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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | References "Distributed Deep Learning in Open Collaborations", ArXiv Code for sahajBERT experiments in the DeDLOC repository. | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Conclusion In this blog post, we have discussed the method that can enable collaborative pretraining of neural networks with sahajBERT as the first truly successful example of applying it to a real-world problem. What does this all mean for the broader ML community? First, it is now possible to run large-scale distributed pretraining with your friends, and we hope to see a lot of cool new models that were previously less feasible to obtain. Also, our result might be important for multilingual NLP, since now the community for any language can train their own models without the need for significant computational resources concentrated in one place. | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Acknowledgements The DeDLOC paper and sahajBERT training experiment were created by Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin, Lucile Saulnier, Quentin Lhoest, Anton Sinitsin, Dmitry Popov, Dmitry Pyrkin, Maxim Kashirin, Alexander Borzunov, Albert Villanova del Moral, Denis Mazur, Ilia Kobelev, Yacine Jernite, Thomas Wolf, and Gennady Pekhimenko. This project is the result of a collaboration between Hugging Face, Yandex Research, HSE University, MIPT, University of Toronto and Vector Institute. In addition, we would like to thank Stas Bekman, Dmitry Abulkhanov, Roman Zhytar, Alexander Ploshkin, Vsevolod Plokhotnyuk and Roman Kail for their invaluable help with building the training infrastructure. Also, we thank Abhishek Thakur for helping with downstream evaluation and Tanmoy Sarkar with Omar Sanseviero, who helped us organize the collaborative experiment and gave regular status updates to the participants over the course of the training run. Below, you can see all participants of the collaborative experiment: | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Distributed Deep Learning in Open Collaborations | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | # Deep Learning over the Internet: Training Language Models Collaboratively With the additional help of Quentin Lhoest and Sylvain Lesage. Modern language models often require a significant amount of compute for pretraining, making it impossible to obtain them without access to tens and hundreds of GPUs or TPUs. Though in theory it might be possible to combine the resources of multiple individuals, in practice, such distributed training methods have previously seen limited success because connection speeds over the Internet are way slower than in high-performance GPU supercomputers. In this blog post, we describe DeDLOC — a new method for collaborative distributed training that can adapt itself to the network and hardware constraints of participants. We show that it can be successfully applied in real-world scenarios by pretraining sahajBERT, a model for the Bengali language, with 40 volunteers. On downstream tasks in Bengali, this model achieves nearly state-of-the-art quality with results comparable to much larger models that used hundreds of high-tier accelerators. | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Dataset The last thing we need to cover is the training dataset. As you probably know, the great strength of pretrained models like BERT or ALBERT is that you don't need an annotated dataset, but just a lot of texts. To train sahajBERT, we used the Bengali Wikipedia dump from 03/20/2021 and the Bengali subset of OSCAR (600MB + 6GB of text). These two datasets can easily be downloaded from the HF Hub. However, loading an entire dataset requires time and storage — two things that our peers do not necessarily have. To make the most of the resources provided by the participants, we have implemented dataset streaming, which allows them to train the model nearly as soon as they join the network. Specifically, the examples in the dataset are downloaded and transformed in parallel to the training. We can also shuffle the dataset so that our peers have little chance to process the same examples at the same time. As the dataset is not downloaded and preprocessed in advance, the transformations needed to go from plain text to a training example (shown in the figure below) are done on the fly. From a raw sample to a training sample The dataset streaming mode is available from version v1.9 of the 🤗 datasets library, so you can use it right now as follows: python from datasets import load_dataset oscar_dataset = load_dataset("oscar", name="unshuffled_deduplicated_bn", streaming=True) 💡 Learn more about loading datasets in streaming mode in the documentation | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Training with volunteers In its most frequently used version, distributed training with multiple GPUs is pretty straightforward. Recall that when doing deep learning, you usually compute gradients of your loss function averaged across many examples in a batch of training data. In case of data-parallel distributed DL, you simply split the data across multiple workers, compute gradients separately, and then average them once the local batches are processed. When the average gradient is computed on all workers, we adjust the model weights with the optimizer and continue training our model. You can see an illustration of different tasks that are executed below. Typical machine learning tasks executed by peers in distributed training, possibly with a separation of roles Often, to reduce the amount of synchronization and to stabilize the learning process, we can accumulate the gradients for N batches before averaging, which is equivalent to increasing the actual batch size N times. This approach, combined with the observation that most state-of-the-art language models use large batches, led us to a simple idea: let's accumulate one very large batch across all volunteer devices before each optimizer step! Along with complete equivalence to regular distributed training and easy scalability, this method also has the benefit of built-in fault tolerance, which we illustrate below. Let's consider a couple of potential failure cases that we might encounter throughout a collaborative experiment. By far, the most frequent scenario is that one or several peers disconnect from the training procedure: they might have an unstable connection or simply want to use their GPUs for something else. In this case, we only suffer a minor setback of training: the contribution of these peers gets deducted from the currently accumulated batch size, but other participants will compensate for that with their gradients. Also, if more peers join, the target batch size will simply be reached faster, and our training procedure will naturally speed up. You can see a demonstration of this in the video: | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | 🌟 A journey of collaboration: join us, follow our progress Every journey starts with a first step, and ours was the public release of Optimum. Join us and make your first step by giving the library a Star, so you can follow along as we introduce new supported hardware, acceleration techniques and optimized models. If you would like to see new hardware and features be supported in Optimum, or you are interested in joining us to work at the intersection of software and hardware, please reach out to us at [email protected] | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | 🏭 Optimum puts Transformers to work To get optimal performance training and serving models, the model acceleration techniques need to be specifically compatible with the targeted hardware. Each hardware platform offers specific software tooling, features and knobs that can have a huge impact on performance. Similarly, to take advantage of advanced model acceleration techniques like sparsity and quantization, optimized kernels need to be compatible with the operators on silicon, and specific to the neural network graph derived from the model architecture. Diving into this 3-dimensional compatibility matrix and how to use model acceleration libraries is daunting work, which few Machine Learning Engineers have experience on. Optimum aims to make this work easy, providing performance optimization tools targeting efficient AI hardware, built in collaboration with our Hardware Partners, and turn Machine Learning Engineers into ML Optimization wizards. With the Transformers library, we made it easy for researchers and engineers to use state-of-the-art models, abstracting away the complexity of frameworks, architectures and pipelines. With the Optimum library, we are making it easy for engineers to leverage all the available hardware features at their disposal, abstracting away the complexity of model acceleration on hardware platforms. | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | 🤗 Optimum in practice: how to quantize a model for Intel Xeon CPU | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | 🤔 Why quantization is important but tricky to get right Pre-trained language models such as BERT have achieved state-of-the-art results on a wide range of natural language processing tasks, other Transformer based models such as ViT and Speech2Text have achieved state-of-the-art results on computer vision and speech tasks respectively: transformers are everywhere in the Machine Learning world and are here to stay. However, putting transformer-based models into production can be tricky and expensive as they need a lot of compute power to work. To solve this many techniques exist, the most popular being quantization. Unfortunately, in most cases quantizing a model requires a lot of work, for many reasons: 1. The model needs to be edited: some ops need to be replaced by their quantized counterparts, new ops need to be inserted (quantization and dequantization nodes), and others need to be adapted to the fact that weights and activations will be quantized. This part can be very time-consuming because frameworks such as PyTorch work in eager mode, meaning that the changes mentioned above need to be added to the model implementation itself. PyTorch now provides a tool called torch.fx that allows you to trace and transform your model without having to actually change the model implementation, but it is tricky to use when tracing is not supported for your model out of the box. On top of the actual editing, it is also necessary to find which parts of the model need to be edited, which ops have an available quantized kernel counterpart and which ops don't, and so on. 2. Once the model has been edited, there are many parameters to play with to find the best quantization settings: - Which kind of observers should I use for range calibration? - Which quantization scheme should I use? - Which quantization related data types (int8, uint8, int16) are supported on my target device? 