However, both of the above resources are currently suboptimal. The model summary is not always kept up to date by the authors. The speed at which new model architectures are released and old model architectures become outdated makes it extremely difficult to have an up-to-date summary of all model architectures. Similarly, it doesn't necessarily mean that the most downloaded model checkpoint is the best one. E.g. bert-base-cased is amongst the most downloaded model checkpoints but is not the best performing checkpoint anymore. The best approach is to try out various model architectures, stay up to date with new model architectures by following experts in the field, and check well-known leaderboards. For text-classification, the important benchmarks to look at are GLUE and SuperGLUE. Both benchmarks evaluate pretrained models on a variety of text-classification tasks, such as grammatical correctness, natural language inference, Yes/No question answering, etc..., which are quite similar to our target task of sentiment analysis. Thus, it is reasonable to choose one of the leading models of these benchmarks for our task. At the time of writing this blog post, the best performing models are very large models containing more than 10 billion parameters most of which are not open-sourced, e.g. ST-MoE-32B, Turing NLR v5, or ERNIE 3.0. One of the top-ranking models that is easily accessible is DeBERTa. Therefore, let's try out DeBERTa's newest base version - i.e. microsoft/deberta-v3-base. | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Install all necessary packages To begin with, let's install git-lfs so that we can automatically upload our trained checkpoints to the Hub during training. bash apt install git-lfs Also, we install the 🤗 Transformers and 🤗 Datasets libraries to run this notebook. Since we will be using DeBERTa in this blog post, we also need to install the sentencepiece library for its tokenizer. bash pip install datasets transformers[sentencepiece] Next, let's login into our Hugging Face account so that models are uploaded correctly under your name tag. python from huggingface_hub import notebook_login notebook_login() Output: Login successful Your token has been saved to /root/.huggingface/token Authenticated through git-credential store but this isn't the helper defined on your machine. You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default git config --global credential.helper store | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Fine-tune the model Having preprocessed the dataset, next we can fine-tune the model. We will make use of the popular Hugging Face Trainer which allows us to start training in just a couple of lines of code. The Trainer can be used for more or less all tasks in PyTorch and is extremely convenient by taking care of a lot of boilerplate code needed for training. Let's start by loading the model checkpoint using the convenient AutoModelForSequenceClassification. Since the checkpoint of the model repository is just a pretrained checkpoint we should define the size of the classification head by passing num_lables=5 (since we have 5 sentiment classes). python from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained(model_repository, num_labels=5) Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2ForSequenceClassification: ['mask_predictions.classifier.bias', 'mask_predictions.LayerNorm.bias', 'mask_predictions.dense.weight', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.weight', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.classifier.weight'] - This IS expected if you are initializing DebertaV2ForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing DebertaV2ForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at microsoft/deberta-v3-base and are newly initialized: ['pooler.dense.bias', 'classifier.weight', 'classifier.bias', 'pooler.dense.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. Next, we load a data collator. A data collator is responsible for making sure each batch is correctly padded during training, which should happen dynamically since training samples are reshuffled before each epoch. python from transformers import DataCollatorWithPadding data_collator = DataCollatorWithPadding(tokenizer=tokenizer) During training, it is important to monitor the performance of the model on a held-out validation set. To do so, we should pass a to define a compute_metrics function to the Trainer which is then called at each validation step during training. The simplest metric for the text classification task is accuracy, which simply states how much percent of the training samples were correctly classified. Using the accuracy metric might be problematic however if the validation or test data is very unbalanced. Let's verify quickly that this is not the case by counting the occurrences of each label. python from collections import Counter print("Validation:", Counter(tokenized_datasets["validation"]["labels"])) print("Test:", Counter(tokenized_datasets["test"]["labels"])) Output: Validation: Counter({0: 1000, 1: 1000, 2: 1000, 3: 1000, 4: 1000}) Test: Counter({0: 1000, 1: 1000, 2: 1000, 3: 1000, 4: 1000}) The validation and test data sets are as balanced as they can be, so we can safely use accuracy here! Let's load the accuracy metric via the datasets library. python from datasets import load_metric accuracy = load_metric("accuracy") Next, we define the compute_metrics which will be applied to the predicted outputs of the model which is of type EvalPrediction and therefore exposes the model's predictions and the gold labels. We compute the predicted label class by taking the argmax of the model's prediction before passing it alongside the gold labels to the accuracy metric. python import numpy as np def compute_metrics(pred): pred_logits = pred.predictions pred_classes = np.argmax(pred_logits, axis=-1) labels = np.asarray(pred.label_ids) acc = accuracy.compute(predictions=pred_classes, references=labels) return {"accuracy": acc["accuracy"]} Great, now all components required for training are ready and all that's left to do is to define the hyper-parameters of the Trainer. We need to make sure that the model checkpoints are uploaded to the Hugging Face Hub during training. By setting push_to_hub=True, this is done automatically at every save_steps via the convenient push_to_hub method. Besides, we define some standard hyper-parameters such as learning rate, warm-up steps and training epochs. We will log the loss every 500 steps and run evaluation every 5000 steps. python from transformers import TrainingArguments training_args = TrainingArguments( output_dir="deberta_amazon_reviews_v1", num_train_epochs=2, learning_rate=2e-5, warmup_steps=200, logging_steps=500, save_steps=5000, eval_steps=5000, push_to_hub=True, evaluation_strategy="steps", ) Putting it all together, we can finally instantiate the Trainer by passing all required components. We'll use the "validation" split as the held-out dataset during training. python from transformers import Trainer trainer = Trainer( args=training_args, compute_metrics=compute_metrics, model=model, tokenizer=tokenizer, data_collator=data_collator, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["validation"] ) The trainer is ready to go 🚀 You can start training by calling trainer.train(). python train_metrics = trainer.train().metrics trainer.save_metrics("train", train_metrics) Output: ***** Running training ***** Num examples = 200000 Num Epochs = 2 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 8 Gradient Accumulation steps = 1 Total optimization steps = 50000 Output: Step Training Loss Validation Loss Accuracy 5000 0.931200 0.979602 0.585600 10000 0.931600 0.933607 0.597400 15000 0.907600 0.917062 0.602600 20000 0.902400 0.919414 0.604600 25000 0.879400 0.910928 0.608400 30000 0.806700 0.933923 0.609200 35000 0.826800 0.907260 0.616200 40000 0.820500 0.904160 0.615800 45000 0.795000 0.918947 0.616800 50000 0.783600 0.907572 0.618400 Output: ***** Running Evaluation ***** Num examples = 5000 Batch size = 8 Saving model checkpoint to deberta_amazon_reviews_v1/checkpoint-50000 Configuration saved in deberta_amazon_reviews_v1/checkpoint-50000/config.json Model weights saved in deberta_amazon_reviews_v1/checkpoint-50000/pytorch_model.bin tokenizer config file saved in deberta_amazon_reviews_v1/checkpoint-50000/tokenizer_config.json Special tokens file saved in deberta_amazon_reviews_v1/checkpoint-50000/special_tokens_map.json added tokens file saved in deberta_amazon_reviews_v1/checkpoint-50000/added_tokens.json Training completed. Do not forget to share your model on huggingface.co/models =) Cool, we see that the model seems to learn something! Training loss and validation loss are going down and the accuracy also ends up being well over random chance (20%). Interestingly, we see an accuracy of around 58.6 % after only 5000 steps which doesn't improve that much anymore afterward. Choosing a bigger model or training for longer would have probably given better results here, but that's good enough for our hypothetical use case! Alright, finally let's upload the model checkpoint to the Hub. python trainer.push_to_hub() Output: Saving model checkpoint to deberta_amazon_reviews_v1 Configuration saved in deberta_amazon_reviews_v1/config.json Model weights saved in deberta_amazon_reviews_v1/pytorch_model.bin tokenizer config file saved in deberta_amazon_reviews_v1/tokenizer_config.json Special tokens file saved in deberta_amazon_reviews_v1/special_tokens_map.json added tokens file saved in deberta_amazon_reviews_v1/added_tokens.json Several commits (2) will be pushed upstream. The progress bars may be unreliable. | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Optimization As soon as you think the model's performance is good enough for production it's all about making the model as memory efficient and fast as possible. There are some obvious solutions to this like choosing the best suited accelerated hardware, e.g. better GPUs, making sure no gradients are computed during the forward pass, or lowering the precision, e.g. to float16. More advanced optimization methods include using open-source accelerator libraries such as ONNX Runtime, quantization, and inference servers like Triton. At Hugging Face, we have been working a lot to facilitate the optimization of models, especially with our open-source Optimum library. Optimum makes it extremely simple to optimize most 🤗 Transformers models. If you're looking for highly optimized solutions which don't require any technical knowledge, you might be interested in the Inference API, a plug & play solution to serve in production a wide variety of machine learning tasks, including sentiment analysis. Moreover, if you are searching for support for your custom use cases, Hugging Face's team of experts can help accelerate your ML projects! Our team answer questions and find solutions as needed in your machine learning journey from research to production. Visit hf.co/support to learn more and request a quote. | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Evaluate / Analyse the model Now that we have fine-tuned the model we need to be very careful about analyzing its performance. Note that canonical metrics, such as accuracy, are useful to get a general picture about your model's performance, but it might not be enough to evaluate how well the model performs on your actual use case. The better approach is to find a metric that best describes the actual use case of the model and measure exactly this metric during and after training. Let's dive into evaluating the model 🤿. The model has been uploaded to the Hub under deberta_v3_amazon_reviews after training, so in a first step, let's download it from there again. python from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("patrickvonplaten/deberta_v3_amazon_reviews") The Trainer is not only an excellent class to train a model, but also to evaluate a model on a dataset. Let's instantiate the trainer with the same instances and functions as before, but this time there is no need to pass a training dataset. python trainer = Trainer( args=training_args, compute_metrics=compute_metrics, model=model, tokenizer=tokenizer, data_collator=data_collator, ) We use the Trainer's predict function to evaluate the model on the test dataset on the same metric. python prediction_metrics = trainer.predict(tokenized_datasets["test"]).metrics prediction_metrics Output: ***** Running Prediction ***** Num examples = 5000 Batch size = 8 Output: {'test_accuracy': 0.608, 'test_loss': 0.9637690186500549, 'test_runtime': 21.9574, 'test_samples_per_second': 227.714, 'test_steps_per_second': 28.464} The results are very similar to performance on the validation dataset, which is usually a good sign as it shows that the model didn't overfit the test dataset. However, 60% accuracy is far from being perfect on a 5-class classification problem, but do we need very high accuracy for all classes? Since we are mostly concerned with very negative customer feedback, let's just focus on how well the model performs on classifying reviews of the most unsatisfied customers. We also decide to help the model a bit - all feedback classified as either very unsatisfied or unsatisfied will be handled by us - to catch close to 99% of the very unsatisfied messages. At the same time, we also measure how many unsatisfied messages we can answer this way and how much unnecessary work we do by answering messages of neutral, satisfied, and very satisfied customers. Great, let's write a new compute_metrics function. python import numpy as np def compute_metrics(pred): pred_logits = pred.predictions pred_classes = np.argmax(pred_logits, axis=-1) labels = np.asarray(pred.label_ids) # First let's compute % of very unsatisfied messages we can catch very_unsatisfied_label_idx = (labels == 0) very_unsatisfied_pred = pred_classes[very_unsatisfied_label_idx] # Now both 0 and 1 labels are 0 labels the rest is > 0 very_unsatisfied_pred = very_unsatisfied_pred * (very_unsatisfied_pred - 1) # Let's count how many labels are 0 -> that's the "very unsatisfied"-accuracy true_positives = sum(very_unsatisfied_pred == 0) / len(very_unsatisfied_pred) # Second let's compute how many satisfied messages we unnecessarily reply to satisfied_label_idx = (labels > 1) satisfied_pred = pred_classes[satisfied_label_idx] # how many predictions are labeled as unsatisfied over all satisfied messages? false_positives = sum(satisfied_pred <= 1) / len(satisfied_pred) return {"%_unsatisfied_replied": round(true_positives, 2), "%_satisfied_incorrectly_labels": round(false_positives, 2)} We again instantiate the Trainer to easily run the evaluation. python trainer = Trainer( args=training_args, compute_metrics=compute_metrics, model=model, tokenizer=tokenizer, data_collator=data_collator, ) And let's run the evaluation again with our new metric computation which is better suited for our use case. python prediction_metrics = trainer.predict(tokenized_datasets["test"]).metrics prediction_metrics Output: ***** Running Prediction ***** Num examples = 5000 Batch size = 8 Output: {'test_%_satisfied_incorrectly_labels': 0.11733333333333333, 'test_%_unsatisfied_replied': 0.949, 'test_loss': 0.9637690186500549, 'test_runtime': 22.8964, 'test_samples_per_second': 218.375, 'test_steps_per_second': 27.297} Cool! This already paints a pretty nice picture. We catch around 95% of very unsatisfied customers automatically at a cost of wasting our efforts on 10% of satisfied messages. Let's do some quick math. We receive daily around 10,000 messages for which we expect ca. 500 to be very negative. Instead of having to answer to all 10,000 messages, using this automatic filtering, we would only need to look into 500 + 0.12 * 10,000 = 1700 messages and only reply to 475 messages while incorrectly missing 5% of the messages. Pretty nice - a 83% reduction in human effort at missing only 5% of very unsatisfied customers! Obviously, the numbers don't represent the gained value of an actual use case, but we could come close to it with enough high-quality training data of your real-world example! Let's save the results python trainer.save_metrics("prediction", prediction_metrics) and again upload everything on the Hub. python trainer.push_to_hub() Output: Saving model checkpoint to deberta_amazon_reviews_v1 Configuration saved in deberta_amazon_reviews_v1/config.json Model weights saved in deberta_amazon_reviews_v1/pytorch_model.bin tokenizer config file saved in deberta_amazon_reviews_v1/tokenizer_config.json Special tokens file saved in deberta_amazon_reviews_v1/special_tokens_map.json added tokens file saved in deberta_amazon_reviews_v1/added_tokens.json To https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1 599b891..ad77e6d main -> main Dropping the following result as it does not have all the necessary fields: {'task': {'name': 'Text Classification', 'type': 'text-classification'}} To https://huggingface.co/patrickvonplaten/deberta_amazon_reviews_v1 ad77e6d..13e5ddd main -> main The data is now saved here. That's it for today 😎. As a final step, it would also make a lot of sense to try the model out on actual real-world data. This can be done directly on the inference widget on the model card: It does seem to generalize quite well to real-world data 🔥 | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Defining Task, Dataset & Model Before jumping into the actual coding part, it's important to have a clear definition of the use case that you would like to automate or partly automate. A clear definition of the use case helps identify the most suitable task, dataset to use, and model to apply for your use case. | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Defining your NLP task Alright, let's dive into a hypothetical problem we wish to solve using models of natural language processing models. Let's assume we are selling a product and our customer support team receives thousands of messages including feedback, complaints, and questions which ideally should all be answered. Quickly, it becomes obvious that customer support is by no means able to reply to every message. Thus, we decide to only respond to the most unsatisfied customers and aim to answer 100% of those messages, as these are likely the most urgent compared to the other neutral and positive messages. Assuming that a) messages of very unsatisfied customers represent only a fraction of all messages and b) that we can filter out unsatisfied messages in an automated way, customer support should be able to reach this goal. To filter out unsatisfied messages in an automated way, we plan on applying natural language processing technologies. The first step is to map our use case - filtering out unsatisfied messages - to a machine learning task. The tasks page on the Hugging Face Hub is a great place to get started to see which task best fits a given scenario. Each task has a detailed description and potential use cases. The task of finding messages of the most unsatisfied customers can be modeled as a text classification task: Classify a message into one of the following 5 categories: very unsatisfied, unsatisfied, neutral, satisfied, or very satisfied. | [
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Supercharged Customer Service with Machine Learning | patrickvonplaten | April 25, 2022 | supercharge-customer-service-with-machine-learning | guide, nlp | https://huggingface.co/blog/supercharge-customer-service-with-machine-learning | Preprocess the dataset Before we can start training the model, we should bring the dataset in a format that is understandable by the model. Thankfully, the 🤗 Datasets library makes this extremely easy as you will see in the following cells. The load_dataset function loads the dataset, nicely arranges it into predefined attributes, such as review_body and stars, and finally saves the newly arranged data using the arrow format on disk. The arrow format allows for fast and memory-efficient data reading and writing. Let's load and prepare the English version of the amazon_reviews_multi dataset. python from datasets import load_dataset amazon_review = load_dataset("amazon_reviews_multi", "en") Output: Downloading and preparing dataset amazon_reviews_multi/en (download: 82.11 MiB, generated: 58.69 MiB, post-processed: Unknown size, total: 140.79 MiB) to /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609... Dataset amazon_reviews_multi downloaded and prepared to /root/.cache/huggingface/datasets/amazon_reviews_multi/en/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609. Subsequent calls will reuse this data. Great, that was fast 🔥. Let's take a look at the structure of the dataset. python print(amazon_review) Output: {.output .execute_result execution_count="5"} DatasetDict({ train: Dataset({ features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'], num_rows: 200000 }) validation: Dataset({ features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'], num_rows: 5000 }) test: Dataset({ features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'], num_rows: 5000 }) }) We have 200,000 training examples as well as 5000 validation and test examples. This sounds reasonable for training! We're only really interested in the input being the "review_body" column and the target being the "starts" column. Let's check out a random example. python random_id = 34 print("Stars:", amazon_review["train"][random_id]["stars"]) print("Review:", amazon_review["train"][random_id]["review_body"]) Output: Stars: 1 Review: This product caused severe burning of my skin. I have used other brands with no problems The dataset is in a human-readable format, but now we need to transform it into a "machine-readable" format. Let's define the model repository which includes all utils necessary to preprocess and fine-tune the checkpoint we decided on. python model_repository = "microsoft/deberta-v3-base" Next, we load the tokenizer of the model repository, which is a DeBERTa's Tokenizer. python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_repository) As mentioned before, we will use the "review_body" as the model's input and "stars" as the model's target. Next, we make use of the tokenizer to transform the input into a sequence of token ids that can be understood by the model. The tokenizer does exactly this and can also help you to limit your input data to a certain length to not run into a memory issue. Here, we limit the maximum length to 128 tokens which in the case of DeBERTa corresponds to roughly 100 words which in turn corresponds to ca. 5-7 sentences. Looking at the dataset viewer again, we can see that this covers pretty much all training examples. Important: This doesn't mean that our model cannot handle longer input sequences, it just means that we use a maximum length of 128 for training since it covers 99% of our training and we don't want to waste memory. Transformer models have shown to be very good at generalizing to longer sequences after training. If you want to learn more about tokenization in general, please have a look at the Tokenizers docs. The labels are easy to transform as they already correspond to numbers in their raw form, i.e. the range from 1 to 5. Here we just shift the labels into the range 0 to 4 since indexes usually start at 0. Great, let's pour our thoughts into some code. We will define a preprocess_function that we'll apply to each data sample. python def preprocess_function(example): output_dict = tokenizer(example["review_body"], max_length=128, truncation=True) output_dict["labels"] = [e - 1 for e in example["stars"]] return output_dict To apply this function to all data samples in our dataset, we use the map method of the amazon_review object we created earlier. This will apply the function on all the elements of all the splits in amazon_review, so our training, validation, and testing data will be preprocessed in one single command. We run the mapping function in batched=True mode to speed up the process and also remove all columns since we don't need them anymore for training. python tokenized_datasets = amazon_review.map(preprocess_function, batched=True, remove_columns=amazon_review["train"].column_names) Let's take a look at the new structure. python tokenized_datasets Output: DatasetDict({ train: Dataset({ features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'], num_rows: 200000 }) validation: Dataset({ features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'], num_rows: 5000 }) test: Dataset({ features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'], num_rows: 5000 }) }) We can see that the outer layer of the structure stayed the same but the naming of the columns has changed. Let's take a look at the same random example we looked at previously only that it's preprocessed now. python print("Input IDS:", tokenized_datasets["train"][random_id]["input_ids"]) print("Labels:", tokenized_datasets["train"][random_id]["labels"]) Output: Input IDS: [1, 329, 714, 2044, 3567, 5127, 265, 312, 1158, 260, 273, 286, 427, 340, 3006, 275, 363, 947, 2] Labels: 0 Alright, the input text is transformed into a sequence of integers which can be transformed to word embeddings by the model, and the label index is simply shifted by -1. | [
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Introducing Hugging Face for Education | Violette | April 25, 2022 | education | community | https://huggingface.co/blog/education | # Introducing Hugging Face for Education 🤗 Given that machine learning will make up the overwhelming majority of software development and that non-technical people will be exposed to AI systems more and more, one of the main challenges of AI is adapting and enhancing employee skills. It is also becoming necessary to support teaching staff in proactively taking AI's ethical and critical issues into account. As an open-source company democratizing machine learning, Hugging Face believes it is essential to educate people from all backgrounds worldwide. We launched the ML demo.cratization tour in March 2022, where experts from Hugging Face taught hands-on classes on Building Machine Learning Collaboratively to more than 1000 students from 16 countries. Our new goal: to teach machine learning to 5 million people by the end of 2023. This blog post provides a high-level description of how we will reach our goals around education. | [
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Introducing Hugging Face for Education | Violette | April 25, 2022 | education | community | https://huggingface.co/blog/education | 🤗 Education for All 🗣️ Our goal is to make the potential and limitations of machine learning understandable to everyone. We believe that doing so will help evolve the field in a direction where the application of these technologies will lead to net benefits for society as a whole. Some examples of our existing efforts: - we describe in a very accessible way different uses of ML models (summarization, text generation, object detection…), - we allow everyone to try out models directly in their browser through widgets in the model pages, hence lowering the need for technical skills to do so (example), - we document and warn about harmful biases identified in systems (like GPT-2). - we provide tools to create open-source ML apps that allow anyone to understand the potential of ML in one click. | [
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Introducing Hugging Face for Education | Violette | April 25, 2022 | education | community | https://huggingface.co/blog/education | 🤗 Education Events & News - 09/08[EVENT]: ML Demo.cratization tour in Argentina at 2pm (GMT-3). Link here 🔥 We are currently working on more content in the course, and more! Stay tuned! | [
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Introducing Hugging Face for Education | Violette | April 25, 2022 | education | community | https://huggingface.co/blog/education | 🤗 Education for Instructors 🗣️ We want to empower educators with tools and offer collaborative spaces where students can build machine learning using open-source technologies and state-of-the-art machine learning models. - We provide to educators free infrastructure and resources to quickly introduce real-world applications of ML to theirs students and make learning more fun and interesting. By creating a classroom for free from the hub, instructors can turn their classes into collaborative environments where students can learn and build ML-powered applications using free open-source technologies and state-of-the-art models. - We’ve assembled a free toolkit translated to 8 languages that instructors of machine learning or Data Science can use to easily prepare labs, homework, or classes. The content is self-contained so that it can be easily incorporated into an existing curriculum. This content is free and uses well-known Open Source technologies (🤗 transformers, gradio, etc). Feel free to pick a tutorial and teach it! 1️⃣ A Tour through the Hugging Face Hub 2️⃣ Build and Host Machine Learning Demos with Gradio & Hugging Face 3️⃣ Getting Started with Transformers - We're organizing a dedicated, free workshop (June 6) on how to teach our educational resources in your machine learning and data science classes. Do not hesitate to register. - We are currently doing a worldwide tour in collaboration with university instructors to teach more than 10000 students one of our core topics: How to build machine learning collaboratively? You can request someone on the Hugging Face team to run the session for your class via the ML demo.cratization tour initiative. | [
