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Practical LLMs for Real-World Applications

Authors:
(1) Suzanna Sia, Johns Hopkins University;
(2) David Mueller;
(3) Kevin Duh. Table of Links Abstract and 1. Background
2. Data and Settings
3. Where does In-context MT happen?
4. Characterising Redundancy in Layers
5. Inference Efficiency
6. Further Analysis
7. Conclusion, Acknowledgments, and References
A. Appendix A. Appendix A.1. Graphical View of Context Masking Experiments A.2. Prompt Format A.3. Additional Results on English & Spanish In addition to the language pairs en → fr and fr → en, we also run experiments on English and Spanish language pairs, both en → es and es → en. Due to space limitations, we plot the results of those experiments here. Overall, we see largely identical trends on both directions of English and Spanish to what we observe on English and French translation tasks, leading us to conclude that our conclusions generalize across different translation tasks. A.4. Autoregressive Decoder only Transformer The transformer consists of stacked blocks of self-attention, which itself consists of smaller units of self-attention heads that are concatenated before being fed through a fully connected layer. In autoregressive decoder-only transformers, training and inference adopts a causal mask, where current positions are only able to attend to previous timesteps, instead of being able to attend to the entire input sequence. Unlike encoder-decoder NMT models where source and target sentence have separate processing transformer blocks, decoder-only means that the same model weights are both used to “encode" the source sentence and “decode" the target sentence in a single continuous sequence. A.5. Training with Autoregressive Translation The original language modeling objective in GPT training involves predicting the entire input token sequence which consists of both the source and target sentence (shifted by 1 position). We found this to produce slightly worse results than only minimising the negative log likelihood of predicting the target sentence to be translated, and not the entire sequence. We consider this autoregressive translation training. A.6. L0 Attention Gate Training Training Details For Section 6.5, We train using Adam Optimizer (β1 = 0.9, β2 = 0.999) with a batch size of 32, and learning rate of 0.001, early stopping patience of 10 and threshold of 0.01. We initialise attention head gates to be 1 instead of random or 0.5 as this leads to faster convergence. We experiment with two different training settings, the 0-prompts Train setting and the 5-prompts Train setting. As described in Section A.5, we train the model by predicting only the target sentence, conditioned on the context. In the 0-prompt setting, the context consists of the instructions and the source sentence to be translated. In the 5-prompt setting, the context consists of the instructions, 5 prompt examples, and the source sentence to be translated. In the 0-prompt setting, the conditional prefix consists of the instructions and the source sentence to be translated. In the 5-prompt setting, the conditional prefix consists of the instruction, 5 source target sentence pairs, and the source sentence to be translated. Data We used the first 10,000 lines of en → fr from WMT06 Europarl (Koehn, 2005) for training.[7] To test the generalisability of trained attention head gates, we use a different test domain, FLORES (Goyal et al., 2021) to reflect the scarcity of in-domain data. We also test an additional language direction en→pt in FLORES to see if training can generalise across languages. Training Details We train using Adam Optimizer (β1 = 0.9, β2 = 0.999) with a batch size of 32, and learning rate of 0.001. We use a large early stopping patience of 10 and threshold of 0.01, and train for up to 100 epochs. This is due to the nature of L0 training; we do not expect performance to improve over many iterations and would like the attention gates to keep training as long as there is no large loss in performance. We initialise attention head gates to be 1 instead of random or 0.5 as this leads to much faster convergence and better