3. Balance the trade-off between quantization and an acceptable accuracy loss. 4. Export the quantized model for the target device. Although PyTorch and TensorFlow made great progress in making things easy for quantization, the complexities of transformer based models makes it hard to use the provided tools out of the box and get something working without putting up a ton of effort. | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | Why 🤗 Optimum? | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | 💡 How Intel is solving quantization and more with Neural Compressor Intel® Neural Compressor (formerly referred to as Low Precision Optimization Tool or LPOT) is an open-source python library designed to help users deploy low-precision inference solutions. The latter applies low-precision recipes for deep-learning models to achieve optimal product objectives, such as inference performance and memory usage, with expected performance criteria. Neural Compressor supports post-training quantization, quantization-aware training and dynamic quantization. In order to specify the quantization approach, objective and performance criteria, the user must provide a configuration yaml file specifying the tuning parameters. The configuration file can either be hosted on the Hugging Face's Model Hub or can be given through a local directory path. | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | 🔮 State-of-the-Art Models The collaboration with our Hardware Partners will yield hardware-specific optimized model configurations and artifacts, which we will make available to the AI community via the Hugging Face Model Hub. We hope that Optimum and hardware-optimized models will accelerate the adoption of efficiency in production workloads, which represent most of the aggregate energy spent on Machine Learning. And most of all, we hope that Optimum will accelerate the adoption of Transformers at scale, not just for the biggest tech companies, but for all of us. | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | ⚡️State of the Art Hardware Optimum will focus on achieving optimal production performance on dedicated hardware, where software and hardware acceleration techniques can be applied for maximum efficiency. We will work hand in hand with our Hardware Partners to enable, test and maintain acceleration, and deliver it in an easy and accessible way through Optimum, as we did with Intel and Neural Compressor. We will soon announce new Hardware Partners who have joined us on our journey toward Machine Learning efficiency. | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | 🔥 How to easily quantize Transformers for Intel Xeon CPUs with Optimum | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | 🤯 Scaling Transformers is hard What do Tesla, Google, Microsoft and Facebook all have in common? Well many things, but one of them is they all run billions of Transformer model predictions every day. Transformers for AutoPilot to drive your Tesla (lucky you!), for Gmail to complete your sentences, for Facebook to translate your posts on the fly, for Bing to answer your natural language queries. Transformers have brought a step change improvement in the accuracy of Machine Learning models, have conquered NLP and are now expanding to other modalities starting with Speech and Vision. But taking these massive models into production, and making them run fast at scale is a huge challenge for any Machine Learning Engineering team. What if you don’t have hundreds of highly skilled Machine Learning Engineers on payroll like the above companies? Through Optimum, our new open source library, we aim to build the definitive toolkit for Transformers production performance, and enable maximum efficiency to train and run models on specific hardware. | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | # Introducing 🤗 Optimum: The Optimization Toolkit for Transformers at Scale This post is the first step of a journey for Hugging Face to democratize state-of-the-art Machine Learning production performance. To get there, we will work hand in hand with our Hardware Partners, as we have with Intel below. Join us in this journey, and follow Optimum, our new open source library! | [
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Introducing Optimum: The Optimization Toolkit for Transformers at Scale | mfuntowicz | September 14, 2021 | hardware-partners-program | guide | https://huggingface.co/blog/hardware-partners-program | Follow 🤗 Optimum: a journey to democratize ML production performance | [
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Hugging Face and Graphcore partner for IPU-optimized Transformers | sallydoherty | September 14, 2021 | graphcore | graphcore, partnerships | https://huggingface.co/blog/graphcore | # Hugging Face and Graphcore partner for IPU-optimized Transformers > | [
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Hugging Face and Graphcore partner for IPU-optimized Transformers | sallydoherty | September 14, 2021 | graphcore | graphcore, partnerships | https://huggingface.co/blog/graphcore | Speaking at the 2021 AI Hardware Summit, Hugging Face announced the launch of their new Hardware Partner Program, including device-optimized models and software integrations. Here, Graphcore - creators of the Intelligence Processing Unit (IPU) and a founding member of the program – explain how their partnership with Hugging Face will allow developers to easily accelerate their use of state-of-the-art Transformer models. Graphcore and Hugging Face are two companies with a common goal – to make it easier for innovators to harness the power of machine intelligence. Hugging Face’s Hardware Partner Program will allow developers using Graphcore systems to deploy state-of-the-art Transformer models, optimised for our Intelligence Processing Unit (IPU), at production scale, with minimum coding complexity. | [
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Hugging Face and Graphcore partner for IPU-optimized Transformers | sallydoherty | September 14, 2021 | graphcore | graphcore, partnerships | https://huggingface.co/blog/graphcore | Optimising Transformers for Production Transformers have completely transformed (pun intended) the field of AI. Models such as BERT are widely used by Graphcore customers in a huge array of applications, across NLP and beyond. These multi-talented models can perform feature extraction, text generation, sentiment analysis, translation and many more functions. Already, Hugging Face plays host to hundreds of Transformers, from the French-language CamemBERT to ViT which applies lessons learned in NLP to computer vision. The Transformers library is downloaded an average of 2 million times every month and demand is growing. With a user base of more than 50,000 developers – Hugging Face has seen the fastest ever adoption of an open-source project. Now, with its Hardware Partner Program, Hugging Face is connecting the ultimate Transformer toolset with today's most advanced AI hardware. Using Optimum, a new open-source library and toolkit, developers will be able to access hardware-optimized models certified by Hugging Face. These are being developed in a collaboration between Graphcore and Hugging Face, with the first IPU-optimized models appearing on Optimum later this year. Ultimately, these will cover a wide range of applications, from vision and speech to translation and text generation. Hugging Face CEO Clément Delangue said: “Developers all want access to the latest and greatest hardware – like the Graphcore IPU, but there’s always that question of whether they’ll have to learn new code or processes. With Optimum and the Hugging Face Hardware Program, that’s just not an issue. It’s essentially plug-and-play". | [
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Hugging Face and Graphcore partner for IPU-optimized Transformers | sallydoherty | September 14, 2021 | graphcore | graphcore, partnerships | https://huggingface.co/blog/graphcore | SOTA Models meet SOTA Hardware Prior to the announcement of the Hugging Face Partnership, we had demonstrated the power of the IPU to accelerate state-of-the-art Transformer models with a special Graphcore-optimised implementation of Hugging Face BERT using Pytorch. Full details of this example can be found in the Graphcore blog BERT-Large training on the IPU explained. The dramatic benchmark results for BERT running on a Graphcore system, compared with a comparable GPU-based system are surely a tantalising prospect for anyone currently running the popular NLP model on something other than the IPU. This type of acceleration can be game changing for machine learning researchers and engineers, winning them back valuable hours of training time and allowing them many more iterations when developing new models. Now Graphcore users will be able to unlock such performance advantages, through the Hugging Face platform, with its elegant simplicity and superlative range of models. Together, Hugging Face and Graphcore are helping even more people to access the power of Transformers and accelerate the AI revolution. Visit the Hugging Face Hardware Partner portal to learn more about Graphcore IPU systems and how to gain access | [
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Hugging Face and Graphcore partner for IPU-optimized Transformers | sallydoherty | September 14, 2021 | graphcore | graphcore, partnerships | https://huggingface.co/blog/graphcore | What is an Intelligence Processing Unit? IPUs are the processors that power Graphcore’s IPU-POD datacenter compute systems. This new type of processor is designed to support the very specific computational requirements of AI and machine learning. Characteristics such as fine-grained parallelism, low precision arithmetic, and the ability to handle sparsity have been built into our silicon. Instead of adopting a SIMD/SIMT architecture like GPUs, Graphcore’s IPU uses a massively parallel, MIMD architecture, with ultra-high bandwidth memory placed adjacent to the processor cores, right on the silicon die. This design delivers high performance and new levels of efficiency, whether running today’s most popular models, such as BERT and EfficientNet, or exploring next-generation AI applications. Software plays a vital role in unlocking the IPU’s capabilities. Our Poplar SDK has been co-designed with the processor since Graphcore’s inception. Today it fully integrates with standard machine learning frameworks, including PyTorch and TensorFlow, as well as orchestration and deployment tools such as Docker and Kubernetes. Making Poplar compatible with these widely used, third-party systems allows developers to easily port their models from their other compute platforms and start taking advantage of the IPU’s advanced AI capabilities. | [
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] |
Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Share Some Love You can now like any model, dataset, or Space on http://huggingface.co, meaning you can share some love with the community ❤️. You can also keep an eye on who's liking what by clicking on the likes box 👀. Go ahead and like your own repos, we're not judging 😉. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | NEW: AutoNLP In Your Browser We released a new AutoNLP experience: a web interface to train models straight from your browser! Now all it takes is a few clicks to train, evaluate and deploy 🤗 Transformers models on your own data. Try it out - NO CODE needed! | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | New Widgets The Hub has 18 widgets that allow users to try out models directly in the browser. With our latest integrations to Sentence Transformers, we also introduced two new widgets: feature extraction and sentence similarity. The latest audio classification widget enables many cool use cases: language identification, street sound detection 🚨, command recognition, speaker identification, and more! You can try this out with transformers and speechbrain models today! 🔊 (Beware, when you try some of the models, you might need to bark out loud) You can try our early demo of structured data classification with Scikit-learn. And finally, we also introduced new widgets for image-related models: text to image, image classification, and object detection. Try image classification with Google's ViT model here and object detection with Facebook AI's DETR model here! | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Research At BigScience we held our first live event (since the kick off) in July BigScience Episode #1. Our second event BigScience Episode #2 was held on September 20th, 2021 with technical talks and updates by the BigScience working groups and invited talks by Jade Abbott (Masakhane), Percy Liang (Stanford CRFM), Stella Biderman (EleutherAI) and more. We have completed the first large-scale training on Jean Zay, a 13B English only decoder model (you can find the details here), and we're currently deciding on the architecture of the second model. The organization working group has filed the application for the second half of the compute budget: Jean Zay V100 : 2,500,000 GPU hours. 