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Introducing Hugging Face for Education | Violette | April 25, 2022 | education | community | https://huggingface.co/blog/education | 🤗 Education for Beginners 🗣️ We want to lower the barrier to becoming a machine learning engineer by providing online courses, hands-on workshops, and other innovative techniques. - We provide a free course about natural language processing (NLP) and more domains (soon) using free tools and libraries from the Hugging Face ecosystem. It’s completely free and without ads. The ultimate goal of this course is to learn how to apply Transformers to (almost) any machine learning problem! - We provide a free course about Deep Reinforcement Learning. In this course, you can study Deep Reinforcement Learning in theory and practice, learn to use famous Deep RL libraries, train agents in unique environments, publish your trained agents in one line of code to the Hugging Face Hub, and more! - We provide a free course on how to build interactive demos for your machine learning models. The ultimate goal of this course is to allow ML developers to easily present their work to a wide audience including non-technical teams or customers, researchers to more easily reproduce machine learning models and behavior, end users to more easily identify and debug failure points of models, and more! - Experts at Hugging Face wrote a book on Transformers and their applications to a wide range of NLP tasks. Apart from those efforts, many team members are involved in other educational efforts such as: - Participating in meetups, conferences and workshops. - Creating podcasts, YouTube videos, and blog posts. - Organizing events in which free GPUs are provided for anyone to be able to train and share models and create demos for them. | [
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Getting Started with Transformers on Habana Gaudi | juliensimon | April 26, 2022 | getting-started-habana | partnerships, guide | https://huggingface.co/blog/getting-started-habana | Fine-tuning a text classification model on Habana Gaudi I first clone the Optimum Habana repository inside the container I've just started. git clone https://github.com/huggingface/optimum-habana.git Then, I install the Optimum Habana package from source. cd optimum-habana pip install . Then, I move to the subdirectory containing the text classification example and install the required Python packages. cd examples/text-classification pip install -r requirements.txt I can now launch the training job, which downloads the bert-large-uncased-whole-word-masking model from the Hugging Face hub, and fine-tunes it on the MRPC task of the GLUE benchmark. Please note that I'm fetching the Habana Gaudi configuration for BERT from the Hugging Face hub, and you could also use your own. In addition, other popular models are supported, and you can find their configuration file in the Habana organization. python run_glue.py \ --model_name_or_path bert-large-uncased-whole-word-masking \ --gaudi_config_name Habana/bert-large-uncased-whole-word-masking \ --task_name mrpc \ --do_train \ --do_eval \ --per_device_train_batch_size 32 \ --learning_rate 3e-5 \ --num_train_epochs 3 \ --max_seq_length 128 \ --use_habana \ --use_lazy_mode \ --output_dir ./output/mrpc/ After 2 minutes and 12 seconds, the job is complete and has achieved an excellent F1 score of 0.9181, which could certainly improve with more epochs. ***** train metrics ***** epoch = 3.0 train_loss = 0.371 train_runtime = 0:02:12.85 train_samples = 3668 train_samples_per_second = 82.824 train_steps_per_second = 2.597 ***** eval metrics ***** epoch = 3.0 eval_accuracy = 0.8505 eval_combined_score = 0.8736 eval_f1 = 0.8968 eval_loss = 0.385 eval_runtime = 0:00:06.45 eval_samples = 408 eval_samples_per_second = 63.206 eval_steps_per_second = 7.901 Last but not least, I terminate the EC2 instance to avoid unnecessary charges. Looking at the Savings Summary in the EC2 console, I see that I saved 70% thanks to Spot Instances, paying only $3.93 per hour instead of $13.11. As you can see, the combination of Transformers, Habana Gaudi, and AWS instances is powerful, simple, and cost-effective. Give it a try and let us know what you think. We definitely welcome your questions and feedback on the Hugging Face Forum. --- Please reach out to Habana to learn more about training Hugging Face models on Gaudi processors. | [
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Getting Started with Transformers on Habana Gaudi | juliensimon | April 26, 2022 | getting-started-habana | partnerships, guide | https://huggingface.co/blog/getting-started-habana | Setting up an Habana Gaudi instance on AWS The simplest way to work with Habana Gaudi accelerators is to launch an Amazon EC2 DL1 instance. These instances are equipped with 8 Habana Gaudi processors that can easily be put to work thanks to the Habana Deep Learning Amazon Machine Image (AMI). This AMI comes preinstalled with the Habana SynapseAI® SDK, and the tools required to run Gaudi accelerated Docker containers. If you'd like to use other AMIs or containers, instructions are available in the Habana documentation. Starting from the EC2 console in the us-east-1 region, I first click on Launch an instance and define a name for the instance ("habana-demo-julsimon"). Then, I search the Amazon Marketplace for Habana AMIs. I pick the Habana Deep Learning Base AMI (Ubuntu 20.04). Next, I pick the dl1.24xlarge instance size (the only size available). Then, I select the keypair that I'll use to connect to the instance with ssh. If you don't have a keypair, you can create one in place. As a next step, I make sure that the instance allows incoming ssh traffic. I do not restrict the source address for simplicity, but you should definitely do it in your account. By default, this AMI will start an instance with 8GB of Amazon EBS storage, which won't be enough here. I bump storage to 50GB. Next, I assign an Amazon IAM role to the instance. In real life, this role should have the minimum set of permissions required to run your training job, such as the ability to read data from one of your Amazon S3 buckets. This role is not needed here as the dataset will be downloaded from the Hugging Face hub. If you're not familiar with IAM, I highly recommend reading the Getting Started documentation. Then, I ask EC2 to provision my instance as a Spot Instance, a great way to reduce the $13.11 per hour cost. Finally, I launch the instance. A couple of minutes later, the instance is ready and I can connect to it with ssh. Windows users can do the same with PuTTY by following the documentation. ssh -i ~/.ssh/julsimon-keypair.pem [email protected] On this instance, the last setup step is to pull the Habana container for PyTorch, which is the framework I'll use to fine-tune my model. You can find information on other prebuilt containers and on how to build your own in the Habana documentation. docker pull \ vault.habana.ai/gaudi-docker/1.5.0/ubuntu20.04/habanalabs/pytorch-installer-1.11.0:1.5.0-610 Once the image has been pulled to the instance, I run it in interactive mode. docker run -it \ --runtime=habana \ -e HABANA_VISIBLE_DEVICES=all \ -e OMPI_MCA_btl_vader_single_copy_mechanism=none \ --cap-add=sys_nice \ --net=host \ --ipc=host vault.habana.ai/gaudi-docker/1.5.0/ubuntu20.04/habanalabs/pytorch-installer-1.11.0:1.5.0-610 I'm now ready to fine-tune my model. | [
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Getting Started with Transformers on Habana Gaudi | juliensimon | April 26, 2022 | getting-started-habana | partnerships, guide | https://huggingface.co/blog/getting-started-habana | # Getting Started with Transformers on Habana Gaudi A couple of weeks ago, we've had the pleasure to announce that Habana Labs and Hugging Face would partner to accelerate Transformer model training. Habana Gaudi accelerators deliver up to 40% better price performance for training machine learning models compared to the latest GPU-based Amazon EC2 instances. We are super excited to bring this price performance advantages to Transformers 🚀 In this hands-on post, I'll show you how to quickly set up a Habana Gaudi instance on Amazon Web Services, and then fine-tune a BERT model for text classification. As usual, all code is provided so that you may reuse it in your projects. Let's get started! | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 3. What’s a common mistake you see people make trying to integrate ML into Pharmaceuticals? Wouldn’t say this is necessarily a mistake, but I see many people gravitate toward extreme perspectives when it comes to AI applications in healthcare; either too conservative or too aggressive. Some people are resistant due to high regulatory requirements. We had to qualify many of our AI applications with strict GxP validation. It may require a fair amount of work, but we believe the effort is worthwhile. On the opposite end of the spectrum, there are many people who think AI/Deep Learning models can outperform humans in many applications and run completely autonomously. As practitioners, we know that currently, neither is true. ML models can be incredibly valuable but still make mistakes. So I recommend a more progressive approach. The key is to have a framework that can leverage the power of AI while having goalkeepers in place. FDA has taken actions to regulate how AI/ML should be used in software as a medical device and I believe that’s a positive step forward for our industry. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 3. What’s a common mistake you see people make trying to integrate ML into Logistics? I think that the most common mistake I see is people doing ML and Data Science in a vacuum. Most ML applications within logistics will significantly change the dynamics of the problem if they are being used so it's important to develop models iteratively with the business and make sure that performance in reality matches what you expect in training. An example of this is in pricing where if you underprice a lane slightly, your prices may be too competitive which will create an influx of freight on that lane. This, in turn, may cause costs to go up as the brokers struggle to find capacity for those loads, exacerbating the issue. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 4. What excites you most about the future of ML? There is so much exciting stuff going on. I think the pace and democratization of the field is perhaps what I find most exciting. I remember almost 10 years ago writing my first seq2seq model for language translation. It was hundreds of lines of code, took forever to train and was pretty challenging. Now you can basically build a system to translate any language to any other language in under 100 lines of python code. It’s insane! This trend is most likely to continue and as the ML infrastructure gets better and better it will be easier and easier for people without deep domain expertise to deploy and serve models to other people. Much like in the beginning of the internet, software developers were few and far between and you needed a skilled team to set up a website. Then things like Django, Rails, etc. came out making website building easy but serving it was hard. We’re kind of at this place where building the models is easy but serving them reliably, monitoring them reliably, etc. is still challenging. I think in the next few years the barrier to entry is going to come WAY down here and basically, any high schooler could deploy a deep transformer to some cloud infrastructure and start serving useful results to the general population. This is really exciting because it means we’ll start to see more and more tangible innovation, much like the explosion of online services. So many cool things! | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | # Director of Machine Learning Insights [Part 1] Few seats at the Machine Learning table span both technical skills, problem solving and business acumen like Directors of Machine Learning Directors of Machine Learning and/or Data Science are often expected to design ML systems, have deep knowledge of mathematics, familiarity with ML frameworks, rich data architecture understanding, experience applying ML to real-world applications, solid communication skills, and often expected to keep on top of industry developments. A tall order! For these reasons, we’ve tapped into this unique group of ML Directors for a series of articles highlighting their thoughts on current ML insights and industry trends ranging from Healthcare to Finance, eCommerce, SaaS, Research, Media, and more. For example, one Director will note how ML can be used to reduce empty deadheading truck driving (which occurs ~20% of the time) down to just 19% would cut carbon emissions by ~100,000 Americans. Note: This is back of napkin math, done by an ex-rocket Scientist however, so we’ll take it. In this first installment, you’ll hear from a researcher (who’s using ground penetrating radar to detect buried landmines), an ex-Rocket Scientist, a Dzongkha fluent amateur gamer (Kuzu = Hello!), an ex-van living Scientist, a high-performance Data Science team coach who’s still very hands-on, a data practitioner who values relationships, family, dogs, and pizza. —All of whom are currently Directors of Machine Learning with rich field insights. 🚀 Let’s meet some top Machine Learning Directors and hear what they have to say about Machine Learning’s impact on their prospective industries: | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 2. What are the biggest ML challenges within Pharmaceuticals? The biggest ML challenge within pharma and healthcare is how to ensure equality and diversity in AI applications. For example, how to make sure the training set has good representations of all ethnic groups. Due to the nature of healthcare and pharma, this problem can have a much greater impact compared to applications in some other fields. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 2. What are the biggest ML challenges within Scientific research? There are many challenges. One example is when using ML for Science research, we have to think carefully through the data collection and curation protocols. In some cases, the protocols we used for non-ML analysis are not appropriate or effective. The quality of the data and how representative it is of what we expect to see in the application can make a huge impact on the performance, reliability, and trustworthiness of an ML-based system. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 3. What’s a common mistake you see people make trying to integrate ML into Science? Related to the question above, one common mistake is misinterpreting results or performance to be a function of just the ML system and not also considering the data collection, curation, calibration, and normalization protocols. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 4. What excites you most about the future of ML? The intersections between AI/ML and other hard sciences and technologies. I’m excited to see what’s to come. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 3. What’s a common mistake you see people make trying to integrate ML into Utilities? Not unique to utilizes, but moving too fast and neglecting good data quality and simple quality checks. Aside from this machine learning is practiced among many groups in some direct or indirect way. A challenge is integrating best development practices across teams. This also means model tracking and being able to persist experiments and continuous discovery. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 4. What excites you most about the future of ML? There are a lot of really exciting directions. A lot of my research currently is in spaces where we have a huge amount of prior knowledge and empirically derived models. For example, I have ongoing work using ML for forest ecology research. The forestry community has a rich body of prior knowledge and current purely data-driven ML systems are not leveraging. I think hybrid methods that seamlessly blend prior knowledge with ML approaches will be an interesting and exciting path forward. An example may be understanding how likely two species are to co-occur in an area. Or what species distribution we could expect given certain environmental conditions. These could potentially be used w/ data-driven methods to make predictions in changing conditions. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 2. What are the biggest ML challenges within Marketing? As with most things in Machine Learning the problems often aren’t really with the models themselves. With tools like Hugging Face, torch hub, etc. so many great and flexible models are available to work with. The real challenges have to do with collecting, cleaning, and managing the data. If we want to talk about the hard ML-y bits of the job, some of it comes down to the fact that there is a lot of noise in what people view and enjoy. Understanding things like virality are really really hard. Understanding what makes a creator/influencer successful over time is really hard. There is a lot of weird preference information buried in some pretty noisy difficult-to-acquire data. These problems come down to having really solid communication between data, ML, and business teams, and building models which augment and collaborate with humans instead of fully automating away their roles. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | Li Tan - Director of Machine Learning & AI at Johnson & Johnson Background: Li is an AI/ML veteran with 15+ years of experience leading high-profile Data Science teams within industry leaders like Johnson & Johnson, Microsoft, and Amazon. Fun Fact: Li continues to be curious, is always learning, and enjoys hands-on programming. Johnson & Johnson: A Multinational corporation that develops medical devices, pharmaceuticals, and consumer packaged goods. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | Alina Zare - Director of the Machine Learning & Sensing Laboratory at the University of Florida Background: Alina Zare teaches and conducts research in the area of machine learning and artificial intelligence as a Professor in the Electrical and Computer Engineering Department at the University of Florida and Director of the Machine Learning and Sensing Lab. Dr. Zare’s research has focused primarily on developing new machine learning algorithms to automatically understand and process data and imagery. Her research work has included automated plant root phenotyping, sub-pixel hyperspectral image analysis, target detection, and underwater scene understanding using synthetic aperture sonar, LIDAR data analysis, Ground Penetrating Radar analysis, and buried landmine and explosive hazard detection. Fun Fact: Alina is a rower. She joined the crew team in high school, rowed throughout college and grad school, was head coach of the University of Missouri team while she was an assistant professor, and then rowed as a masters rower when she joined the faculty at UF. Machine Learning & Sensing Laboratory: A University of Florida laboratory that develops machine learning methods for autonomously analyzing and understanding sensor data. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 1. How has ML made a positive impact on the Energy/Utility industry? Access to business insight. Provided a pre-requisite is great data. Utilities have many data relationships within their data portfolio from customers to devices, more specifically, this speaks to monthly billing amounts and enrollment in energy savings programs. Data like that could be stored in a relational database, whereas device or asset data we can think of as the pieces of machinery that make our grid. Bridging those types of data is non-trivial. In addition, third-party data spatial/gis and weather are extremely important. Through the lens of machine learning, we are able to find and explore features and outcomes that have a real impact. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 1. How has ML made a positive impact on Media? Privacy first personalization for customers: Every user is unique and while their long-term interests are stable, their short-term interests are stochastic. They expect their relationship with the Media to reflect this. The combination of advancement in hardware acceleration and Deep Learning for recommendations has unlocked the ability to start deciphering this nuance and serve users with the right content at the right time at the right touchpoint. Assistive tools for makers: Makers are the limited assets in media and preserving their creative bandwidth by ML driven human-in-the-loop assistive tools have seen an outsized impact. Something as simple as automatically suggesting an appropriate title, image, video, and/or product that can go along with the content they are creating unlocks a collaborative machine-human flywheel. Tightened testing: In a capital intensive media venture, there is a need to shorten the time between collecting information on what resonates with users and immediately acting on it. With a wide variety of Bayesian techniques and advancements in reinforcement learning, we have been able to drastically reduce not only the time but the cost associated with it. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 2. What are the biggest ML challenges within Logistics? The big challenge within logistics is due to the fact that the industry is so fragmented: there is no shared pool of data that would allow technology solutions to "see" the big picture. For example a large fraction of brokerage loads, maybe a majority, costs are negotiated on a load by load basis making them highly volatile. This makes pricing a very difficult problem to solve. If the industry became more transparent and shared data more freely, so much more would become possible. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | Archi Mitra - Director of Machine Learning at Buzzfeed Background: Bringing balance to the promise of ML for business. People over Process. Strategy over Hope. AI Ethics over AI Profits. Brown New Yorker. Fun Fact: I can speak Dzongkha (google it!) and am a supporter of Youth for Seva. Buzzfeed: An American Internet media, news and entertainment company with a focus on digital media. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 3. What’s a common mistake you see people make trying to integrate ML into Marketing? I don’t think this is exclusive to marketing but prioritizing machine learning and data science over good infrastructure is a big problem I see often. Organizations hear about ML and want to get a piece of the pie so they hire some data scientists only to find out that they don’t have any infrastructure to service their new fancy pants models. A ton of the value of ML is in the infrastructure around the models and if you’ve got trained models but no infrastructure you’re hosed. One of the really nice things about BEN is we invested heavily in our data infrastructure and built the horse before the cart. Now Data Scientists can build models that get served to our end users quickly instead of having to figure out every step of that pipeline themselves. Invest in data engineering before hiring lots of ML folks. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | Nathan Cahill Ph.D. - Director of Machine Learning at Xpress Technologies Background: Nathan is a passionate machine learning leader with 7 years of experience in research and development, and three years experience creating business value by shipping ML models to prod. He specializes in finding and strategically prioritizing the business' biggest pain points: unlocking the power of data earlier on in the growth curve. Fun Fact: Before getting into transportation and logistics I was engineering rockets at Northrop Grumman. #RocketScience Xpress Technologies: A digital freight matching technology to connect Shippers, Brokers and Carriers to bring efficiency and automation to the Transportation Industry. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 4. What excites you most about the future of ML? Hopefully, small data-driven general-purpose multi-modal multi-task real-time ML systems that create step-function improvements in drug discovery, high precision surgery, climate control systems & immersive metaverse experiences. Realistically, more accessible, low-effort meta-learning techniques for highly accurate text and image generation. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 4. What excites you most about the future of ML? I’ve been doing this for over a decade, and I somehow still feel like a novice. I feel fortunate to have been part of teams where I’d be lucky to be called the average member. My feeling is that the next ten years and beyond will be more focused on data engineering to see even a larger number of use cases covered by machine learning. --- 🤗 Thank you for joining us in this first installment of ML Director Insights. Stay tuned for more insights from ML Directors in SaaS, Finance, and e-Commerce. Big thanks to Eric Golinko, Nicolas Bertagnolli, Nathan Cahill, Alina Zare, Li Tan, and Archi Mitra for their brilliant insights and participation in this piece. We look forward to watching each of your continued successes and will be cheering you on each step of the way. 🎉 Lastly, if you or your team are interested in accelerating your ML roadmap with Hugging Face Experts please visit hf.co/support to learn more. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | Eric Golinko - Director of Machine Learning at E Source Background: Experienced data practitioner and team builder. I’ve worked in many industries across companies of different sizes. I’m a problem solver, by training a mathematician and computer scientist. But, above all, I value relationships, family, dogs, travel and pizza. Fun Fact: Eric adores nachos! E Source: Provides independent market intelligence, consulting, and predictive data science to utilities, major energy users, and other key players in the retail energy marketplace. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 1. How has ML made a positive impact on Logistics/Transportation? The transportation industry is incredibly fragmented. The top players in the game have less than 1% market share. As a result, there exist inefficiencies that can be solved by digital solutions. For example, when you see a semi-truck driving on the road, there is currently a 20% chance that the truck is driving with nothing in the back. Yes, 20% of the miles a tractor-trailer drives are from the last drop off of their previous load to their next pickup. The chances are that there is another truck driving empty (or "deadheading") in the other direction. With machine learning and optimization this deadhead percent can be reduced significantly, and just taking that number from 20% to 19% percent would cut the equivalent carbon emissions of 100,000 Americans. Note: the carbon emissions of 100k Americans were my own back of the napkin math. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | Nicolas Bertagnolli - Director of Machine Learning at BEN Background: Nic is a scientist and engineer working to improve human communication through machine learning. He’s spent the last decade applying ML/NLP to solve data problems in the medical space from uncovering novel patterns in cancer genomes to leveraging billions of clinical notes to reduce costs and improve outcomes. At BEN, Nic innovates intelligent technologies that scale human capabilities to reach people. See his CV, research, and Medium articles here. Fun Fact: Nic lived in a van and traveled around the western United States for three years before starting work at BEN. BEN: An entertainment AI company that places brands inside influencer, streaming, TV, and film content to connect brands with audiences in a way that advertisements cannot. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 2. What are the biggest ML challenges within Media? Privacy, editorial voice, and equitable coverage: Media is a key pillar in the democratic world now more than ever. ML needs to respect that and operate within constraints that are not strictly considered table stakes in any other domain or industry. Finding a balance between editorially curated content & programming vs ML driven recommendations continues to be a challenge. Another unique challenge to BuzzFeed is we believe that the internet should be free which means we don't track our users like others can. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 2. What are the biggest ML challenges within Utilities? There is a demystification that needs to happen. What machine learning can do and where it needs to be monitored or could fall short. The utility industry has established ways of operating, machine learning can be perceived as a disruptor. Because of this, departments can be slow to adopt any new technology or paradigm. However, if the practitioner is able to prove results, then results create traction and a larger appetite to adopt. Additional challenges are on-premise data and access to the cloud and infrastructure. It’s a gradual process and has a learning curve that requires patience. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 3. What’s a common mistake you see people make trying to integrate ML into Media? Ignoring “the makers” of media: Media is prevalent because it houses a voice that has a deep influence on people. The editors, content creators, writers & makers are the larynx of that voice and the business and building ML that enables them, extends their impact and works in harmony with them is the key ingredient to success. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 1. How has ML made a positive impact on Pharmaceuticals? AI/ML applications have exploded in the pharmaceuticals space the past few years and are making many long-term positive impacts. Pharmaceuticals and healthcare have many use cases that can leverage AI/ML. Applications range from research, and real-world evidence, to smart manufacturing and quality assurance. The technologies used are also very broad: NLP/NLU, CV, AIIoT, Reinforcement Learning, etc. even things like AlphaFold. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 4. What excites you the most about the future of ML? I think the thing that excites me most about ML is the opportunity to make people better at their jobs. As ML begins to be ubiquitous in business, it will be able to help speed up decisions and automate redundant work. This will accelerate the pace of innovation and create immense economic value. I can’t wait to see what problems we solve in the next 10 years aided by data science and ML! | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 1. How has ML made a positive impact on Science ML has made a positive impact in a number of ways from helping to automate tedious and/or slow tasks or providing new ways to examine and look at various questions. One example from my work in ML for plant science is that we have developed ML approaches to automate plant root segmentation and characterization in imagery. This task was previously a bottleneck for plant scientists looking at root imagery. By automating this step through ML we can conduct these analyses at a much higher throughput and begin to use this data to investigate plant biology research questions at scale. | [