performance. For the regularisation weight λ, we search over a hyperparameter set of {0.1, 0.01, 0.001, 0.0001} and found 0.01 performs best on the validation set. A.7. L0 head masking experiments. Additional experiments on L0 head masking in the fr→ en and es→ en direction. A.8. Generalisability of L0 gate training We experiment with 0-prompts and 5-prompts in training and using λ = 0 (no regularisation) and λ = 0.01. L0 training for the 0-prompts shows some gains for the 0-prompts test case, and with no loss on the 5-prompts test case (Table 2). Notably, this persists in en→pt, a different language direction from training. The robustness of translation performance under multiple testing conditions (number of prompts, datasets, language directions) gives some confidence that the trained discrete attention head gates from L0 support a general ability to translate (Table 2). In contrast, the soft attention head gates without regularisation (λ = 0) appear to overfit as they perform well on some conditions but deteriorate in others. We observe that 0-prompt training for L0(λ = 0.01) also A.9. Qualitative Analysis of Layer-wise Masking BLOOM is observed to be more robust to masking of layers; suggesting that task location is more distributed. For the 5-prompt setting, the performance only decreases very slightly. For the 0-prompt setting, we observe that similar to GPTNEO, performance drops when masking out the middle layers. At the aggregate level, BLOOM appears to still be translating (> 0 BLEU) even when layers are masked. However we observe that the drop in performance is due to around 40 to 50% of the test sentences scoring < 5 BLEU points. There is a clear failure to translate, not simply producing poorer translations. This paper is available on arxiv under CC BY 4.0 DEED license. [7] Data available from https://www.statmt.org/europarl/ Authors: (1) Suzanna Sia, Johns Hopkins University; (2) David Mueller; (3) Kevin Duh. Authors: Authors: (1) Suzanna Sia, Johns Hopkins University; (2) David Mueller; (3) Kevin Duh. Table of Links Abstract and 1. Background 2. Data and Settings 3. Where does In-context MT happen? 4. Characterising Redundancy in Layers 5. Inference Efficiency 6. Further Analysis 7. Conclusion, Acknowledgments, and References A. Appendix Abstract and 1. Background Abstract and 1. Background 2. Data and Settings 2. Data and Settings 3. Where does In-context MT happen? 3. Where does In-context MT happen? 4. Characterising Redundancy in Layers 4. Characterising Redundancy in Layers 5. Inference Efficiency 5. Inference Efficiency 6. Further Analysis 6. Further Analysis 7. Conclusion, Acknowledgments, and References 7. Conclusion, Acknowledgments, and References A. Appendix A. Appendix A. Appendix A.1. Graphical View of Context Masking Experiments A.2. Prompt Format A.3. Additional Results on English & Spanish In addition to the language pairs en → fr and fr → en, we also run experiments on English and Spanish language pairs, both en → es and es → en. Due to space limitations, we plot the results of those experiments here. Overall, we see largely identical trends on both directions of English and Spanish to what we observe on English and French translation tasks, leading us to conclude that our conclusions generalize across different translation tasks. A.4. Autoregressive Decoder only Transformer The transformer consists of stacked blocks of self-attention, which itself consists of smaller units of self-attention heads that are concatenated before being fed through a fully connected layer. In autoregressive decoder-only transformers, training and inference adopts a causal mask, where current positions are only able to attend to previous timesteps, instead of being able to attend to the entire input sequence. Unlike encoder-decoder NMT models where source and target sentence have separate processing transformer blocks, decoder-only means that the same model weights are both used to “encode" the source sentence and “decode" the target sentence in a single continuous sequence. A.5. Training with Autoregressive Translation The original language modeling objective in GPT training involves predicting the entire input token