🚀 In June, we shared the result of our collaboration with the Yandex research team: DeDLOC, a method to collaboratively train your large neural networks, i.e. without using an HPC cluster, but with various accessible resources such as Google Colaboratory or Kaggle notebooks, personal computers or preemptible VMs. Thanks to this method, we were able to train sahajBERT, a Bengali language model, with 40 volunteers! And our model competes with the state of the art, and even is the best for the downstream task of classification on Soham News Article Classification dataset. You can read more about it in this blog post. This is a fascinating line of research because it would make model pre-training much more accessible (financially speaking)! In June our paper, How Many Data Points is a Prompt Worth?, got a Best Paper award at NAACL! In it, we reconcile and compare traditional and prompting approaches to adapt pre-trained models, finding that human-written prompts are worth up to thousands of supervised data points on new tasks. You can also read its blog post. We're looking forward to EMNLP this year where we have four accepted papers! - Our paper "Datasets: A Community Library for Natural Language Processing" documents the Hugging Face Datasets project that has over 300 contributors. This community project gives easy access to hundreds of datasets to researchers. It has facilitated new use cases of cross-dataset NLP, and has advanced features for tasks like indexing and streaming large datasets. - Our collaboration with researchers from TU Darmstadt lead to another paper accepted at the conference ("Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning"). In this paper, we show that prompt-based fine-tuned language models (which achieve strong performance in few-shot setups) still suffer from learning surface heuristics (sometimes called dataset biases), a pitfall that zero-shot models don't exhibit. - Our submission "Block Pruning For Faster Transformers" has also been accepted as a long paper. In this paper, we show how to use block sparsity to obtain both fast and small Transformer models. Our experiments yield models which are 2.4x faster and 74% smaller than BERT on SQuAD. | [
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] |
Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Coming soon: Infinity Transformers latency down to 1ms? 🤯🤯🤯 We have been working on a really sleek solution to achieve unmatched efficiency for state-of-the-art Transformer models, for companies to deploy in their own infrastructure. - Infinity comes as a single-container and can be deployed in any production environment. - It can achieve 1ms latency for BERT-like models on GPU and 4-10ms on CPU 🤯🤯🤯 - Infinity meets the highest security requirements and can be integrated into your system without the need for internet access. You have control over all incoming and outgoing traffic. ⚠️ Join us for a live announcement and demo on Sep 28, where we will be showcasing Infinity for the first time in public! | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | NEW: Hardware Acceleration Hugging Face is partnering with leading AI hardware accelerators such as Intel, Qualcomm and GraphCore to make state-of-the-art production performance accessible and extend training capabilities on SOTA hardware. As the first step in this journey, we introduced a new Open Source library: 🤗 Optimum - the ML optimization toolkit for production performance 🏎. Learn more in this blog post. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | New in Datasets You can find 1400 public datasets in https://huggingface.co/datasets thanks to the awesome contributions from all our community. 💯 The support for datasets keeps growing: it can be used in JAX, process parquet files, use remote files, and has wider support for other domains such as Automatic Speech Recognition and Image Classification. Users can also directly host and share their datasets to the community simply by uploading their data files in a repository on the Dataset Hub. What are the new datasets highlights? Microsoft CodeXGlue datasets for multiple coding tasks (code completion, generation, search, etc), huge datasets such as C4 and MC4, and many more such as RussianSuperGLUE and DISFL-QA. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | New in Transformers Summer has been an exciting time for 🤗 Transformers! The library reached 50,000 stars, 30 million total downloads, and almost 1000 contributors! 🤩 So what's new? JAX/Flax is now the 3rd supported framework with over 5000 models in the Hub! You can find actively maintained examples for different tasks such as text classification. We're also working hard on improving our TensorFlow support: all our examples have been reworked to be more robust, TensorFlow idiomatic, and clearer. This includes examples such as summarization, translation, and named entity recognition. You can now easily publish your model to the Hub, including automatically authored model cards, evaluation metrics, and TensorBoard instances. There is also increased support for exporting models to ONNX with the new transformers.onnx module. bash python -m transformers.onnx --model=bert-base-cased onnx/bert-base-cased/ The last 4 releases introduced many new cool models! - DETR can do fast end-to-end object detection and image segmentation. Check out some of our community tutorials! - ByT5 is the first tokenizer-free model in the Hub! You can find all available checkpoints here. - CANINE is another tokenizer-free encoder-only model by Google AI, operating directly at the character level. You can find all (multilingual) checkpoints here. - HuBERT shows exciting results for downstream audio tasks such as command classification and emotion recognition. Check the models here. - LayoutLMv2 and LayoutXLM are two incredible models capable of parsing document images (like PDFs) by incorporating text, layout, and visual information. We built a Space demo so you can directly try it out! Demo notebooks can be found here. - BEiT by Microsoft Research makes self-supervised Vision Transformers outperform supervised ones, using a clever pre-training objective inspired by BERT. - RemBERT, a large multilingual Transformer that outperforms XLM-R (and mT5 with a similar number of parameters) in zero-shot transfer. - Splinter which can be used for few-shot question answering. Given only 128 examples, Splinter is able to reach ~73% F1 on SQuAD, outperforming MLM-based models by 24 points! The Hub is now integrated into transformers, with the ability to push to the Hub configuration, model, and tokenizer files without leaving the Python runtime! The Trainer can now push directly to the Hub every time a checkpoint is saved: | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Last words 😎 🔥 Summer was fun! So many things have happened! We hope you enjoyed reading this blog post and looking forward to share the new projects we're working on. See you in the winter! ❄️ | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | JAX/FLAX Sprint In July we hosted our biggest community event ever with almost 800 participants! In this event co-organized with the JAX/Flax and Google Cloud teams, compute-intensive NLP, Computer Vision, and Speech projects were made accessible to a wider audience of engineers and researchers by providing free TPUv3s. The participants created over 170 models, 22 datasets, and 38 Spaces demos 🤯. You can explore all the amazing demos and projects here. There were talks around JAX/Flax, Transformers, large-scale language modeling, and more! You can find all recordings here. We're really excited to share the work of the 3 winning teams! 1. Dall-e mini. DALL·E mini is a model that generates images from any prompt you give! DALL·E mini is 27 times smaller than the original DALL·E and still has impressive results. 2. DietNerf. DietNerf is a 3D neural view synthesis model designed for few-shot learning of 3D scene reconstruction using 2D views. This is the first Open Source implementation of the "Putting Nerf on a Diet" paper. 3. CLIP RSIC. CLIP RSIC is a CLIP model fine-tuned on remote sensing image data to enable zero-shot satellite image classification and captioning. This project demonstrates how effective fine-tuned CLIP models can be for specialized domains. Apart from these very cool projects, we're excited about how these community events enable training large and multi-modal models for multiple languages. For example, we saw the first ever Open Source big LMs for some low-resource languages like Swahili, Polish and Marathi. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Open Source | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | TensorBoard Integration In late June, we launched a TensorBoard integration for all our models. If there are TensorBoard traces in the repo, an automatic, free TensorBoard instance is launched for you. This works with both public and private repositories and for any library that has TensorBoard traces! | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Community | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | NEW: Inference on SageMaker We launched a new integration with AWS to make it easier than ever to deploy 🤗 Transformers in SageMaker 🔥. Pick up the code snippet right from the 🤗 Hub model page! Learn more about how to leverage transformers in SageMaker in our docs or check out these video tutorials. For questions reach out to us on the forum: https://discuss.huggingface.co/c/sagemaker/17 | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Spaces Beta (hf.co/spaces) Spaces is a simple and free solution to host Machine Learning demo applications directly on your user profile or your organization hf.co profile. We support two awesome SDKs that let you build cool apps easily in Python: Gradio and Streamlit. In a matter of minutes you can deploy an app and share it with the community! 🚀 Spaces lets you set up secrets, permits custom requirements, and can even be managed directly from GitHub repos. You can sign up for the beta at hf.co/spaces. Here are some of our favorites! - Create recipes with the help of Chef Transformer - Transcribe speech to text with HuBERT - Do segmentation in a video with the DINO model - Use Paint Transformer to make paintings from a given picture - Or you can just explore any of the over 100 existing Spaces! | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Expert Acceleration Program Check out out the brand new home for the Expert Acceleration Program; you can now get direct, premium support from our Machine Learning experts and build better ML solutions, faster. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Metrics In July, we added the ability to list evaluation metrics in model repos by adding them to their model card📈. If you add an evaluation metric under the model-index section of your model card, it will be displayed proudly in your model repo. If that wasn't enough, these metrics will be automatically linked to the corresponding Papers With Code leaderboard. That means as soon as you share your model on the Hub, you can compare your results side-by-side with others in the community. 💪 Check out this repo as an example, paying close attention to model-index section of its model card to see how you can do this yourself and find the metrics in Papers with Code automatically. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Welcoming new Libraries to the Hub Apart from having deep integration with transformers-based models, the Hub is also building great partnerships with Open Source ML libraries to provide free model hosting and versioning. We've been achieving this with our huggingface_hub Open-Source library as well as new Hub documentation. All spaCy canonical pipelines can now be found in the official spaCy organization, and any user can share their pipelines with a single command python -m spacy huggingface-hub. To read more about it, head to https://huggingface.co/blog/spacy. You can try all canonical spaCy models directly in the Hub in the demo Space! Another exciting integration is Sentence Transformers. You can read more about it in the blog announcement: you can find over 200 models in the Hub, easily share your models with the rest of the community and reuse models from the community. But that's not all! You can now find over 100 Adapter Transformers in the Hub and try out Speechbrain models with widgets directly in the browser for different tasks such as audio classification. If you're interested in our collaborations to integrate new ML libraries to the Hub, you can read more about them here. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Hugging Face Course In June, we launched the first part of our free online course! The course teaches you everything about the 🤗 Ecosystem: Transformers, Tokenizers, Datasets, Accelerate, and the Hub. You can also find links to the course lessons in the official documentation of our libraries. The live sessions for all chapters can be found on our YouTube channel. Stay tuned for the next part of the course which we'll be launching later this year! | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | New Features In the last few months, the Hub went from 10,000 public model repositories to over 16,000 models! Kudos to our community for sharing so many amazing models with the world. And beyond the numbers, we have a ton of cool new features to share with you! | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | More Features That's not everything that has happened in the Hub. We've introduced new and improved documentation of the Hub. We also introduced two widely requested features: users can now transfer/rename repositories and directly upload new files to the Hub. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Inference API Webinar: We hosted a live webinar to show how to add Machine Learning capabilities with just a few lines of code. We also built a VSCode extension that leverages the Hugging Face Inference API to generate comments describing Python code. Hugging Face + Zapier Demo 20,000+ Machine Learning models connected to 3,000+ apps? 🤯 By leveraging the Inference API, you can now easily connect models right into apps like Gmail, Slack, Twitter, and more. In this demo video, we created a zap that uses this code snippet to analyze your Twitter mentions and alerts you on Slack about the negative ones. Hugging Face + Google Sheets Demo With the Inference API, you can easily use zero-shot classification right into your spreadsheets in Google Sheets. Just add this script in Tools -> Script Editor: Few-shot learning in practice We wrote a blog post about what Few-Shot Learning is and explores how GPT-Neo and 🤗 Accelerated Inference API are used to generate your own predictions. | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | # Summer At Hugging Face 😎 Summer is now officially over and these last few months have been quite busy at Hugging Face. From new features in the Hub to research and Open Source development, our team has been working hard to empower the community through open and collaborative technology. In this blog post you'll catch up on everything that happened at Hugging Face in June, July and August! This post covers a wide range of areas our team has been working on, so don't hesitate to skip to the parts that interest you the most 🤗 1. New Features 2. Community 3. Open Source 4. Solutions 5. Research | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Solutions | [
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Summer at Hugging Face ☀️ | huggingface | September 24, 2021 | summer-at-huggingface | community | https://huggingface.co/blog/summer-at-huggingface | Bonus On top of everything we just shared, our team has been doing lots of other things. Here are just some of them: - 📖 This 3-part video series shows the theory on how to train state-of-the-art sentence embedding models. - We presented at PyTorch Community Voices and participated in a QA (video). - Hugging Face has collaborated with NLP in Spanish and SpainAI in a Spanish course that teaches concepts and state-of-the art architectures as well as their applications through use cases. - We presented at MLOps World Demo Days. | [
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Showcase Your Projects in Spaces using Gradio | merve | October 5, 2021 | gradio-spaces | guide | https://huggingface.co/blog/gradio-spaces | # Showcase Your Projects in Spaces using Gradio It's so easy to demonstrate a Machine Learning project thanks to Gradio. In this blog post, we'll walk you through: - the recent Gradio integration that helps you demo models from the Hub seamlessly with few lines of code leveraging the Inference API. - how to use Hugging Face Spaces to host demos of your own models. | [
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Showcase Your Projects in Spaces using Gradio | merve | October 5, 2021 | gradio-spaces | guide | https://huggingface.co/blog/gradio-spaces | Serving Custom Model Checkpoints with Gradio in Hugging Face Spaces You can serve your models in Spaces even if the Inference API does not support your model. Just wrap your model inference in a Gradio Interface as described below and put it in Spaces. | [
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Showcase Your Projects in Spaces using Gradio | merve | October 5, 2021 | gradio-spaces | guide | https://huggingface.co/blog/gradio-spaces | Uploading your Models to the Spaces You can serve your demos in Hugging Face thanks to Spaces! To do this, simply create a new Space, and then drag and drop your demos or use Git. Easily build your first demo with Spaces here! | [
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Showcase Your Projects in Spaces using Gradio | merve | October 5, 2021 | gradio-spaces | guide | https://huggingface.co/blog/gradio-spaces | Hugging Face Hub Integration in Gradio You can demonstrate your models in the Hub easily. You only need to define the Interface that includes: - The repository ID of the model you want to infer with - A description and title - Example inputs to guide your audience After defining your Interface, just call .launch() and your demo will start running. You can do this in Colab, but if you want to share it with the community a great option is to use Spaces! Spaces are a simple, free way to host your ML demo apps in Python. To do so, you can create a repository at https://huggingface.co/new-space and select Gradio as the SDK. Once done, you can create a file called app.py, copy the code below, and your app will be up and running in a few seconds! python import gradio as gr description = "Story generation with GPT-2" title = "Generate your own story" examples = [["Adventurer is approached by a mysterious stranger in the tavern for a new quest."]] interface = gr.Interface.load("huggingface/pranavpsv/gpt2-genre-story-generator", description=description, examples=examples ) interface.launch() You can play with the Story Generation model here Under the hood, Gradio calls the Inference API which supports Transformers as well as other popular ML frameworks such as spaCy, SpeechBrain and Asteroid. This integration supports different types of models, image-to-text, speech-to-text, text-to-speech and more. You can check out this example BigGAN ImageNet text-to-image model here. Implementation is below. python import gradio as gr description = "BigGAN text-to-image demo." title = "BigGAN ImageNet" interface = gr.Interface.load("huggingface/osanseviero/BigGAN-deep-128", description=description, title = title, examples=[["american robin"]] ) interface.launch() | [
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Showcase Your Projects in Spaces using Gradio | merve | October 5, 2021 | gradio-spaces | guide | https://huggingface.co/blog/gradio-spaces | Mix and Match Models! Using Gradio Series, you can mix-and-match different models! Here, we've put a French to English translation model on top of the story generator and a English to French translation model at the end of the generator model to simply make a French story generator. python import gradio as gr from gradio.mix import Series description = "Generate your own D&D story!" title = "French Story Generator using Opus MT and GPT-2" translator_fr = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-fr-en") story_gen = gr.Interface.load("huggingface/pranavpsv/gpt2-genre-story-generator") translator_en = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-en-fr") examples = [["L'aventurier est approché par un mystérieux étranger, pour une nouvelle quête."]] Series(translator_fr, story_gen, translator_en, description = description, title = title, examples=examples, inputs = gr.inputs.Textbox(lines = 10)).launch() You can check out the French Story Generator here | [
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Hosting your Models and Datasets on Hugging Face Spaces using Streamlit | merve | October 5, 2021 | streamlit-spaces | guide | https://huggingface.co/blog/streamlit-spaces | Showcase your Datasets and Data Visualizations Streamlit provides many components to help you visualize datasets. It works seamlessly with 🤗 Datasets, pandas, and visualization libraries such as matplotlib, seaborn and bokeh. Let's start by loading a dataset. A new feature in Datasets, called streaming, allows you to work immediately with very large datasets, eliminating the need to download all of the examples and load them into memory. python from datasets import load_dataset import streamlit as st dataset = load_dataset("merve/poetry", streaming=True) df = pd.DataFrame.from_dict(dataset["train"]) If you have structured data like mine, you can simply use st.dataframe(df) to show your dataset. There are many Streamlit components to plot data interactively. One such component is st.barchart(), which I used to visualize the most used words in the poem contents. python st.write("Most appearing words including stopwords") st.bar_chart(words[0:50]) If you'd like to use libraries like matplotlib, seaborn or bokeh, all you have to do is to put st.pyplot() at the end of your plotting script. python st.write("Number of poems for each author") sns.catplot(x="author", data=df, kind="count", aspect = 4) plt.xticks(rotation=90) st.pyplot() You can see the interactive bar chart, dataframe component and hosted matplotlib and seaborn visualizations below. You can check out the code here. | [
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Hosting your Models and Datasets on Hugging Face Spaces using Streamlit | merve | October 5, 2021 | streamlit-spaces | guide | https://huggingface.co/blog/streamlit-spaces | Showcase your Datasets and Models using Streamlit on Hugging Face Spaces Streamlit allows you to visualize datasets and build demos of Machine Learning models in a neat way. In this blog post we will walk you through hosting models and datasets and serving your Streamlit applications in Hugging Face Spaces. | [
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Hosting your Models and Datasets on Hugging Face Spaces using Streamlit | merve | October 5, 2021 | streamlit-spaces | guide | https://huggingface.co/blog/streamlit-spaces | Hosting your Projects in Hugging Face Spaces You can simply drag and drop your files as shown below. Note that you need to include your additional dependencies in the requirements.txt. Also note that the version of Streamlit you have on your local is the same. For seamless usage, refer to Spaces API reference. There are so many components and packages you can use to demonstrate your models, datasets, and visualizations. You can get started here. | [
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Hosting your Models and Datasets on Hugging Face Spaces using Streamlit | merve | October 5, 2021 | streamlit-spaces | guide | https://huggingface.co/blog/streamlit-spaces | # Hosting your Models and Datasets on Hugging Face Spaces using Streamlit | [