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Director of Machine Learning Insights [Series] | britneymuller | April 27, 2022 | ml-director-insights | community, research | https://huggingface.co/blog/ml-director-insights | 1. How has ML made a positive impact on Marketing? In so many ways! It’s completely changing the landscape. Marketing is a field steeped in tradition based on gut feelings. In the past 20 years, there has been a move to more and more statistically informed marketing decisions but many brands are still relying on the gut instincts of their marketing departments. ML is revolutionizing this. With the ability to analyze data about which advertisements perform well we can make really informed decisions about how and who we market to. At BEN, ML has really helped us take the guesswork out of a lot of the process when dealing with influencer marketing. Data helps shine a light through the fog of bias and subjectivity so that we can make informed decisions. That’s just the obvious stuff! ML is also making it possible to make safer marketing decisions for brands. For example, it’s illegal to advertise alcohol to people under the age of 21. Using machine learning we can identify influencers whose audiences are mainly above 21. This scales our ability to help alcohol brands, and also brands who are worried about their image being associated with alcohol. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Kili Kili is an end-to-end AI training platform for data-centric businesses. Kili provides optimized labeling features and quality management tools to manage your data. You can quickly annotate the image, video, text, pdf, and voice data while controlling the quality of the dataset. It also has powerful APIs for GraphQL and Python which eases data management a lot. It is available either online or on-premise and it enables modern Machine Learning technics either on computer vision or on NLP and OCR. It supports text classification, named entity recognition (NER), relation extraction, and more NLP/OCR tasks. It also supports computer vision tasks like object detection, image transcription, video classification, semantic segmentation, and many more! Kili is a commercial tool but you can also create a free developer account to try Kili’s tools. You can learn more from the pricing page. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Labeling Now that we have the source data uploaded, the platform has a built-in labeling interface which is pretty easy to use. Available keyboard shortcuts helped while annotating the data. We used the interface without breaking a sweat, there are automatically defined shortcuts and it simplifies the labeling. We can see the shortcuts by clicking the keyboard icon at the right-upper part of the interface, they are also shown by underlined characters in the labeling interface at the right. Some samples were very weird, so we decided to skip them while labeling. In general, the process was way easier thanks to Kili’s built-in platform. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Final Analysis We can use the fine-tuned model to conduct the final analysis now. All we have to do is load the data, process it, and get the prediction results from the model. Then we can use a pre-trained model for sentiment analysis and hopefully get insights. We use Google Colab for the inference (here) and then exported the results to result.csv. It can be found in results in the GitHub repository. We then analyzed the results in another google collaboratory notebook for an interactive experience. So you can also use it easily and interactively. Let’s check the results now! We can see that the given scores are highly positive. In general, the application is liked by the users. This also matches with the sentiment analysis, most of the reviews are positive and the least amount of reviews are classified as negative. As we can see from above, the model's performance is kind of understandable. Positive scores are dominantly higher than the others, just like the sentimental analysis graph shows. As it comes to the categories defined before, it seems that the model predicts most of the reviews are about users' experiences (excluding experiences related to other categories): We can also see the sentiment predictions over defined categories below: We won't do a detailed analysis of the reviews, a basic understanding of potential problems would suffice. Therefore, it is enough to conclude simple results from the final data: - It is understandable that most of the reviews about the subscription are negative. Paid content generally is not welcomed in mobile applications. - There are many negative reviews about the interface. This may be a clue for further analysis. Maybe there is a misconception about features, or a feature doesn't work as users thought. - People have generally liked the articles and most of them had good experiences. Important note about the plot: we haven't filtered the reviews by application version. When we look at the results of the latest current version (4.5), it seems that the interface of the application confuses the users or has annoying bugs. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | AutoTrain with HuggingFace Automated Machine Learning is a term for automating a Machine Learning pipeline. It also includes data cleaning, model selection, and hyper-parameter optimization too. We can use 🤗 transformers for automated hyper-parameter searching. Hyper-parameter optimization is a difficult and time-consuming process. While we can build our pipeline ourselves by using transformers and other powerful APIs, it is also possible to fully automate this with AutoTrain. AutoTrain is built on many powerful APIs like transformers, datasets and inference-api. Cleaning the data, model selection, and hyper-parameter optimization steps are all fully automated in AutoTrain. One can fully utilize this framework to build production-ready SOTA transformer models for a specific task. Currently, AutoTrain supports binary and multi-label text classification, token classification, extractive question answering, text summarization, and text scoring. It also supports many languages like English, German, French, Spanish, Finnish, Swedish, Hindi, Dutch, and more. If your language is not supported by AutoTrain, it is also possible to use custom models with custom tokenizers. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Introduction Understanding your users’ needs is crucial in any user-related business. But it also requires a lot of hard work and analysis, which is quite expensive. Why not leverage Machine Learning then? With much less coding by using Auto ML. In this article, we will leverage HuggingFace AutoTrain and Kili to build an active learning pipeline for text classification. Kili is a platform that empowers a data-centric approach to Machine Learning through quality training data creation. It provides collaborative data annotation tools and APIs that enable quick iterations between reliable dataset building and model training. Active learning is a process in which you add labeled data to the data set and then retrain a model iteratively. Therefore, it is endless and requires humans to label the data. As a concrete example use case for this article, we will build our pipeline by using user reviews of Medium from the Google Play Store. After that, we are going to categorize the reviews with the pipeline we built. Finally, we will apply sentiment analysis to the classified reviews. Then we will analyze the results, understanding the users’ needs and satisfaction will be much easier. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Project We will work on an example of review classification, along with sentiment analysis, to get insights about a mobile application. We have extracted around 40 thousand reviews of Medium from the Google Play Store. We will annotate the review texts in this dataset step by step. And then we’re going to build a pipeline for review classification. In the modeling, the first model will be prepared with AutoTrain. Then we will also build a model without using AutoTrain. All the code and the dataset can be found on the GitHub repository of the project. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | # Opinion Classification with Kili and HuggingFace AutoTrain | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Exporting the Labeled Data The labeled data is exported with ease by using Python API. The script below exports the labeled and reviewed samples into a dataframe, then saves it with a given name as a CSV file. (scripts/prepare_dataset.py) python import argparse import os import pandas as pd from dotenv import load_dotenv from kili.client import Kili load_dotenv() parser = argparse.ArgumentParser() parser.add_argument('--output_name', required=True, type=str, default='dataset.csv') parser.add_argument('--remove', required=False, type=str) args = vars(parser.parse_args()) API_KEY = os.getenv('KILI_API_KEY') dataset_path = '../data/processed/lowercase_cleaned_dataset.csv' output_path = os.path.join('../data/processed', args['output_name']) def extract_labels(labels_dict): response = labels_dict[-1] # pick the latest version of the sample label_job_dict = response['jsonResponse']['JOB_0'] categories = label_job_dict['categories'] # all samples have a label, we can just pick it by its index label = categories[0]['name'] return label kili = Kili(API_KEY) print('Authenticated!') # query will return a list that contains matched elements (projects in this case) # since we have only one project with this name, we can just pick the first index project = kili.projects( search_query='User review dataset for topic classification')[0] project_id = project['id'] # we can customize the returned fields # the fields below are pretty much enough, # labels.jsonResponse carries the labeling data returned_fields = [ 'id', 'externalId', 'labels.jsonResponse', 'skipped', 'status' ] # I read the raw dataset too in order to match the samples with externalId dataset = pd.read_csv(dataset_path) # we can fetch the data as a dataframe df = kili.assets(project_id=project_id, status_in=['LABELED', 'REVIEWED'], fields=returned_fields, format='pandas') print('Got the samples!') # we will pass the skipped samples df_ns = df[~df['skipped']].copy() # extract the labeled samples df_ns.loc[:, 'label'] = df_ns['labels'].apply(extract_labels) # The externalId column is returned as string, let’s convert it to integer # to use as indices df_ns.loc[:, 'content'] = dataset.loc[df_ns.externalId.astype(int), 'content'] # we can drop the `labels` column now df_ns = df_ns.drop(columns=['labels']) # we'll remove the multi-labeled samples df_ns = df_ns[df_ns['label'] != 'MULTI_LABEL'].copy() # also remove the samples with label specified in remove argument if it's given if args['remove']: df_ns = df_ns.drop(index=df_ns[df_ns['label'] == args['remove']].index) print(‘DATA FETCHING DONE') print('DATASET HAS %d SAMPLES' % (len(df_ns))) print('SAVING THE PROCESSED DATASET TO: %s' % os.path.abspath(output_path)) df_ns.to_csv(output_path, index=False) print('DONE!') Nice! We now have the labeled data as a csv file. Let's create a dataset repository in HuggingFace and upload the data there! It's really simple, just click your profile picture and select New Dataset option. Then enter the repository name, pick a license if you want and it's done! Now we can upload the dataset from Add file in the Files and versions tab. Dataset viewer is automatically available after you upload the data, we can easily check the samples! It is also possible to upload the dataset to Hugging Face's dataset hub by using datasets package. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Conclusion Now we can use the pre-trained model to try to understand the potential shortcomings of the mobile application. Then it would be easier to analyze a specific feature. We used HuggingFace’s powerful APIs and AutoTrain along with Kili’s easy-to-use interface in this example. The modeling with AutoTrain just took 30 minutes, it chose the models and trained them for our use. AutoTrain is definitely much more efficient since I spent more time as I develop the model by myself. All the code, datasets, and scripts can be found in github. You can also try the AutoTrain model. While we can consider this as a valid starting point, we should collect more data and try to build better pipelines. Better pipelines would result in more efficient improvements. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Modeling Let's use active learning. We iteratively label and fine-tune the model. In each iteration, we label 50 samples in the dataset. The number of samples is shown below: Let’s try out AutoTrain first: First, open the AutoTrain 1. Create a project 2. We can select the dataset repository we created before or upload the dataset again. Then we need to choose the split type, I’ll leave it as Auto. 3. Train the models AutoTrain will try different models and select the best models. Then performs hyper-parameter optimization automatically. The dataset is also processed automatically. The price totally depends on your use case. It can be as low as $10 or it can be more expensive than the current value. The training is done after around 20 minutes, the results are pretty good! The best model’s accuracy is almost %89. Now we can use this model to perform the analysis, it only took about 30 minutes to set up the whole thing. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Modeling without AutoTrain We will use Ray Tune and Hugging Face’s Trainer API to search hyper-parameters and fine-tune a pre-trained deep learning model. We have selected roBERTa base sentiment classification model which is trained on tweets for fine-tuning. We've fine-tuned the model on google collaboratory and it can be found on the notebooks folder in the GitHub repository. Ray tune is a popular library for hyper-parameter optimization which comes with many SOTA algorithms out of the box. It is also possible to use Optuna and SigOpt. We also used [Async Successive Halving Algorithm (ASHA) as the scheduler and HyperOpt as the search algorithm. Which is pretty much a starting point. You can use different schedulers and search algorithms. What will we do? - Import the necessary libraries (a dozen of them) and prepare a dataset class - Define needed functions and methods to process the data - Load the pre-trained model and tokenizer - Run hyper-parameter search - Use the best results for evaluation Let’s start with importing necessary libraries! (all the code is in notebooks/modeling.ipynb and google collaboratory notebook) python # general data science/utilization/visualization imports import json import os import random # progress bar from tqdm import tqdm # data manipulation / reading import numpy as np import pandas as pd # visualization import plotly.express as px import matplotlib.pyplot as plt # pre-defined evaluation metrics from sklearn.metrics import (accuracy_score, f1_score, precision_score, recall_score) from sklearn.model_selection import train_test_split # torch imports import torch import torch.nn as nn from torch.utils.data import DataLoader, Dataset, random_split # huggingface imports import transformers from datasets import load_metric from transformers import (AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments) # ray tune imports for hyperparameter optimization from ray.tune.schedulers import ASHAScheduler, PopulationBasedTraining from ray.tune.suggest.hyperopt import HyperOptSearch We will set a seed for the libraries we use for reproducibility python def seed_all(seed): torch.manual_seed(seed) random.seed(seed) np.random.seed(seed) SEED=42 seed_all(SEED) Now let’s define our dataset class! python class TextClassificationDataset(Dataset): def __init__(self, dataframe): self.labels = dataframe.label.to_list() self.inputs = dataframe.content.to_list() self.labels_to_idx = {k:v for k,v in labels_dict.items()} # copy the labels_dict dictionary def __len__(self): return len(self.inputs) def __getitem__(self, idx): if type(idx)==torch.Tensor: idx = list(idx) input_data = self.inputs[idx] target = self.labels[idx] target = self.labels_to_idx[target] return {'text': input_data, 'label':target} We can download the model easily by specifying HuggingFace hub repository. It is also needed to import the tokenizer for the specified model. We have to provide a function to initialize the model during hyper-parameter optimization. The model will be defined there. The metric to optimize is accuracy, we want this value to be as high as possible. Because of that, we need to load the metric, then define a function to get the predictions and calculate the preferred metric. python model_name = 'cardiffnlp/twitter-roberta-base-sentiment' # we will perform the search to optimize the model accuracy, # we need to specify and load the accuracy metric as a first step metric = load_metric("accuracy") # since we already entered a model name, we can load the tokenizer # we can also load the model but i'll describe it in the model_init function. tokenizer = AutoTokenizer.from_pretrained(model_name) def model_init(): """ Hyperparameter optimization is performed by newly initialized models, therefore we will need to initialize the model again for every single search run. This function initializes and returns the pre-trained model selected with `model_name` """ return AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=4, return_dict=True, ignore_mismatched_sizes=True) # the function to calculate accuracy def compute_metrics(eval_pred): logits, labels = eval_pred predictions = np.argmax(logits, axis=-1) # just pick the indices that has the maximum values return metric.compute(predictions=predictions, references=labels) After defining metric calculation and model initialization function, we can load the data: python file_name = "dataset-11.csv" dataset_path = os.path.join('data/processed', file_name) dataset = pd.read_csv(dataset_path) I also defined two dictionaries for mapping labels to indices and indices to labels. python idx_to_label = dict(enumerate(dataset.label.unique())) labels_dict = {v:k for k,v in idx_to_label.items()} Now we can define the search algorithm and the scheduler for the hyper-parameter-search. python scheduler = ASHAScheduler(metric='objective', mode='max') search_algorithm = HyperOptSearch(metric='objective', mode='max', random_state_seed=SEED) # number of runs for parameter searching n_trials = 40 We also need to tokenize the text data before passing it to the model, we can easily do this by using the loaded tokenizer. Ray Tune works in a black-box setting so I used tokenizer as a default argument for a work-around. Otherwise, an error about tokenizer definition would arise. python def tokenize(sample, tokenizer=tokenizer): tokenized_sample = tokenizer(sample['text'], padding=True, truncation=True) tokenized_sample['label'] = sample['label'] return tokenized_sample Another utility function that returns stratified and tokenized Torch dataset splits: python def prepare_datasets(dataset_df, test_size=.2, val_size=.2): train_set, test_set = train_test_split(dataset_df, test_size=test_size, stratify=dataset_df.label, random_state=SEED) train_set, val_set = train_test_split(train_set, test_size=val_size, stratify=train_set.label, random_state=SEED) # shuffle the dataframes beforehand train_set = train_set.sample(frac=1, random_state=SEED) val_set = val_set.sample(frac=1, random_state=SEED) test_set = test_set.sample(frac=1, random_state=SEED) # convert dataframes to torch datasets train_dataset = TextClassificationDataset(train_set) val_dataset = TextClassificationDataset(val_set) test_dataset = TextClassificationDataset(test_set) # tokenize the datasets tokenized_train_set = train_dataset.map(tokenize) tokenized_val_set = val_dataset.map(tokenize) tokenized_test_set = test_dataset.map(tokenize) # finally return the processed sets return tokenized_train_set, tokenized_val_set, tokenized_test_set Now we can perform the search! Let’s start by processing the data: python tokenized_train_set, tokenized_val_set, tokenized_test_set = prepare_datasets(dataset) training_args = TrainingArguments( 'trial_results', evaluation_strategy="steps", disable_tqdm=True, skip_memory_metrics=True, ) trainer = Trainer( args=training_args, tokenizer=tokenizer, train_dataset=tokenized_train_set, eval_dataset=tokenized_val_set, model_init=model_init, compute_metrics=compute_metrics ) best_run = trainer.hyperparameter_search( direction="maximize", n_trials=n_trials, backend="ray", search_alg=search_algorithm, scheduler=scheduler ) We performed the search with 20 and 40 trials respectively, the results are shown below. The weighted average of F1, Recall, and Precision scores for 20 runs. The weighted average of F1, Recall, and Precision scores for 40 runs. The performance spiked up at the third dataset version. At some point in data labeling, I’ve introduced too much bias to the dataset mistakingly. As we can see its performance becomes more reasonable since the sample variance increased later on. The final model is saved at Google Drive and can be downloaded from here, it is also possible to download via the download_models.py script. | [
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Opinion Classification with Kili and HuggingFace AutoTrain | alperiox | April 28, 2022 | opinion-classification-with-kili | guide | https://huggingface.co/blog/opinion-classification-with-kili | Dataset Let’s start by taking a look at the raw dataset, There are 10 columns and 40130 samples in this dataset. The only column we need is content which is the review of the user. Before starting, we need to define some categories. We have defined 4 categories, - Subscription: Since medium has a subscription option, anything related to users' opinions about subscription features should belong here. - Content: Medium is a sharing platform, there are lots of writings from poetry to advanced artificial intelligence research. Users’ opinions about a variety of topics, the quality of the content should belong here. - Interface: Thoughts about UI, searching articles, recommendation engine, and anything related to the interface should belong here. This also includes payment-related issues. - User Experience: The user’s general thoughts and opinions about the application. Which should be generally abstract without indicating another category. For the labeling part, we need to create a project in Kili’s platform at first. We can use either the web interface of the platform or APIs. Let's see both. From the web interface: From the project list page, we create a multi-class text classification project. After that, on the project’s page, you can add your data by clicking the Add assets button. Currently, you can add at most 25000 samples, but you can extend this limit if you contact the Kili sales team. After we create our project, we need to add jobs. We can prepare a labeling interface from the Settings page Although we have defined 4 categories, it is inevitable to come across reviews that should have multiple categories or completely weird ones. I will add two more labels (which are not to use in modeling) to catch these cases too. In our example, we added two more labels (Other, Multi-label). We also added a named entity recognition (NER) job just to specify how we decided on a label while labeling. The final interface is shown below As you can see from the menu at the left, it is also possible to drop a link that describes your labels on the Instructions page. We can also add other members to our project from Members or add quality measures from the Quality management pages. More information can be found in the documentation. Now, let’s create our project with Python API: At first, we need to import needed libraries (notebooks/kili_project_management.ipynb) python import os #we will process the data (which is a csv file) import pandas as pd #API client from kili.client import Kili #Why not use pretty progress bars? from tqdm import tqdm from dotenv import load_dotenv load_dotenv() In order to access the platform, we need to authenticate our client python API_KEY = os.getenv('KILI_API_KEY') # initialize and authenticate the Kili client kili = Kili(api_key = API_KEY) Now we can start to prepare our interface, the interface is just a dictionary in Python. We will define our jobs, then fill the labels up. Since all labels also could have children labels, we will pass labels as dictionaries too. python labels = ['User experience', 'Subscription', 'Content', 'Other', 'Multi label'] entity_dict = { 'User experience': '#cc4125', 'Subscription': '#4543e6', 'Content': '#3edeb6', } project_name = 'User review dataset for topic classification' project_description = "Medium's app reviews fetched from google play store for topic classification" interface = { 'jobs': { 'JOB_0': { 'mlTask': 'CLASSIFICATION', 'instruction': 'Labels', 'required': 1, 'content': { "categories": {}, "input": "radio", }, }, 'JOB_1': { 'mlTask': "NAMED_ENTITIES_RECOGNITION", 'instruction': 'Entities', 'required': 1, 'content': { 'categories': {}, "input": "radio" }, }, } } # fill the interface json with jobs for label in labels: # converts labels to uppercase and replaces whitespaces with underscores (_) # ex. User experience -> USER_EXPERIENCE # this is the preferred way to fill the interface label_upper = label.strip().upper().replace(' ', '_') # content_dict_0 = interface['jobs']['JOB_0']['content'] categories_0 = content_dict_0['categories'] category = {'name': label, 'children': []} categories_0[label_upper] = category for label, color in entity_dict.items(): label_upper = label.strip().upper().replace(' ', '_') content_dict_1 = interface['jobs']['JOB_1']['content'] categories_1 = content_dict_1['categories'] category = {'name': label, 'children': [], 'color': color} categories_1[label_upper] = category # now we can create our project # this method returns the created project’s id project_id = kili.create_project(json_interface=interface, input_type='TEXT', title=project_name, description=project_description)['id'] We are ready to upload our data to the project. The append_many_to_dataset method can be used to import the data into the platform. By using the Python API, we can import the data by batch of 100 maximum. Here is a simple function to upload the data: python def import_dataframe(project_id:str, dataset:pd.DataFrame, text_data_column:str, external_id_column:str, subset_size:int=100) -> bool: """ Arguments: Inputs - project_id (str): specifies the project to load the data, this is also returned when we create our project - dataset (pandas DataFrame): Dataset that has proper columns for id and text inputs - text_data_column (str): specifies which column has the text input data - external_id_column (str): specifies which column has the ids - subset_size (int): specifies the number of samples to import at a time. Cannot be higher than 100 Outputs: None Returns: True or False regards to process succession """ assert subset_size <= 100, "Kili only allows to upload 100 assets at most at a time onto the app" L = len(dataset) # set 25000 as an upload limit, can be changed if L>25000: print('Kili Projects currently supports maximum 25000 samples as default. Importing first 25000 samples...') L=25000 i = 0 while i+subset_size < L: subset = dataset.iloc[i:i+subset_size] externalIds = subset[external_id_column].astype(str).to_list() contents = subset[text_data_column].astype(str).to_list() kili.append_many_to_dataset(project_id=project_id, content_array=contents, external_id_array=externalIds) i += subset_size return True It simply imports the given dataset DataFrame to a project specified by project_id. We can see the arguments from docstring, we just need to pass our dataset along with the corresponding column names. We’ll just use the sample indices we get when we load the data. And then voila, uploading the data is done! python dataset_path = '../data/processed/lowercase_cleaned_dataset.csv' df = pd.read_csv(dataset_path).reset_index() # reset index to get the indices import_dataframe(project_id, df, 'content', 'index') It wasn’t difficult to use the Python API, the helper methods we used covered many difficulties. We also used another script to check the new samples when we updated the dataset. Sometimes the model performance drop down after the dataset update. This is due to simple mistakes like mislabeling and introducing bias to the dataset. The script simply authenticates and then moves distinct samples of two given dataset versions to To Review. We can change the property of a sample through update_properties_in_assets method: (scripts/move_diff_to_review.py) python # Set up the Kili client and arguments from kili.client import Kili from dotenv import load_dotenv import os import argparse import pandas as pd load_dotenv() parser = argparse.ArgumentParser() parser.add_argument('--first', required=True, type=str, help='Path to first dataframe') parser.add_argument('--second', required=True, type=str, help='Path to second dataframe') args = vars(parser.parse_args()) # set the kili connection up API_KEY = os.getenv('KILI_API_KEY') kili = Kili(API_KEY) # read dataframes df1 = pd.read_csv(args['first']) df2 = pd.read_csv(args['second']) # concating two of them should let us have duplicates of common elements # then we can drop the duplicated elements without keeping any duplicates to get the different elements across the two dataframes diff_df = pd.concat((df1, df2)).drop_duplicates(keep=False) diff_ids = diff_df['id'].to_list() # The changes should be given as an array that # contains the change for every single sample. # That’s why [‘TO_REVIEW’] * len(diff_df) is passed to status_array argument kili.update_properties_in_assets(diff_ids, status_array=['TO_REVIEW'] * len(diff_ids)) print('SET %d ENTRIES TO BE REVIEWED!' % len(diff_df)) | [