sequence which consists of both the source and target sentence (shifted by 1 position). We found this to produce slightly worse results than only minimising the negative log likelihood of predicting the target sentence to be translated, and not the entire sequence. We consider this autoregressive translation training. A.6. L0 Attention Gate Training Training Details For Section 6.5, We train using Adam Optimizer (β1 = 0.9, β2 = 0.999) with a batch size of 32, and learning rate of 0.001, early stopping patience of 10 and threshold of 0.01. We initialise attention head gates to be 1 instead of random or 0.5 as this leads to faster convergence. We experiment with two different training settings, the 0-prompts Train setting and the 5-prompts Train setting. As described in Section A.5, we train the model by predicting only the target sentence, conditioned on the context. In the 0-prompt setting, the context consists of the instructions and the source sentence to be translated. In the 5-prompt setting, the context consists of the instructions, 5 prompt examples, and the source sentence to be translated. In the 0-prompt setting, the conditional prefix consists of the instructions and the source sentence to be translated. In the 5-prompt setting, the conditional prefix consists of the instruction, 5 source target sentence pairs, and the source sentence to be translated. Data We used the first 10,000 lines of en → fr from WMT06 Europarl (Koehn, 2005) for training.[7] To test the generalisability of trained attention head gates, we use a different test domain, FLORES (Goyal et al., 2021) to reflect the scarcity of in-domain data. We also test an additional language direction en→pt in FLORES to see if training can generalise across languages. Data Training Details We train using Adam Optimizer (β1 = 0.9, β2 = 0.999) with a batch size of 32, and learning rate of 0.001. We use a large early stopping patience of 10 and threshold of 0.01, and train for up to 100 epochs. This is due to the nature of L0 training; we do not expect performance to improve over many iterations and would like the attention gates to keep training as long as there is no large loss in performance. We initialise attention head gates to be 1 instead of random or 0.5 as this leads to much faster convergence and better performance. For the regularisation weight λ, we search over a hyperparameter set of {0.1, 0.01, 0.001, 0.0001} and found 0.01 performs best on the validation set. Training Details A.7. L0 head masking experiments. Additional experiments on L0 head masking in the fr→ en and es→ en direction. A.8. Generalisability of L0 gate training We experiment with 0-prompts and 5-prompts in training and using λ = 0 (no regularisation) and λ = 0.01. L0 training for the 0-prompts shows some gains for the 0-prompts test case, and with no loss on the 5-prompts test case (Table 2). Notably, this persists in en→pt, a different language direction from training. The robustness of translation performance under multiple testing conditions (number of prompts, datasets, language directions) gives some confidence that the trained discrete attention head gates from L0 support a general ability to translate (Table 2). In contrast, the soft attention head gates without regularisation (λ = 0) appear to overfit as they perform well on some conditions but deteriorate in others. We observe that 0-prompt training for L0(λ = 0.01) also A.9. Qualitative Analysis of Layer-wise Masking BLOOM is observed to be more robust to masking of layers; suggesting that task location is more distributed. BLOOM For the 5-prompt setting, the performance only decreases very slightly. For the 0-prompt setting, we observe that similar to GPTNEO, performance drops when masking out the middle layers. At the aggregate level, BLOOM appears to still be translating (> 0 BLEU) even when layers are masked. However we observe that the drop in performance is due to around 40 to 50% of the test sentences scoring < 5 BLEU points. There is a clear failure to translate, not simply producing poorer translations. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [7] Data available from https://www.statmt.org/europarl/