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Hosting your Models and Datasets on Hugging Face Spaces using Streamlit | merve | October 5, 2021 | streamlit-spaces | guide | https://huggingface.co/blog/streamlit-spaces | Building demos for your models You can load any Hugging Face model and build cool UIs using Streamlit. In this particular example we will recreate "Write with Transformer" together. It's an application that lets you write anything using transformers like GPT-2 and XLNet. We will not dive deep into how the inference works. You only need to know that you need to specify some hyperparameter values for this particular application. Streamlit provides many components for you to easily implement custom applications. We will use some of them to receive necessary hyperparameters inside the inference code. - The .text_area component creates a nice area to input sentences to be completed. - The Streamlit .sidebar method enables you to accept variables in a sidebar. - The slider is used to take continuous values. Don't forget to give slider a step, otherwise it will treat the values as integers. - You can let the end-user input integer vaues with number_input . python import streamlit as st # adding the text that will show in the text box as default default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by the Hugging Face team, lets you write a whole document directly from your browser, and you can trigger the Transformer anywhere using the Tab key. Its like having a smart machine that completes your thoughts 😀 Get started by typing a custom snippet, check out the repository, or try one of the examples. Have fun!" sent = st.text_area("Text", default_value, height = 275) max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30) temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05) top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0) top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9) num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1) The inference code returns the generated output, you can print the output using simple st.write. st.write(generated_sequences[-1]) Here's what our replicated version looks like. You can checkout the full code here. | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Data Augmentation In order to regularize our dataset and prevent overfitting due to the size of the dataset, we used both image and text augmentation. Image augmentation was done inline using built-in transforms from Pytorch's Torchvision package. The transformations used were Random Cropping, Random Resizing and Cropping, Color Jitter, and Random Horizontal and Vertical flipping. We augmented the text with backtranslation to generate captions for images with less than 5 unique captions per image. The Marian MT family of models from Hugging Face was used to translate the existing captions into French, Spanish, Italian, and Portuguese and back to English to fill out the captions for these images. As shown in these loss plots below, image augmentation reduced overfitting significantly, and text and image augmentation reduced overfitting even further. Evaluation and Training loss plots comparing (top) no augmentation vs image augmentation, and (bottom) image augmentation vs text+image augmentation | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | # Fine tuning CLIP with Remote Sensing (Satellite) images and captions | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Future Work We are grateful that we have been given an opportunity to further refine our model. Some ideas we have for future work are as follows: 1. Construct a sequence to sequence model using a CLIP encoder and a GPT-3 decoder and train it for image captioning. 2. Fine-tune the model on more image caption pairs from other datasets and investigate if we can improve its performance. 3. Investigate how fine-tuning affects the performance of model on non-RSICD image caption pairs. 4. Investigate the capability of the fine-tuned model to classify outside the categories it has been fine-tuned on. 5. Evaluate the model using other criteria such as image classification. | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Metrics A subset of the RSICD test set was used for evaluation. We found 30 categories of images in this subset. The evaluation was done by comparing each image with a set of 30 caption sentences of the form "An aerial photograph of {category}". The model produced a ranked list of the 30 captions, from most relevant to least relevant. Categories corresponding to captions with the top k scores (for k=1, 3, 5, and 10) were compared with the category provided via the image file name. The scores are averaged over the entire set of images used for evaluation and reported for various values of k, as shown below. The baseline model represents the pre-trained openai/clip-vit-base-path32 CLIP model. This model was fine-tuned with captions and images from the RSICD dataset, which resulted in a significant performance boost, as shown below. Our best model was trained with image and text augmentation, with batch size 1024 (128 on each of the 8 TPU cores), and the Adam optimizer with learning rate 5e-6. We trained our second base model with the same hyperparameters, except that we used the Adafactor optimizer with learning rate 1e-4. You can download either model from their model repos linked to in the table below. | Model-name | k=1 | k=3 | k=5 | k=10 | | ---------------------------------------- | ----- | ----- | ----- | ----- | | baseline | 0.572 | 0.745 | 0.837 | 0.939 | | bs128x8-lr1e-4-augs/ckpt-2 | 0.819 | 0.950 | 0.974 | 0.994 | | bs128x8-lr1e-4-imgaugs/ckpt-2 | 0.812 | 0.942 | 0.970 | 0.991 | | bs128x8-lr1e-4-imgaugs-textaugs/ckpt-42 | 0.843 | 0.958 | 0.977 | 0.993 | | bs128x8-lr5e-5-imgaugs-textaugs/ckpt-8 | 0.831 | 0.959 | 0.977 | 0.994 | | bs128x8-lr5e-5-imgaugs/ckpt-4 | 0.746 | 0.906 | 0.956 | 0.989 | | bs128x8-lr5e-5-imgaugs-textaugs-2/ckpt-4 | 0.811 | 0.945 | 0.972 | 0.993 | | bs128x8-lr5e-5-imgaugs-textaugs-3/ckpt-5 | 0.823 | 0.946 | 0.971 | 0.992 | | bs128x8-lr5e-5-wd02/ckpt-4 | 0.820 | 0.946 | 0.965 | 0.990 | | bs128x8-lr5e-6-adam/ckpt-11 | 0.883 | 0.968 | 0.982 | 0.998 | 1 - our best model, 2 - our second best model | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Dataset We fine-tuned the CLIP model primarily with the RSICD dataset. This dataset consists of about 10,000 images collected from Google Earth, Baidu Map, MapABC, and Tianditu. It is provided freely to the research community to advance remote sensing captioning via Exploring Models and Data for Remote Sensing Image Caption Generation (Lu et al, 2017). The images are (224, 224) RGB images at various resolutions, and each image has up to 5 captions associated with it. Some examples of images from the RSICD dataset In addition, we used the UCM Dataset and the Sydney dataset for training, The UCM dataset is based on the UC Merced Land Use dataset. It consists of 2100 images belonging to 21 classes (100 images per class), and each image has 5 captions. The Sydney dataset contains images of Sydney, Australia from Google Earth. It contains 613 images belonging to 7 classes. Images are (500, 500) RGB and provides 5 captions for each image. We used these additional datasets because we were not sure if the RSICD dataset would be large enough to fine-tune CLIP. | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Demo You can access the CLIP-RSICD Demo here. It uses our fine-tuned CLIP model to provide the following functionality: * Text to Image search * Image to Image search * Find text feature in image The first two functionalities use the RSICD test set as its image corpus. They are encoded using our best fine-tuned CLIP model and stored in a NMSLib index which allows Approximate Nearest Neighbor based retrieval. For text-to-image and image-to-image search respectively, the query text or image are encoded with our model and matched against the image vectors in the corpus. For the third functionality, we divide the incoming image into patches and encode them, encode the queried text feature, match the text vector with each image patch vector, and return the probability of finding the feature in each patch. | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Training | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Model Our model is just the fine-tuned version of the original CLIP model shown below. Inputs to the model are a batch of captions and a batch of images passed through the CLIP text encoder and image encoder respectively. The training process uses contrastive learning to learn a joint embedding representation of image and captions. In this embedding space, images and their respective captions are pushed close together, as are similar images and similar captions. Conversely, images and captions for different images, or dissimilar images and captions, are likely to be pushed further apart. CLIP Training and Inference (Image Credit: CLIP: Connecting Text and Images (https://openai.comclip/)) | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Fine tuning CLIP with Remote Sensing (Satellite) images and captions In July this year, Hugging Face organized a Flax/JAX Community Week, and invited the community to submit projects to train Hugging Face transformers models in the areas of Natural Language Processing (NLP) and Computer Vision (CV). Participants used Tensor Processing Units (TPUs) with Flax and JAX. JAX is a linear algebra library (like numpy) that can do automatic differentiation (Autograd) and compile down to XLA, and Flax is a neural network library and ecosystem for JAX. TPU compute time was provided free by Google Cloud, who co-sponsored the event. Over the next two weeks, teams participated in lectures from Hugging Face and Google, trained one or more models using JAX/Flax, shared them with the community, and provided a Hugging Face Spaces demo showcasing the capabilities of their model. Approximately 100 teams participated in the event, and it resulted in 170 models and 36 demos. Our team, like probably many others, is a distributed one, spanning 12 time zones. Our common thread is that we all belong to the TWIML Slack Channel, where we came together based on a shared interest in Artificial Intelligence (AI) and Machine Learning (ML) topics. We fine-tuned the CLIP Network from OpenAI with satellite images and captions from the RSICD dataset. The CLIP network learns visual concepts by being trained with image and caption pairs in a self-supervised manner, by using text paired with images found across the Internet. During inference, the model can predict the most relevant image given a text description or the most relevant text description given an image. CLIP is powerful enough to be used in zero-shot manner on everyday images. However, we felt that satellite images were sufficiently different from everyday images that it would be useful to fine-tune CLIP with them. Our intuition turned out to be correct, as the evaluation results (described below) shows. In this post, we describe details of our training and evaluation process, and our plans for future work on this project. The goal of our project was to provide a useful service and demonstrate how to use CLIP for practical use cases. Our model can be used by applications to search through large collections of satellite images using textual queries. Such queries could describe the image in totality (for example, beach, mountain, airport, baseball field, etc) or search or mention specific geographic or man-made features within these images. CLIP can similarly be fine-tuned for other domains as well, as shown by the medclip-demo team for medical images. The ability to search through large collections of images using text queries is an immensely powerful feature, and can be used as much for social good as for malign purposes. Possible applications include national defense and anti-terrorism activities, the ability to spot and address effects of climate change before they become unmanageable, etc. Unfortunately, this power can also be misused, such as for military and police surveillance by authoritarian nation-states, so it does raise some ethical questions as well. You can read about the project on our project page, download our trained model to use for inference on your own data, or see it in action on our demo. | [