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Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel | smangrul | May 2, 2022 | pytorch-fsdp | guide | https://huggingface.co/blog/pytorch-fsdp | Few caveats to be aware of - PyTorch FSDP auto wraps sub-modules, flattens the parameters and shards the parameters in place. Due to this, any optimizer created before model wrapping gets broken and occupies more memory. Hence, it is highly recommended and efficient to prepare model before creating optimizer. Accelerate will automatically wrap the model and create an optimizer for you in case of single model with a warning message. > FSDP Warning: When using FSDP, it is efficient and recommended to call prepare for the model before creating the optimizer > However, below is the recommended way to prepare model and optimizer while using FSDP: diff model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True) + model = accelerator.prepare(model) optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr) - model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(model, - optimizer, train_dataloader, eval_dataloader, lr_scheduler - ) + optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( + optimizer, train_dataloader, eval_dataloader, lr_scheduler + ) - In case of a single model, if you have created optimizer with multiple parameter groups and called prepare with them together, then the parameter groups will be lost and the following warning is displayed: > FSDP Warning: When using FSDP, several parameter groups will be conflated into a single one due to nested module wrapping and parameter flattening. > This is because parameter groups created before wrapping will have no meaning post wrapping due parameter flattening of nested FSDP modules into 1D arrays (which can consume many layers). For instance, below are the named parameters of FSDP model on GPU 0 (When using 2 GPUs. Around 55M (110M/2) params in 1D arrays as this will have the 1st shard of the parameters). Here, if one has applied no weight decay for [bias, LayerNorm.weight] named parameters of unwrapped BERT-Base model, it can’t be applied to the below FSDP wrapped model as there are no named parameters with either of those strings and the parameters of those layers are concatenated with parameters of various other layers. More details mentioned in this issue (The original model parameters' .grads are not set, meaning that they cannot be optimized separately (which is why we cannot support multiple parameter groups)). { '_fsdp_wrapped_module.flat_param': torch.Size([494209]), '_fsdp_wrapped_module._fpw_module.bert.embeddings.word_embeddings._fsdp_wrapped_module.flat_param': torch.Size([11720448]), '_fsdp_wrapped_module._fpw_module.bert.encoder._fsdp_wrapped_module.flat_param': torch.Size([42527232]) } - In case of multiple models, it is necessary to prepare the models before creating optimizers else it will throw an error. - Mixed precision is currently not supported with FSDP as we wait for PyTorch to fix support for it. # How it works 📝 (Source: link) The above workflow gives an overview of what happens behind the scenes when FSDP is activated. Let's first understand how DDP works and how FSDP improves it. In DDP, each worker/accelerator/GPU has a replica of the entire model parameters, gradients and optimizer states. Each worker gets a different batch of data, it goes through the forwards pass, a loss is computed followed by the backward pass to generate gradients. Now, an all-reduce operation is performed wherein each worker gets the gradients from the remaining workers and averaging is done. In this way, each worker now has the same global gradients which are used by the optimizer to update the model parameters. We can see that having full replicas consume a lot of redundant memory on each GPU, which limits the batch size as well as the size of the models. FSDP precisely addresses this by sharding the optimizer states, gradients and model parameters across the data parallel workers. It further facilitates CPU offloading of all those tensors, thereby enabling loading large models which won't fit the available GPU memory. Similar to DDP, each worker gets a different batch of data. During the forward pass, if the CPU offload is enabled, the parameters of the local shard are first copied to the GPU/accelerator. Then, each worker performs all-gather operation for a given FSDP wrapped module/layer(s) to all get the needed parameters, computation is performed followed by releasing/emptying the parameter shards of other workers. This continues for all the FSDP modules. The loss gets computed after the forward pass and during the backward pass, again an all-gather operation is performed to get all the needed parameters for a given FSDP module, computation is performed to get local gradients followed by releasing the shards of other workers. Now, the local gradients are averaged and sharded to each relevant workers using reduce-scatter operation. This allows each worker to update the parameters of its local shard. If CPU offload is activated, the gradients are passed to CPU for updating parameters directly on CPU. Please refer [7, 8, 9] for all the in-depth details on the workings of the PyTorch FSDP and the extensive experimentation carried out using this feature. # Issues If you encounter any issues with the integration part of PyTorch FSDP, please open an Issue in accelerate. But if you have problems with PyTorch FSDP configuration, and deployment - you need to ask the experts in their domains, therefore, please, open a PyTorch Issue instead. # References [1] Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers [2] ZeRO: Memory Optimizations Toward Training Trillion Parameter Models [3] DeepSpeed: Extreme-scale model training for everyone - Microsoft Research [4] Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism [5] Introducing GPipe, an Open Source Library for Efficiently Training Large-scale Neural Network Models [6] Which hardware do you need to train a 176B parameters model? [7] Introducing PyTorch Fully Sharded Data Parallel (FSDP) API | PyTorch [8] Getting Started with Fully Sharded Data Parallel(FSDP) — PyTorch Tutorials 1.11.0+cu102 documentation [9] Training a 1 Trillion Parameter Model With PyTorch Fully Sharded Data Parallel on AWS | by PyTorch | PyTorch | Mar, 2022 | Medium [10] Fit More and Train Faster With ZeRO via DeepSpeed and FairScale | [
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Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel | smangrul | May 2, 2022 | pytorch-fsdp | guide | https://huggingface.co/blog/pytorch-fsdp | # Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel In this post we will look at how we can leverage Accelerate Library for training large models which enables users to leverage the latest features of PyTorch FullyShardedDataParallel (FSDP). # Motivation 🤗 With the ever increasing scale, size and parameters of the Machine Learning (ML) models, ML practitioners are finding it difficult to train or even load such large models on their hardware. On one hand, it has been found that large models learn quickly (data and compute efficient) and are significantly more performant when compared to smaller models [1]; on the other hand, it becomes prohibitive to train such models on most of the available hardware. Distributed training is the key to enable training such large ML models. There have been major recent advances in the field of Distributed Training at Scale. Few the most notable advances are given below: 1. Data Parallelism using ZeRO - Zero Redundancy Optimizer [2] 1. Stage 1: Shards optimizer states across data parallel workers/GPUs 2. Stage 2: Shards optimizer states + gradients across data parallel workers/GPUs 3. Stage 3: Shards optimizer states + gradients + model parameters across data parallel workers/GPUs 4. CPU Offload: Offloads the gradients + optimizer states to CPU building on top of ZERO Stage 2 [3] 2. Tensor Parallelism [4]: Form of model parallelism wherein sharding parameters of individual layers with huge number of parameters across accelerators/GPUs is done in a clever manner to achieve parallel computation while avoiding expensive communication synchronization overheads. 3. Pipeline Parallelism [5]: Form of model parallelism wherein different layers of the model are put across different accelerators/GPUs and pipelining is employed to keep all the accelerators running simultaneously. Here, for instance, the second accelerator/GPU computes on the first micro-batch while the first accelerator/GPU computes on the second micro-batch. 4. 3D parallelism [3]: Employs Data Parallelism using ZERO + Tensor Parallelism + Pipeline Parallelism to train humongous models in the order of 100s of Billions of parameters. For instance, BigScience 176B parameters Language Model employ this [6]. In this post we will look at Data Parallelism using ZeRO and more specifically the latest PyTorch feature FullyShardedDataParallel (FSDP). DeepSpeed and FairScale have implemented the core ideas of the ZERO paper. These have already been integrated in transformers Trainer and accompanied by great blog Fit More and Train Faster With ZeRO via DeepSpeed and FairScale [10]. PyTorch recently upstreamed the Fairscale FSDP into PyTorch Distributed with additional optimizations. # Accelerate 🚀: Leverage PyTorch FSDP without any code changes We will look at the task of Causal Language Modelling using GPT-2 Large (762M) and XL (1.5B) model variants. Below is the code for pre-training GPT-2 model. It is similar to the official causal language modeling example here with the addition of 2 arguments n_train (2000) and n_val (500) to prevent preprocessing/training on entire data in order to perform quick proof of concept benchmarks. run_clm_no_trainer.py Sample FSDP config after running the command accelerate config: bash compute_environment: LOCAL_MACHINE deepspeed_config: {} distributed_type: FSDP fsdp_config: min_num_params: 2000 offload_params: false sharding_strategy: 1 machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main mixed_precision: 'no' num_machines: 1 num_processes: 2 use_cpu: false | [
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Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel | smangrul | May 2, 2022 | pytorch-fsdp | guide | https://huggingface.co/blog/pytorch-fsdp | Multi-GPU FSDP Here, we experiment on the Single-Node Multi-GPU setting. We compare the performance of Distributed Data Parallel (DDP) and FSDP in various configurations. First, GPT-2 Large(762M) model is used wherein DDP works with certain batch sizes without throwing Out Of Memory (OOM) errors. Next, GPT-2 XL (1.5B) model is used wherein DDP fails with OOM error even on batch size of 1. We observe that FSDP enables larger batch sizes for GPT-2 Large model and it enables training the GPT-2 XL model with decent batch size unlike DDP. Hardware setup: 2X24GB NVIDIA Titan RTX GPUs. Command for training GPT-2 Large Model (762M parameters): bash export BS=#`try with different batch sizes till you don't get OOM error, #i.e., start with larger batch size and go on decreasing till it fits on GPU` time accelerate launch run_clm_no_trainer.py \ --model_name_or_path gpt2-large \ --dataset_name wikitext \ --dataset_config_name wikitext-2-raw-v1 \ --per_device_train_batch_size $BS --per_device_eval_batch_size $BS --num_train_epochs 1 --block_size 12 Sample FSDP Run: | Method | Batch Size Max ($BS) | Approx Train Time (minutes) | Notes | | --- | --- | --- | --- | | DDP (Distributed Data Parallel) | 7 | 15 | | | DDP + FP16 | 7 | 8 | | | FSDP with SHARD_GRAD_OP | 11 | 11 | | | FSDP with min_num_params = 1M + FULL_SHARD | 15 | 12 | | | FSDP with min_num_params = 2K + FULL_SHARD | 15 | 13 | | | FSDP with min_num_params = 1M + FULL_SHARD + Offload to CPU | 20 | 23 | | | FSDP with min_num_params = 2K + FULL_SHARD + Offload to CPU | 22 | 24 | | Table 1: Benchmarking FSDP on GPT-2 Large (762M) model With respect to DDP, from Table 1 we can observe that FSDP enables larger batch sizes, up to 2X-3X without and with CPU offload setting, respectively. In terms of train time, DDP with mixed precision is the fastest followed by FSDP using ZERO Stage 2 and Stage 3, respectively. As the task of causal language modelling always has fixed context sequence length (--block_size), the train time speedup with FSDP wasn’t that great. For applications with dynamic batching, FSDP which enables larger batch sizes will likely have considerable speed up in terms of train time. FSDP mixed precision support currently has few issues with transformer. Once this is supported, the training time speed up will further improve considerably. | [