Where does In-context Translation Happen in Large Language Models: Appendix

Why 1-Bit Transformers Will Change the World

This paper is available on arxiv under CC 4.0 license. . Authors: Gemini Team, Google Table of Links Abstract and Introduction Model Architecture Training Infrastructure Training Dataset Evaluation Responsible Deployment Discussion and Conclusion, References Contributions and Acknowledgments Appendix 9. Appendix 9.1. Chain-of-Thought Comparisons on MMLU benchmark We contrast several chain-of-thought approaches on MMLU and discuss their results in this section. We proposed a new approach where model produces k chain-of-thought samples, selects the majority vote if the model is confident above a threshold, and otherwise defers to the greedy sample choice. The thresholds are optimized for each model based on their validation split performance. The proposed approach is referred to as uncertainty-routed chain-of-thought. The intuition behind this approach is that chain-of-thought samples might degrade performance compared to the maximum-likelihood decision when the model is demonstrably inconsistent. We compare the gains from the proposed approach on both Gemini Ultra and GPT-4 in Figure 7. We find that Gemini Ultra benefits more from this approach compared to using only chain-of-thought samples. GPT-4’s performance improves from 84.2% with greedy sampling to 87.3% with uncertainty-routed chain-of-thought approach with 32 samples, but it already achieves these gains from using 32 chain-of-thought samples. In contrast, Gemini Ultra improves its performance significantly from 84.0% with greedy sampling to 90.0% with uncertainty-routed chain-of-thought approach with 32 samples while it marginally improves to 85.0% with the use of 32 chain-of-thought samples only. 9.2. Capabilities and Benchmarking Tasks We use more than 50 benchmarks as a holistic harness to evaluate the Gemini models across text, image, audio and video. We provide a detailed list of benchmarking tasks for six different capabilities in text understanding and generation: factuality, long context, math/science, reasoning, summarization, and multilinguality. We also enumerate the benchmarks used for image understanding, video understanding, and audio understanding tasks. • : We use 5 benchmarks: BoolQ (Clark et al., 2019), NaturalQuestions-Closed (Kwiatkowski et al., 2019), NaturalQuestions-Retrieved (Kwiatkowski et al., 2019), RealtimeQA (Kasai et al., 2022), TydiQA-noContext and TydiQA-goldP (Clark et al., 2020). Factuality We use 6 benchmarks: NarrativeQA (Kočiský et al., 2018), Scrolls-Qasper, Scrolls-Quality (Shaham et al., 2022), XLsum (En), XLSum (non-English languages) (Hasan et al., 2021), and one other internal benchmark. • Long Context: We use 8 benchmarks: GSM8k (with CoT) (Cobbe et al., 2021), Hendryck’s MATH pass@1 (Hendrycks et al., 2021b), MMLU (Hendrycks et al., 2021a), Math-StackExchange, Math-AMC 2022-2023 problems, and three other internal benchmarks. • Math/Science: • : We use 7 benchmarks: BigBench Hard (with CoT) (Srivastava et al., 2022; Suzgun et al., 2022), CLRS (Veličković et al., 2022), Proof Writer (Tafjord et al., 2020), Reasoning-Fermi problems (Kalyan et al., 2021), Lambada (Paperno et al., 2016), HellaSwag (Zellers et al., 2019), DROP (Dua et al., 