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Fine tuning CLIP with Remote Sensing (Satellite) images and captions | arampacha | October 13, 2021 | fine-tune-clip-rsicd | community, cv, nlp | https://huggingface.co/blog/fine-tune-clip-rsicd | Evaluation | [
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The Age of Machine Learning As Code Has Arrived | juliensimon | October 20, 2021 | the-age-of-ml-as-code | analysis | https://huggingface.co/blog/the-age-of-ml-as-code | # The Age of Machine Learning As Code Has Arrived The 2021 edition of the State of AI Report came out last week. So did the Kaggle State of Machine Learning and Data Science Survey. There's much to be learned and discussed in these reports, and a couple of takeaways caught my attention. > "AI is increasingly being applied to mission critical infrastructure like national electric grids and automated supermarket warehousing calculations during pandemics. However, there are questions about whether the maturity of the industry has caught up with the enormity of its growing deployment." There's no denying that Machine Learning-powered applications are reaching into every corner of IT. But what does that mean for companies and organizations? How do we build rock-solid Machine Learning workflows? Should we all hire 100 Data Scientists ? Or 100 DevOps engineers? > "Transformers have emerged as a general purpose architecture for ML. Not just for Natural Language Processing, but also Speech, Computer Vision or even protein structure prediction." Old timers have learned the hard way that there is no silver bullet in IT. Yet, the Transformer architecture is indeed very efficient on a wide variety of Machine Learning tasks. But how can we all keep up with the frantic pace of innovation in Machine Learning? Do we really need expert skills to leverage these state of the art models? Or is there a shorter path to creating business value in less time? Well, here's what I think. | [
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The Age of Machine Learning As Code Has Arrived | juliensimon | October 20, 2021 | the-age-of-ml-as-code | analysis | https://huggingface.co/blog/the-age-of-ml-as-code | Machine Learning For The Masses! Machine Learning is everywhere, or at least it's trying to be. A few years ago, Forbes wrote that "Software ate the world, now AI is eating Software", but what does this really mean? If it means that Machine Learning models should replace thousands of lines of fossilized legacy code, then I'm all for it. Die, evil business rules, die! Now, does it mean that Machine Learning will actually replace Software Engineering? There's certainly a lot of fantasizing right now about AI-generated code, and some techniques are certainly interesting, such as finding bugs and performance issues. However, not only shouldn't we even consider getting rid of developers, we should work on empowering as many as we can so that Machine Learning becomes just another boring IT workload (and boring technology is great). In other words, what we really need is for Software to eat Machine Learning! | [
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The Age of Machine Learning As Code Has Arrived | juliensimon | October 20, 2021 | the-age-of-ml-as-code | analysis | https://huggingface.co/blog/the-age-of-ml-as-code | Machine Learning as Code, right here, right now! A lot of you seem to agree. We have over 52,000 stars on Github. For the first year, Hugging Face is also featured in the Kaggle survey, with usage already over 10%. Thank you all. And yeah, we're just getting started. --- Interested in how Hugging Face can help your organization build and deploy production-grade Machine Learning solutions? Get in touch at [email protected] (no recruiters, no sales pitches, please). | [
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The Age of Machine Learning As Code Has Arrived | juliensimon | October 20, 2021 | the-age-of-ml-as-code | analysis | https://huggingface.co/blog/the-age-of-ml-as-code | Infrastructure? So what? It's 2021. IT infrastructure should no longer stand in the way. Software has devoured it a while ago, abstracting it away with cloud APIs, infrastructure as code, Kubeflow and so on. Yes, even on premises. The same is quickly happening for Machine Learning infrastructure. According to the Kaggle survey, 75% of respondents use cloud services, and over 45% use an Enterprise ML platform, with Amazon SageMaker, Databricks and Azure ML Studio taking the top 3 spots. With MLOps, software-defined infrastructure and platforms, it's never been easier to drag all these great ideas out of the sandbox, and to move them to production. To answer my original question, I'm pretty sure you need to hire more ML-savvy Software and DevOps engineers, not more Data Scientists. But deep down inside, you kind of knew that, right? Now, let's talk about Transformers. --- | [
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The Age of Machine Learning As Code Has Arrived | juliensimon | October 20, 2021 | the-age-of-ml-as-code | analysis | https://huggingface.co/blog/the-age-of-ml-as-code | Things are not different this time For years, I've argued and swashbuckled that decade-old best practices for Software Engineering also apply to Data Science and Machine Learning: versioning, reusability, testability, automation, deployment, monitoring, performance, optimization, etc. I felt alone for a while, and then the Google cavalry unexpectedly showed up: > "Do machine learning like the great engineer you are, not like the great machine learning expert you aren't." - Rules of Machine Learning, Google There's no need to reinvent the wheel either. The DevOps movement solved these problems over 10 years ago. Now, the Data Science and Machine Learning community should adopt and adapt these proven tools and processes without delay. This is the only way we'll ever manage to build robust, scalable and repeatable Machine Learning systems in production. If calling it MLOps helps, fine: I won't argue about another buzzword. It's really high time we stopped considering proof of concepts and sandbox A/B tests as notable achievements. They're merely a small stepping stone toward production, which is the only place where assumptions and business impact can be validated. Every Data Scientist and Machine Learning Engineer should obsess about getting their models in production, as quickly and as often as possible. An okay production model beats a great sandbox model every time. | [
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The Age of Machine Learning As Code Has Arrived | juliensimon | October 20, 2021 | the-age-of-ml-as-code | analysis | https://huggingface.co/blog/the-age-of-ml-as-code | Transformers! Transformers! Transformers! (Ballmer style) Says the State of AI report: "The Transformer architecture has expanded far beyond NLP and is emerging as a general purpose architecture for ML". For example, recent models like Google's Vision Transformer, a convolution-free transformer architecture, and CoAtNet, which mixes transformers and convolution, have set new benchmarks for image classification on ImageNet, while requiring fewer compute resources for training. Transformers also do very well on audio (say, speech recognition), as well as on point clouds, a technique used to model 3D environments like autonomous driving scenes. The Kaggle survey echoes this rise of Transformers. Their usage keeps growing year over year, while RNNs, CNNs and Gradient Boosting algorithms are receding. On top of increased accuracy, Transformers also keep fulfilling the transfer learning promise, allowing teams to save on training time and compute costs, and to deliver business value quicker. With Transformers, the Machine Learning world is gradually moving from "Yeehaa!! Let's build and train our own Deep Learning model from scratch" to "Let's pick a proven off the shelf model, fine-tune it on our own data, and be home early for dinner." It's a Good Thing in so many ways. State of the art is constantly advancing, and hardly anyone can keep up with its relentless pace. Remember that Google Vision Transformer model I mentioned earlier? Would you like to test it here and now? With Hugging Face, it's the simplest thing. How about the latest zero-shot text generation models from the Big Science project? You can do the same with another 16,000+ models and 1,600+ datasets, with additional tools for inference, AutoNLP, latency optimization, and hardware acceleration. We can also help you get your project off the ground, from modeling to production. Our mission at Hugging Face is to make Machine Learning as friendly and as productive as possible, for beginners and experts alike. We believe in writing as little code as possible to train, optimize, and deploy models. We believe in built-in best practices. We believe in making infrastructure as transparent as possible. We believe that nothing beats high quality models in production, fast. | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | 2. Hard Negatives In the same figure, we observe that including hard negatives also improves performance. Hard negatives are sample \( p_j \) which are hard to distinguish from \( p_i \). In our example, it could be the pairs « What is the capital of France? » and « What is the capital of the US? » which have a close semantic content and requires precisely understanding the full sentence to be answered correctly. On the contrary, the samples « What is the capital of France? » and «How many Star Wars movies is there?» are less difficult to distinguish since they do not refer to the same topic. | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | 3. Cross dataset batches We concatenated multiple datasets to train our models. We built a large batch and gathered samples from the same batch dataset to limit the topic distribution and favor hard negatives. However, we also mix at least two datasets in the batch to learn a global structure between topics and not only a local structure within a topic. | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | Improving Quality with Better Batches In our method, we build batches of sample pairs \( (a_i , p_i) \). We consider all other samples from the batch, \( (a_i , p_j), i \neq j \), as negatives sample pairs. The batch composition is therefore a key training aspect. Given the literature in the domain, we mainly focused on three main aspects of the batch. | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | Multiple Negative Ranking Loss The parameters from the composition module are usually learned using a self-supervised objective. For the project, we used a contrastive training method illustrated in the figure below. We constitute a dataset with sentence pairs \( (a_i, p_i) \) such that sentences from the pair have a close meaning. For example, we consider pairs such as (query, answer-passage), (question, duplicate_question),(paper title, cited paper title). Our model is then trained to map pairs \( (a_i , p_i) \) to close vectors while assigning unmatched pairs \( (a_i , p_j), i \neq j \) to distant vectors in the embedding space. This training method is also called training with in-batch negatives, InfoNCE or NTXentLoss. Formally, given a batch of training samples, the model optimises the following loss function: $$-\frac{1}{n}\sum_{i=1}^n\frac{exp(sim(a_i, p_i))}{\sum_j exp(sim(a_i, p_j))}$$ An illustrative example can be seen below. The model first embeds each sentence from every pair in the batch. Then, we compute a similarity matrix between every possible pair \( (a_i, p_j) \). We then compare the similarity matrix with the ground truth, which indicates the original pairs. Finally, we perform the comparison using the cross entropy loss. Intuitively, the model should assign high similarity to the sentences « How many people live in Berlin? » and « Around 3.5 million people live in Berlin » and low similarity to other negative answers such as « The capital of France is Paris » as detailed in the Figure below. In the loss equation, sim indicates a similarity function between \( (a, p) \). The similarity function could be either the Cosine-Similarity or the Dot-Product operator. Both methods have their pros and cons summarized below (Thakur et al., 2021, Bachrach et al., 2014): | Cosine-similarity | Dot-product | |---------------------|-------------| | Vector has highest similarity to itself since \( cos(a, a)=1 \). | Other vectors can have higher dot-products \( dot(a, a) < dot (a, b) \). | | With normalised vectors it is equal to the dot product. The max vector length is equals 1. | It might be slower with certain approximate nearest neighbour methods since the max vector not known. | | With normalised vectors, it is proportional to euclidian distance. It works with k-means clustering. | It does not work with k-means clustering. | In practice, we used a scaled similarity because score differences tends to be too small and apply a scaling factor \( C \) such that \( sim_{scaled}(a, b) = C * sim(a, b) \) with typically \( C = 20 \) (Henderson and al., 2020, Radford and al., 2021). | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | # Train a Sentence Embedding Model with 1 Billion Training Pairs Sentence embedding is a method that maps sentences to vectors of real numbers. Ideally, these vectors would capture the semantic of a sentence and be highly generic. Such representations could then be used for many downstream applications such as clustering, text mining, or question answering. We developed state-of-the-art sentence embedding models as part of the project "Train the Best Sentence Embedding Model Ever with 1B Training Pairs". This project took place during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as guidance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks! | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | Training methodology | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | Model Unlike words, we can not define a finite set of sentences. Sentence embedding methods, therefore, compose inner words to compute the final representation. For example, SentenceBert model (Reimers and Gurevych, 2019) uses Transformer, the cornerstone of many NLP applications, followed by a pooling operation over the contextualized word vectors. (c.f. Figure below.) | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | Conclusion You can find all models and datasets we created during the challenge in our HuggingFace repository. We trained 20 general-purpose Sentence Transformers models such as Mini-LM (Wang and al., 2020), RoBERTa (liu and al., 2019), DistilBERT (Sanh and al., 2020) and MPNet (Song and al., 2020). Our models achieve SOTA on multiple general-purpose Sentence Similarity evaluation tasks. We also shared 8 datasets specialized for Question Answering, Sentence-Similarity, and Gender Evaluation. General sentence embeddings might be used for many applications. We built a Spaces demo to showcase several applications: * The sentence similarity module compares the similarity of the main text with other texts of your choice. In the background, the demo extracts the embedding for each text and computes the similarity between the source sentence and the other using cosine similarity. * Asymmetric QA compares the answer likeliness of a given query with answer candidates of your choice. * Search / Cluster returns nearby answers from a query. For example, if you input « python », it will retrieve closest sentences using dot-product distance. * Gender Bias Evaluation report inherent gender bias in training set via random sampling of the sentences. Given an anchor text without mentioning gender for target occupation and 2 propositions with gendered pronouns, we compare if models assign a higher similarity to a given proposition and therefore evaluate their proportion to favor a specific gender. The Community week using JAX/Flax for NLP & CV has been an intense and highly rewarding experience! The quality of Google’s Flax, JAX, and Cloud and Hugging Face team members' guidance and their presence helped us all learn a lot. We hope all projects had as much fun as we did in ours. Whenever you have questions or suggestions, don’t hesitate to contact us! | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | 1. Size matters In contrastive learning, a larger batch size is synonymous with better performance. As shown in the Figure extracted from Qu and al., (2021), a larger batch size increases the results. | [
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Train a Sentence Embedding Model with 1B Training Pairs | asi | October 25, 2021 | 1b-sentence-embeddings | community, nlp | https://huggingface.co/blog/1b-sentence-embeddings | Training infrastructure and data As mentioned earlier, the quantity of data and the batch size directly impact the model performances. As part of the project, we benefited from efficient hardware infrastructure. We trained our models on TPUs which are compute units developed by Google and super efficient for matrix multiplications. TPUs have some hardware specificities which might require some specific code implementation. Additionally, we trained models on a large corpus as we concatenated multiple datasets up to 1 billion sentence pairs! All datasets used are detailed for each model in the model card. | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | # Large Language Models: A New Moore's Law? A few days ago, Microsoft and NVIDIA introduced Megatron-Turing NLG 530B, a Transformer-based model hailed as "the world’s largest and most powerful generative language model." This is an impressive show of Machine Learning engineering, no doubt about it. Yet, should we be excited about this mega-model trend? I, for one, am not. Here's why. | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | Conclusion Large language model size has been increasing 10x every year for the last few years. This is starting to look like another Moore's Law. We've been there before, and we should know that this road leads to diminishing returns, higher cost, more complexity, and new risks. Exponentials tend not to end well. Remember Meltdown and Spectre? Do we want to find out what that looks like for AI? Instead of chasing trillion-parameter models (place your bets), wouldn't all be better off if we built practical and efficient solutions that all developers can use to solve real-world problems? Interested in how Hugging Face can help your organization build and deploy production-grade Machine Learning solutions? Get in touch at [email protected] (no recruiters, no sales pitches, please). | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | That Warm Feeling is your GPU Cluster For all its engineering brilliance, training Deep Learning models on GPUs is a brute force technique. According to the spec sheet, each DGX server can consume up to 6.5 kilowatts. Of course, you'll need at least as much cooling power in your datacenter (or your server closet). Unless you're the Starks and need to keep Winterfell warm in winter, that's another problem you'll have to deal with. In addition, as public awareness grows on climate and social responsibility issues, organizations need to account for their carbon footprint. According to this 2019 study from the University of Massachusetts, "training BERT on GPU is roughly equivalent to a trans-American flight". BERT-Large has 340 million parameters. One can only extrapolate what the footprint of Megatron-Turing could be... People who know me wouldn't call me a bleeding-heart environmentalist. Still, some numbers are hard to ignore. | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | Fine-Tune Models If you need to specialize a model, there should be very few reasons to train it from scratch. Instead, you should fine-tune it, that is to say train it only for a few epochs on your own data. If you're short on data, maybe of one these datasets can get you started. You guessed it, that's another way to do transfer learning, and it'll help you save on everything! * Less data to collect, store, clean and annotate, * Faster experiments and iterations, * Fewer resources required in production. In other words: save time, save money, save hardware resources, save the world! If you need a tutorial, the Hugging Face course will get you started in no time. | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | This is your Brain on Deep Learning Researchers estimate that the human brain contains an average of 86 billion neurons and 100 trillion synapses. It's safe to assume that not all of them are dedicated to language either. Interestingly, GPT-4 is expected to have about 100 trillion parameters... As crude as this analogy is, shouldn't we wonder whether building language models that are about the size of the human brain is the best long-term approach? Of course, our brain is a marvelous device, produced by millions of years of evolution, while Deep Learning models are only a few decades old. Still, our intuition should tell us that something doesn't compute (pun intended). | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | Optimize Your Models From compilers to virtual machines, software engineers have long used tools that automatically optimize their code for whatever hardware they're running on. However, the Machine Learning community is still struggling with this topic, and for good reason. Optimizing models for size and speed is a devilishly complex task, which involves techniques such as: * Specialized hardware that speeds up training (Graphcore, Habana) and inference (Google TPU, AWS Inferentia). * Pruning: remove model parameters that have little or no impact on the predicted outcome. * Fusion: merge model layers (say, convolution and activation). * Quantization: storing model parameters in smaller values (say, 8 bits instead of 32 bits) Fortunately, automated tools are starting to appear, such as the Optimum open source library, and Infinity, a containerized solution that delivers Transformers accuracy at 1-millisecond latency. | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | So? Am I excited by Megatron-Turing NLG 530B and whatever beast is coming next? No. Do I think that the (relatively small) benchmark improvement is worth the added cost, complexity and carbon footprint? No. Do I think that building and promoting these huge models is helping organizations understand and adopt Machine Learning ? No. I'm left wondering what's the point of it all. Science for the sake of science? Good old marketing? Technological supremacy? Probably a bit of each. I'll leave them to it, then. Instead, let me focus on pragmatic and actionable techniques that you can all use to build high quality Machine Learning solutions. | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | Use Cloud-Based Infrastructure Like them or not, cloud companies know how to build efficient infrastructure. Sustainability studies show that cloud-based infrastructure is more energy and carbon efficient than the alternative: see AWS, Azure, and Google. Earth.org says that while cloud infrastructure is not perfect, "[it's] more energy efficient than the alternative and facilitates environmentally beneficial services and economic growth." Cloud certainly has a lot going for it when it comes to ease of use, flexibility and pay as you go. It's also a little greener than you probably thought. If you're short on GPUs, why not try fine-tune your Hugging Face models on Amazon SageMaker, AWS' managed service for Machine Learning? We've got plenty of examples for you. | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | Use Smaller Models When evaluating models, you should pick the smallest one that can deliver the accuracy you need. It will predict faster and require fewer hardware resources for training and inference. Frugality goes a long way. It's nothing new either. Computer Vision practitioners will remember when SqueezeNet came out in 2017, achieving a 50x reduction in model size compared to AlexNet, while meeting or exceeding its accuracy. How clever that was! Downsizing efforts are also under way in the Natural Language Processing community, using transfer learning techniques such as knowledge distillation. DistilBERT is perhaps its most widely known achievement. Compared to the original BERT model, it retains 97% of language understanding while being 40% smaller and 60% faster. You can try it here. The same approach has been applied to other models, such as Facebook's BART, and you can try DistilBART here. Recent models from the Big Science project are also very impressive. As visible in this graph included in the research paper, their T0 model outperforms GPT-3 on many tasks while being 16x smaller. You can try T0 here. This is the kind of research we need more of! | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | Deep Learning, Deep Pockets? As you would expect, training a 530-billion parameter model on humongous text datasets requires a fair bit of infrastructure. In fact, Microsoft and NVIDIA used hundreds of DGX A100 multi-GPU servers. At $199,000 a piece, and factoring in networking equipment, hosting costs, etc., anyone looking to replicate this experiment would have to spend close to $100 million dollars. Want fries with that? Seriously, which organizations have business use cases that would justify spending $100 million on Deep Learning infrastructure? Or even $10 million? Very few. So who are these models for, really? | [