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Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel | smangrul | May 2, 2022 | pytorch-fsdp | guide | https://huggingface.co/blog/pytorch-fsdp | Capabilities and limitations of the FSDP Integration Let’s dive into the current support that Accelerate provides for FSDP integration and the known limitations. Required PyTorch version for FSDP support: PyTorch Nightly (or 1.12.0 if you read this after it has been released) as the model saving with FSDP activated is only available with recent fixes. Configuration through CLI: 1. Sharding Strategy: [1] FULL_SHARD, [2] SHARD_GRAD_OP 2. Min Num Params: FSDP's minimum number of parameters for Default Auto Wrapping. 3. Offload Params: Decides Whether to offload parameters and gradients to CPU. For more control, users can leverage the FullyShardedDataParallelPlugin wherein they can specify auto_wrap_policy, backward_prefetch and ignored_modules. After creating an instance of this class, users can pass it when creating the Accelerator object. For more information on these options, please refer to the PyTorch FullyShardedDataParallel code. Next, we will see the importance of the min_num_params config. Below is an excerpt from [8] detailing the importance of FSDP Auto Wrap Policy. (Source: link) When using the default_auto_wrap_policy, a layer is wrapped in FSDP module if the number of parameters in that layer is more than the min_num_params . The code for finetuning BERT-Large (330M) model on the GLUE MRPC task is the official complete NLP example outlining how to properly use FSDP feature with the addition of utilities for tracking peak memory usage. fsdp_with_peak_mem_tracking.py We leverage the tracking functionality support in Accelerate to log the train and evaluation peak memory usage along with evaluation metrics. Below is the snapshot of the plots from wandb run. We can observe that the DDP takes twice as much memory as FSDP with auto wrap. FSDP without auto wrap takes more memory than FSDP with auto wrap but considerably less than that of DDP. FSDP with auto wrap with min_num_params=2k takes marginally less memory when compared to setting with min_num_params=1M. This highlights the importance of the FSDP Auto Wrap Policy and users should play around with the min_num_params to find the setting which considerably saves memory and isn’t resulting in lot of communication overhead. PyTorch team is working on auto tuning tool for this config as mentioned in [8]. | [
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Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel | smangrul | May 2, 2022 | pytorch-fsdp | guide | https://huggingface.co/blog/pytorch-fsdp | CPU Offloading to enable training humongous models that won’t fit the GPU memory Command for training GPT-2 XL Model (1.5B parameters): bash export BS=#`try with different batch sizes till you don't get OOM error, #i.e., start with larger batch size and go on decreasing till it fits on GPU` time accelerate launch run_clm_no_trainer.py \ --model_name_or_path gpt2-xl \ --dataset_name wikitext \ --dataset_config_name wikitext-2-raw-v1 \ --per_device_train_batch_size $BS --per_device_eval_batch_size $BS --num_train_epochs 1 --block_size 12 | Method | Batch Size Max ($BS) | Num GPUs | Approx Train Time (Hours) | Notes | | --- | --- | --- | --- | --- | | DDP | 1 | 1 | NA | OOM Error RuntimeError: CUDA out of memory. Tried to allocate 40.00 MiB (GPU 0; 23.65 GiB total capacity; 22.27 GiB already allocated; 20.31 MiB free; 22.76 GiB reserved in total by PyTorch) | | DDP | 1 | 2 | NA | OOM Error RuntimeError: CUDA out of memory. Tried to allocate 40.00 MiB (GPU 0; 23.65 GiB total capacity; 22.27 GiB already allocated; 20.31 MiB free; 22.76 GiB reserved in total by PyTorch) | | DDP + FP16 | 1 | 1 | NA | OOM Error RuntimeError: CUDA out of memory. Tried to allocate 40.00 MiB (GPU 0; 23.65 GiB total capacity; 22.27 GiB already allocated; 20.31 MiB free; 22.76 GiB reserved in total by PyTorch) | | FSDP with min_num_params = 2K | 5 | 2 | 0.6 | | | FSDP with min_num_params = 2K + Offload to CPU | 10 | 1 | 3 | | | FSDP with min_num_params = 2K + Offload to CPU | 14 | 2 | 1.16 | | Table 2: Benchmarking FSDP on GPT-2 XL (1.5B) model From Table 2, we can observe that DDP (w and w/o fp16) isn’t even able to run with batch size of 1 and results in CUDA OOM error. FSDP with Zero-Stage 3 is able to be run on 2 GPUs with batch size of 5 (effective batch size =10 (5 X 2)). FSDP with CPU offload can further increase the max batch size to 14 per GPU when using 2 GPUs. FSDP with CPU offload enables training GPT-2 1.5B model on a single GPU with a batch size of 10. This enables ML practitioners with minimal compute resources to train such large models, thereby democratizing large model training. | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | Markov Property In papers, you’ll see that the RL process is called the Markov Decision Process (MDP). We’ll talk again about the Markov Property in the following units. But if you need to remember something today about it, Markov Property implies that our agent needs only the current state to decide what action to take and not the history of all the states and actions they took before. | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | The Reinforcement Learning Framework | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | The RL Process The RL Process: a loop of state, action, reward and next state Source: Reinforcement Learning: An Introduction, Richard Sutton and Andrew G. Barto To understand the RL process, let’s imagine an agent learning to play a platform game: - Our Agent receives state \(S_0\) from the Environment — we receive the first frame of our game (Environment). - Based on that state \(S_0\), the Agent takes action \(A_0\) — our Agent will move to the right. - Environment goes to a new state \(S_1\) — new frame. - The environment gives some reward \(R_1\) to the Agent — we’re not dead (Positive Reward +1). This RL loop outputs a sequence of state, action, reward and next state. The agent's goal is to maximize its cumulative reward, called the expected return. | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | The two main approaches for solving RL problems ⇒ Now that we learned the RL framework, how do we solve the RL problem? In other terms, how to build an RL agent that can select the actions that maximize its expected cumulative reward? | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | Keep learning, stay awesome, | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | The big picture The idea behind Reinforcement Learning is that an agent (an AI) will learn from the environment by interacting with it (through trial and error) and receiving rewards (negative or positive) as feedback for performing actions. Learning from interaction with the environment comes from our natural experiences. For instance, imagine putting your little brother in front of a video game he never played, a controller in his hands, and letting him alone. Your brother will interact with the environment (the video game) by pressing the right button (action). He got a coin, that’s a +1 reward. It’s positive, he just understood that in this game he must get the coins. But then, he presses right again and he touches an enemy, he just died -1 reward. By interacting with his environment through trial and error, your little brother understood that he needed to get coins in this environment but avoid the enemies. Without any supervision, the child will get better and better at playing the game. That’s how humans and animals learn, through interaction. Reinforcement Learning is just a computational approach of learning from action. | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | A formal definition If we take now a formal definition: > Reinforcement learning is a framework for solving control tasks (also called decision problems) by building agents that learn from the environment by interacting with it through trial and error and receiving rewards (positive or negative) as unique feedback. > ⇒ But how Reinforcement Learning works? | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | The “Deep” in Reinforcement Learning ⇒ What we've talked about so far is Reinforcement Learning. But where does the "Deep" come into play? Deep Reinforcement Learning introduces deep neural networks to solve Reinforcement Learning problems — hence the name “deep”. For instance, in the next article, we’ll work on Q-Learning (classic Reinforcement Learning) and then Deep Q-Learning both are value-based RL algorithms. You’ll see the difference is that in the first approach, we use a traditional algorithm to create a Q table that helps us find what action to take for each state. In the second approach, we will use a Neural Network (to approximate the q value). Schema inspired by the Q learning notebook by Udacity If you are not familiar with Deep Learning you definitely should watch the fastai Practical Deep Learning for Coders (Free) That was a lot of information, if we summarize: - Reinforcement Learning is a computational approach of learning from action. We build an agent that learns from the environment by interacting with it through trial and error and receiving rewards (negative or positive) as feedback. - The goal of any RL agent is to maximize its expected cumulative reward (also called expected return) because RL is based on the reward hypothesis, which is that all goals can be described as the maximization of the expected cumulative reward. - The RL process is a loop that outputs a sequence of state, action, reward and next state. - To calculate the expected cumulative reward (expected return), we discount the rewards: the rewards that come sooner (at the beginning of the game) are more probable to happen since they are more predictable than the long term future reward. - To solve an RL problem, you want to find an optimal policy, the policy is the “brain” of your AI that will tell us what action to take given a state. The optimal one is the one who gives you the actions that max the expected return. - There are two ways to find your optimal policy: 1. By training your policy directly: policy-based methods. 2. By training a value function that tells us the expected return the agent will get at each state and use this function to define our policy: value-based methods. - Finally, we speak about Deep RL because we introduces deep neural networks to estimate the action to take (policy-based) or to estimate the value of a state (value-based) hence the name “deep.” --- Now that you've studied the bases of Reinforcement Learning, you’re ready to train your first lander agent to land correctly on the Moon 🌕 and share it with the community through the Hub 🔥 Start the tutorial here 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/unit1.ipynb And since the best way to learn and avoid the illusion of competence is to test yourself. We wrote a quiz to help you find where you need to reinforce your study. Check your knowledge here 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/quiz.md Congrats on finishing this chapter! That was the biggest one, and there was a lot of information. And congrats on finishing the tutorial. You’ve just trained your first Deep RL agent and shared it on the Hub 🥳. That’s normal if you still feel confused with all these elements. This was the same for me and for all people who studied RL. Take time to really grasp the material before continuing. It’s important to master these elements and having a solid foundations before entering the fun part. We published additional readings in the syllabus if you want to go deeper 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/README.md Naturally, during the course, we’re going to use and explain these terms again, but it’s better to understand them before diving into the next chapters. In the next chapter, we’re going to learn about Q-Learning and dive deeper into the value-based methods. And don't forget to share with your friends who want to learn 🤗 ! Finally, we want to improve and update the course iteratively with your feedback*. If you have some, please fill this form 👉 https://forms.gle/3HgA7bEHwAmmLfwh9 | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | Exploration/ Exploitation tradeoff Finally, before looking at the different methods to solve Reinforcement Learning problems, we must cover one more very important topic: the exploration/exploitation trade-off. - Exploration is exploring the environment by trying random actions in order to find more information about the environment. - Exploitation is exploiting known information to maximize the reward. Remember, the goal of our RL agent is to maximize the expected cumulative reward. However, we can fall into a common trap. Let’s take an example: In this game, our mouse can have an infinite amount of small cheese (+1 each). But at the top of the maze, there is a gigantic sum of cheese (+1000). However, if we only focus on exploitation, our agent will never reach the gigantic sum of cheese. Instead, it will only exploit the nearest source of rewards, even if this source is small (exploitation). But if our agent does a little bit of exploration, it can discover the big reward (the pile of big cheese). This is what we call the exploration/exploitation trade-off. We need to balance how much we explore the environment and how much we exploit what we know about the environment. Therefore, we must define a rule that helps to handle this trade-off. We’ll see in future chapters different ways to handle it. If it’s still confusing, think of a real problem: the choice of a restaurant: Source: Berkley AI Course - Exploitation: You go every day to the same one that you know is good and take the risk to miss another better restaurant. - Exploration: Try restaurants you never went to before, with the risk of having a bad experience but the probable opportunity of a fantastic experience. To recap: | [
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An Introduction to Deep Reinforcement Learning | ThomasSimonini | May 4, 2022 | deep-rl-intro | rl | https://huggingface.co/blog/deep-rl-intro | Rewards and the discounting The reward is fundamental in RL because it’s the only feedback for the agent. Thanks to it, our agent knows if the action taken was good or not. The cumulative reward at each time step t can be written as: The cumulative reward equals to the sum of all rewards of the sequence. Which is equivalent to: The cumulative reward = rt+1 (rt+k+1 = rt+0+1 = rt+1)+ rt+2 (rt+k+1 = rt+1+1 = rt+2) + ... However, in reality, we can’t just add them like that. The rewards that come sooner (at the beginning of the game) are more likely to happen since they are more predictable than the long-term future reward. Let’s say your agent is this tiny mouse that can move one tile each time step, and your opponent is the cat (that can move too). Your goal is to eat the maximum amount of cheese before being eaten by the cat. As we can see in the diagram, it’s more probable to eat the cheese near us than the cheese close to the cat (the closer we are to the cat, the more dangerous it is). Consequently, the reward near the cat, even if it is bigger (more cheese), will be more discounted since we’re not really sure we’ll be able to eat it. To discount the rewards, we proceed like this: 1. We define a discount rate called gamma. It must be between 0 and 1. Most of the time between 0.99 and 0.95. - The larger the gamma, the smaller the discount. This means our agent cares more about the long-term reward. - On the other hand, the smaller the gamma, the bigger the discount. This means our agent cares more about the short term reward (the nearest cheese). 2. Then, each reward will be discounted by gamma to the exponent of the time step. As the time step increases, the cat gets closer to us, so the future reward is less and less likely to happen. Our discounted cumulative expected rewards is: | [
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