2019). Reasoning • : We use 5 benchmarks: XL Sum (English), XL Sum (non-English languages) (Hasan et al., 2021), WikiLingua (non-English languages), WikiLingua (English) (Ladhak et al., 2020), XSum (Narayan et al., 2018). Summarization • : We use 10 benchmarks: XLSum (Non-English languages) (Hasan et al., 2021), WMT22 (Kocmi et al., 2022), WMT23 (Tom et al., 2023), FRMT (Riley et al., 2023), WikiLingua (Non-English languages) (Ladhak et al., 2020), TydiQA (no context), TydiQA (GoldP) (Clark et al., 2020), MGSM (Shi et al., 2023), translated MMLU (Hendrycks et al., 2021a), NTREX (Federmann et al., 2022), FLORES-200 (Team et al., 2022). Multilinguality We use 9 benchmarks for image understanding: MMMU (Yue et al., 2023), TextVQA (Singh et al., 2019), DocVQA (Mathew et al., 2021), ChartQA (Masry et al., 2022), InfographicVQA (Mathew et al., 2022), MathVista (Lu et al., 2023), AI2D (Kembhavi et al., 2016), VQAv2 (Goyal et al., 2017), XM3600 (Thapliyal et al., 2022) for multi-lingual image understanding, and 6 benchmarks for video understanding: VATEX (Wang et al., 2019) for captioning in two different languages, YouCook2 (Zhou et al., 2018), NextQA (Xiao et al., 2021), ActivityNet-QA (Yu et al., 2019), and Perception Test MCQA (Pătrăucean et al., 2023). • Image and Video: • : We use 5 benchmarks including automatic speech recognition (ASR) tasks such as FLEURS (Conneau et al., 2023), VoxPopuli (Wang et al., 2021), Multi-lingual Librispeech (Pratap et al., 2020), and automatic speech translation task such as CoVoST 2 (Wang et al., 2020). Audio 9.3. Qualitative Examples This section shows sample qualitative examples from prompting the Gemini Ultra model. Some illustrative examples of multimodal reasoning for image understanding tasks over charts, natural images and memes are shown in Figures 8, 9, 11, 13, 14, and 15. Figure 10 shows an example of image generation capabilities of Gemini Ultra where the user generates an interleaved sequence of image and text to design a blog post. Beyond English, Figure 16 shows model’s capability to understand images in a multilingual setting. Gemini models also show strong performance on multimodal image understanding and reasoning in mathematics, as shown in Figures 12, 18 and 19. Figure 20 is an example of complex multimodal reasoning demonstrating how the model composes complex image understanding, code generation, and instruction following capabilities for a given user task. In Figure 17, we see another example of the model being able to generate working code and follow complex user instructions. Finally, Figure 21 shows an example of Gemini Ultra’s capability of understanding video by reasoning over temporally connected set of frames. 9.3.1. Chart understanding and reasoning over data 9.3.2. Multimodal question answering 9.3.3. Interleaved image and text generation 9.3.4. Image understanding and reasoning 9.3.5. Geometrical reasoning 9.3.6. Information seeking about objects 9.3.7. Multimodal reasoning based on visual cues 9.3.8. Multimodal humor understanding 9.4. Commonsense reasoning in a multilingual setting 9.4.1. Reasoning and code generation 9.4.2. Mathematics: Calculus 9.5. Multi-step reasoning and mathematics 9.5.1. Complex image understanding, code generation, and instruction following 9.5.2. Video understanding and reasoning