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Large Language Models: A New Moore's Law? | juliensimon | October 26, 2021 | large-language-models | analysis, nlp | https://huggingface.co/blog/large-language-models | Use Pretrained Models In the vast majority of cases, you won't need a custom model architecture. Maybe you'll want a custom one (which is a different thing), but there be dragons. Experts only! A good starting point is to look for models that have been pretrained for the task you're trying to solve (say, summarizing English text). Then, you should quickly try out a few models to predict your own data. If metrics tell you that one works well enough, you're done! If you need a little more accuracy, you should consider fine-tuning the model (more on this in a minute). | [
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Course Launch Community Event | sgugger | October 26, 2021 | course-launch-event | community, nlp | https://huggingface.co/blog/course-launch-event | Day 1 (November 15th): A high-level view of Transformers and how to train them The first day of talks will focus on a high-level presentation of Transformers models and the tools we can use to train or fine-tune them. Thomas Wolf: Transfer Learning and the birth of the Transformers library Thomas Wolf is co-founder and Chief Science Officer of HuggingFace. The tools created by Thomas Wolf and the Hugging Face team are used across more than 5,000 research organisations including Facebook Artificial Intelligence Research, Google Research, DeepMind, Amazon Research, Apple, the Allen Institute for Artificial Intelligence as well as most university departments. Thomas Wolf is the initiator and senior chair of the largest research collaboration that has ever existed in Artificial Intelligence: “BigScience”, as well as a set of widely used libraries and tools. Thomas Wolf is also a prolific educator and a thought leader in the field of Artificial Intelligence and Natural Language Processing, a regular invited speaker to conferences all around the world (https://thomwolf.io). Margaret Mitchell: On Values in ML Development Margaret Mitchell is a researcher working on Ethical AI, currently focused on the ins and outs of ethics-informed AI development in tech. She has published over 50 papers on natural language generation, assistive technology, computer vision, and AI ethics, and holds multiple patents in the areas of conversation generation and sentiment classification. She previously worked at Google AI as a Staff Research Scientist, where she founded and co-led Google's Ethical AI group, focused on foundational AI ethics research and operationalizing AI ethics Google-internally. Before joining Google, she was a researcher at Microsoft Research, focused on computer vision-to-language generation; and was a postdoc at Johns Hopkins, focused on Bayesian modeling and information extraction. She holds a PhD in Computer Science from the University of Aberdeen and a Master's in computational linguistics from the University of Washington. While earning her degrees, she also worked from 2005-2012 on machine learning, neurological disorders, and assistive technology at Oregon Health and Science University. She has spearheaded a number of workshops and initiatives at the intersections of diversity, inclusion, computer science, and ethics. Her work has received awards from Secretary of Defense Ash Carter and the American Foundation for the Blind, and has been implemented by multiple technology companies. She likes gardening, dogs, and cats. Jakob Uszkoreit: It Ain't Broke So Don't Fix Let's Break It Jakob Uszkoreit is the co-founder of Inceptive. Inceptive designs RNA molecules for vaccines and therapeutics using large-scale deep learning in a tight loop with high throughput experiments with the goal of making RNA-based medicines more accessible, more effective and more broadly applicable. Previously, Jakob worked at Google for more than a decade, leading research and development teams in Google Brain, Research and Search working on deep learning fundamentals, computer vision, language understanding and machine translation. Jay Alammar: A gentle visual intro to Transformers models Jay Alammar, Cohere. Through his popular ML blog, Jay has helped millions of researchers and engineers visually understand machine learning tools and concepts from the basic (ending up in numPy, pandas docs) to the cutting-edge (Transformers, BERT, GPT-3). Matthew Watson: NLP workflows with Keras Matthew Watson is a machine learning engineer on the Keras team, with a focus on high-level modeling APIs. He studied Computer Graphics during undergrad and a Masters at Stanford University. An almost English major who turned towards computer science, he is passionate about working across disciplines and making NLP accessible to a wider audience. Chen Qian: NLP workflows with Keras Chen Qian is a software engineer from Keras team, with a focus on high-level modeling APIs. Chen got a Master degree of Electrical Engineering from Stanford University, and he is especially interested in simplifying code implementations of ML tasks and large-scale ML. Mark Saroufim: How to Train a Model with Pytorch Mark Saroufim is a Partner Engineer at Pytorch working on OSS production tools including TorchServe and Pytorch Enterprise. In his past lives, Mark was an Applied Scientist and Product Manager at Graphcore, yuri.ai, Microsoft and NASA's JPL. His primary passion is to make programming more fun. | [
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Course Launch Community Event | sgugger | October 26, 2021 | course-launch-event | community, nlp | https://huggingface.co/blog/course-launch-event | Day 2 (November 16th): The tools you will use Day 2 will be focused on talks by the Hugging Face, Gradio, and AWS teams, showing you the tools you will use. Lewis Tunstall: Simple Training with the 🤗 Transformers Trainer Lewis is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. He is also a co-author of an upcoming O’Reilly book on Transformers and you can follow him on Twitter (@_lewtun) for NLP tips and tricks! Matthew Carrigan: New TensorFlow Features for 🤗 Transformers and 🤗 Datasets Matt is responsible for TensorFlow maintenance at Transformers, and will eventually lead a coup against the incumbent PyTorch faction which will likely be co-ordinated via his Twitter account @carrigmat. Lysandre Debut: The Hugging Face Hub as a means to collaborate on and share Machine Learning projects Lysandre is a Machine Learning Engineer at Hugging Face where he is involved in many open source projects. His aim is to make Machine Learning accessible to everyone by developing powerful tools with a very simple API. Sylvain Gugger: Supercharge your PyTorch training loop with 🤗 Accelerate Sylvain is a Research Engineer at Hugging Face and one of the core maintainers of 🤗 Transformers and the developer behind 🤗 Accelerate. He likes making model training more accessible. Lucile Saulnier: Get your own tokenizer with 🤗 Transformers & 🤗 Tokenizers Lucile is a machine learning engineer at Hugging Face, developing and supporting the use of open source tools. She is also actively involved in many research projects in the field of Natural Language Processing such as collaborative training and BigScience. Merve Noyan: Showcase your model demos with 🤗 Spaces Merve is a developer advocate at Hugging Face, working on developing tools and building content around them to democratize machine learning for everyone. Abubakar Abid: Building Machine Learning Applications Fast Abubakar Abid is the CEO of Gradio. He received his Bachelor's of Science in Electrical Engineering and Computer Science from MIT in 2015, and his PhD in Applied Machine Learning from Stanford in 2021. In his role as the CEO of Gradio, Abubakar works on making machine learning models easier to demo, debug, and deploy. Mathieu Desvé: AWS ML Vision: Making Machine Learning Accessible to all Customers Technology enthusiast, maker on my free time. I like challenges and solving problem of clients and users, and work with talented people to learn every day. Since 2004, I work in multiple positions switching from frontend, backend, infrastructure, operations and managements. Try to solve commons technical and managerial issues in agile manner. Philipp Schmid: Managed Training with Amazon SageMaker and 🤗 Transformers Philipp Schmid is a Machine Learning Engineer and Tech Lead at Hugging Face, where he leads the collaboration with the Amazon SageMaker team. He is passionate about democratizing and productionizing cutting-edge NLP models and improving the ease of use for Deep Learning. | [
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Course Launch Community Event | sgugger | October 26, 2021 | course-launch-event | community, nlp | https://huggingface.co/blog/course-launch-event | # Course Launch Community Event We are excited to share that after a lot of work from the Hugging Face team, part 2 of the Hugging Face Course will be released on November 15th! Part 1 focused on teaching you how to use a pretrained model, fine-tune it on a text classification task then upload the result to the Model Hub. Part 2 will focus on all the other common NLP tasks: token classification, language modeling (causal and masked), translation, summarization and question answering. It will also take a deeper dive in the whole Hugging Face ecosystem, in particular 🤗 Datasets and 🤗 Tokenizers. To go with this release, we are organizing a large community event to which you are invited! The program includes two days of talks, then team projects focused on fine-tuning a model on any NLP task ending with live demos like this one. Those demos will go nicely in your portfolio if you are looking for a new job in Machine Learning. We will also deliver a certificate of completion to all the participants that achieve building one of them. AWS is sponsoring this event by offering free compute to participants via Amazon SageMaker. To register, please fill out this form. You will find below more details on the two days of talks. | [
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Scaling up BERT-like model Inference on modern CPU - Part 2 | mfuntowicz | November 4, 2021 | bert-cpu-scaling-part-2 | partnerships, intel, guide, nlp | https://huggingface.co/blog/bert-cpu-scaling-part-2 | # Scaling up BERT-like model Inference on modern CPU - Part 2 | [
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