Gemini - A Family of Highly Capable Multimodal Models: Appendix

Gemini - A Family of Highly Capable Multimodal Models: Discussion and Conclusion, References

Gemini - A Family of Highly Capable
Multimodal Models: Evaluation

Operation Weather.

Breaking Barriers: Decentralized Mobile Networks As A Solution To Global Inequality In a world where mobile connectivity is as essential as electricity, it's baffling to realize that not all regions enjoy equal access to this vital infrastructure. Unveiling the crux of this issue, let's explore how decentralization can revolutionize the global telecommunication industry and bring about economic equity. Bridging the Gap: Decentralized Mobile Networks and the Promise of Global Connectivity Despite the leaps and bounds in mobile technology, there's an undeniable gap that separates the digitally connected world from regions that are still grappling with limited or nonexistent mobile network access. This disparity is not just a case of technological haves and have-nots; it's a severe impediment to the affected regions' full participation in the global economy. On the other hand, existing networks are primarily centralized systems where a few major players hold sway over infrastructure, pricing, and data management. Users and small-scale operators have limited economic incentives or power to influence these network environments, leading to a system that often fails to meet their unique needs. A World Without Digital Divide: How Decentralization is Making it Happen The root of the problem lies in the centralization of the mobile networks. The traditional telecom model, dominated by a small number of companies, restricts competition and innovation while often overlooking underserved regions due to lower profitability. Moreover, centralization gives network providers control over user data, leading to privacy concerns. They determine the costs, data caps, and network rules, making the consumer a passive participant rather than an active stakeholder. Decentralization: The Game Changer Enter decentralized mobile networks. By transferring control and ownership from corporations to individual users and operators, decentralized systems could tackle many of the problems that plague our current mobile network infrastructure. Firstly, these systems democratize network access by enabling individuals and businesses to set up and operate nodes, effectively bringing connectivity to underserved areas. Instead of relying on a centralized company's profitability analysis, networks grow organically based on community needs, bridging the digital divide. Secondly, users are financially incentivized to contribute to the network, such as running a node or sharing their resources. This economic motivation could drive network expansion and encourage active participation in the mobile ecosystem, fostering competition and innovation. Moreover, a decentralized mobile network is fundamentally designed to respect user data privacy, returning control of personal data to the user. The Great Digital Divide: A Cool Solution for an Uncool Problem Imagine a world where every person, no matter where they live, has a superfast internet connection right on their smartphone. Sounds cool, right? But here's a bummer - we're not there yet. But don't worry, because World Mobile has a solution that's just as cool. : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program Here's the thing, even though we have crazy fast 5G networks and smartphones that seem straight out of a sci-fi movie, not everyone around the globe gets to enjoy this. Some regions, especially remote or poorer areas, don't have a good mobile network. This means they can't enjoy all the benefits that come with being connected to the world, like online education, e-commerce, or just chilling with Netflix. Plus, the big companies that provide these mobile services usually control everything, from how fast your internet is to how much you need to pay for it. It's like being in a race where the track, the rules, and even the rewards are controlled by a few people. Not exactly fair, huh? But here's the cool part. is trying to change this game by creating a mobile ecosystem where everyone can participate, and the best part? They can even earn money from it! This is what we call a decentralized system - where power doesn't stay with a few big companies but is shared among the users. World Mobile Imagine having your own tiny piece of the internet that you can control, and even better, get paid for helping it run smoothly. This is done using something called World Mobile Token (WMT), which is like digital money or points that you can earn. To make this even more exciting and to make sure that everyone who contributes is rewarded, has come up with a great idea. They're buying back these WMT tokens from people. It's like a thank-you gift to those who help in making this cool new mobile world possible, and it also makes the WMT more valuable. World Mobile So, while it's a bummer that not everyone gets to enjoy the digital wonders of the 21st century, there's hope. With the power of decentralization, World Mobile is paving the way for a future where everyone, everywhere, can enjoy the benefits of mobile connectivity - and even get rewarded for it! Now, how cool is that? Don’t forget to like and share the story!

How World Mobile is Making the Internet Fair - and Fun - for Everyone!

¡Cómo World Mobile está haciendo que Internet sea justo y divertido para todos!

Comment World Mobile rend l'Internet juste - et amusant - pour tout le monde !

कैसे वर्ल्ड मोबाइल इंटरनेट को हर किसी के लिए मेला - और मजेदार - बना रहा है!

World Mobile はインターネットをどのようにしてすべての人にとって公平で楽しいものにしているのでしょうか。

Como a World Mobile está tornando a Internet justa - e divertida - para todos!

World Mobile đang làm cho Internet trở nên công bằng - và thú vị - cho mọi người như thế nào!

World Mobile 如何为每个人打造公平且有趣的互联网！

The two most widely-used open-source machine learning frameworks for training and building deep learning models are TensorFlow and PyTorch. These frameworks have unique differences in their approach to building and training models. While TensorFlow uses a static graph and declarative programming, which provides better optimization opportunities and allows for distributed training, PyTorch makes use of a dynamic computational graph and imperative programming, which allows for a more flexible and easy debugging process. The choice of frameworks depends on the user's specific needs and preferences. This article looks at the available today. Best PyTorch Datasets for Building Deep Learning Models Ultimate List of Open PyTorch Datasets 1. Penn Treebank The Penn Treebank is a collection of annotated English text, which is extensively studied in the natural language processing (NLP) research community. It comprises over 4.5 million words of text from various genres such as magazines, news articles and fictional stories. The dataset also consists of manually annotated information about name entitles, part of speech tags and syntactic structure, used to train and evaluate a wide range of NLP models including language models, parsers and machine translation systems. To download this dataset, click . here 2. Stanford Question Answering Dataset (SQuAD) The SQuAD (Stanford Question Answering Dataset) is a popular benchmark dataset in natural language processing (NLP) that comprises more than 100,000 question-answer pairs, extracted from a set of Wikipedia articles. It is used to evaluate the performance of various NLP models built using PyTorch or other deep learning frameworks. The answer spans have an average length of 3.6 words, and there are 11.0 words in the corresponding passages on average. Here are some extra details about the SQuAD: - It contains 100,000+ question-answer pairs on 536 articles from English Wikipedia. SQuAD 1.0 - It contains 150,000+ question-answer pairs on 100,000+ Wikipedia articles, with additional unanswerable questions to test the model's ability to abstain from answering. SQuAD 2.0 The goal of the dataset is to provide a challenging task for machine learning models to answer questions about a given text passage. Click here to download the . latest version (SQuAD 2.0) 3. Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) This PyTorch dataset is a public dataset of computed tomography images of the chest that has been annotated for lung nodules by multiple radiologists. It comprises 1,018 CT scans collected from various institutions and contains over 23,000 annotated nodules. Each scan in the dataset is accompanied by annotations from four experienced radiologists which provide information on shape, size, nodule location and texture. The dataset was created to support research into the development of computer-aided diagnosis (CAD) systems for lung cancer screening and diagnosis. Note: The dataset is publicly available but subject to certain restrictions and requirements for usage. Click to download. here 4. Fashion-MNIST The PyTorch Fashion MNIST dataset was created by Zalando Research as a replacement for the original MNIST dataset and is available in a PyTorch-compatible format.  The PyTorch Fashion MNIST dataset comprises 70,000 grayscale images of clothing items, including 60,000 training images and 10,000 test images. The images are 28x28 pixels in size and represent 10 different classes of clothing items, including ­T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. It is similar to the original MNIST dataset, but with more challenging classification tasks due to the greater complexity and variety of the clothing items. The dataset can be downloaded . here 5. Yelp Reviews The Yelp Reviews dataset is an extensive collection of over 5 million reviews of local businesses from 11 metropolitan areas in the United States. Each review in the dataset contains information such as the star rating, business category, review text, date, and location. It is a valuable resource interested in building deep learning models with PyTorch. Please sign up and click here to download the . dataset 6. Stanford Dogs This PyTorch Dataset comprises a collection of 20,580 high-quality images of 120 different breeds of dogs, each labelled with information about the breed of the dog in the image. It can be used for image classification and object recognition. With the dataset provided in JPEG format and annotations in a text file, the images are of size 224x224 pixels. The breeds in the dataset range from common breeds such as Golden Retrievers and Labradors to more obscure breeds such as the Otterhound and the Sussex Spaniel. You can download the here. dataset 7. Caltech 101 While the limited number of images per category can be a challenge, the detailed annotations make the PyTorch Caltech 101 dataset a valuable resource for evaluating deep learning models. The dataset is a labelled computer vision dataset with 9,144 high-quality images of objects across 101 categories.  It also covers a wide range of object categories and the images were obtained from various sources. Each image is labelled with the category of object and image, which makes it simpler to use with a variety of deep learning frameworks. Click here to . download 8. STS-B (Semantic Textual Similarity Benchmark) The STS-B (Semantic Textual Similarity Benchmark) dataset is an English dataset used in the STS tasks organized in the context of SemEval between 2012 and 2017. It comprises 8628 sentence pairs with human-assigned similarity scores on a scale of 1 to 5. Drawn from various sources, like news articles, forum posts, images with captions and covering a wide range of topics, it is a popular dataset used with PyTorch for evaluating models’ performance in determining the semantic similarity between two sentences. The STS-B dataset is available in multiple formats, including PyTorch-compatible formats, as PyTorch is a deep-learning framework for training and evaluating models on this dataset. \To download this dataset, click . here 9. WMT'14 English-German This PyTorch dataset is a benchmark dataset for machine translation between English and German created by Stanford in 2015. It comprises parallel corpora of sentence-aligned texts in both English and German, which are used to build and evaluate deep learning models. While the test sets contain 3,000 sentence pairs each, the training set consists of approximately 4.5 million sentence pairs and the average sentence length is 26 words in English and 30 words in German, with a vocabulary size of about 160,000 words for English and 220,000 words for German. You can scroll down and download it . here 10. CelebA This dataset is a popular large-scale face attributes dataset which comprises over 200,000 celebrity images. It was first released by researchers at the Chinese University of Hong Kong in 2015. An image in the CelebA consists of 40 facial attributes such as age, hair colour, facial expression and gender. Also, these images were retrieved from the internet and cover a wide range of facial appearances, including different races, ages and genders. Bounding box annotations for the location of the face in each image, as well as 5 landmark points for the eyes, nose and mouth. Note: The CelebA dataset is under the license of the Creative Commons Attribution-Noncommercial-Share, which permits it to be used for non-commercial research purposes as long as proper credit is given. To use the CelebA dataset in PyTorch, you can use the class, which is part of the module. You can download the dataset from the or . torchvision.datasets.CelebA torchvision website here 11. UCF101 The UFC101 dataset is widely used for video classification in the field of computer vision. It comprises 13,230 videos of human actions from 101 action categories, each containing around 100 to 300 videos. The PyTorch UCF101 dataset is a preprocessed version of the original UCF101 that is ready to use in PyTorch. The preprocessed dataset comprises video frames that have been normalized and resized, including corresponding labels for each video. It’s also split into three sets: training, validation and testing, with approximately 9,500, 3,500 and 3,000 videos, respectively. To download the dataset, click . here 12. HMDB51 The HMDB51 dataset is a collection of videos retrieved from collected from various sources, including movies, TV shows and online videos, comprising 51 action classes, each with at least 101 video clips. It was created by researchers at the University of Central Florida in 2011 for research in human action recognition. The videos are in AVI format and have a resolution of 320x240 pixels, with ground-truth annotations for each video, including the action class label and the start and end frames of the action within the video. Each video in the dataset represents a person performing an action in front of a static camera. The actions include a wide range of activities every day, such as jumping, waving, drinking, and brushing teeth, as well as complex actions like playing guitar and horseback riding. Note: It is used in concomitance with the dataset, which contains a larger number of action classes but with fewer videos per class. UCF101 You can download the dataset . here 13. ActivityNet The ActivityNet is a large-scale video understanding dataset that comprises over 20,000 videos from a diverse set of categories like cooking, sports, dancing etc. The videos have an average length of 3 minutes and are annotated with an average of 1.41 activity segments.  It is available in PyTorch, which is easy to use in deep learning frameworks. The PyTorch version offers preprocessed features extracted from the RGB frames and optical flow fields of each video, as well as ground truth annotations for the temporal segments and labels of activity. You can download the dataset . here 14. VOC dataset (Visual Object Classes) The VOC dataset (Visual Object Classes) was first introduced in 2005 as part of the PASCAL VOC Challenge, which aimed at advancing the state of the art in visual recognition. It consists of images of 20 different object categories, including animals, vehicles, and common household objects. Each of these images is annotated with the locations and classifications of objects within the image. The annotations include both bounding boxes and pixel-level segmentation masks. The dataset is split into two main sets: the training and validation sets. The training set contains approximately 5,000 images with annotations, while the validation set contains around 5,000 images without annotations. In addition, the dataset also includes a test set with approximately 10,000 images, but the annotations for this set are not publicly available. To download the recent dataset, you can download it from the , load it in Python via our open source with just one line of code, or download it from . website Deep Lake Kaggle 15. YCB-Video This dataset is a collection of 3D object models and video sequences designed for object recognition and pose estimation tasks. It contains 21 everyday household items, with each object captured in various lighting conditions and camera viewpoints. The dataset provides pixel-level ground truth annotations and is commonly used for evaluating computer vision algorithms and robotic systems. Click here to download the . dataset 16. KITTI The KITTI dataset is a collection of computer vision data for autonomous driving research. It includes over 4000 high-resolution images, LIDAR point clouds, and sensor data from a car equipped with various sensors. The dataset provides annotations for object detection, tracking, and segmentation, as well as depth maps and calibration parameters. The KITTI dataset is widely used for training and evaluating deep learning models for autonomous driving and robotics. To download the recent dataset, you can download it from the , or download it from . website GitHub Kaggle 17. BraTS The BRATS PyTorch dataset is a collection of magnetic resonance imaging (MRI) scans for brain tumour segmentation. It consists of over 200 high-resolution 3D brain images, each with four modalities (T1, T1c, T2, and FLAIR) and corresponding binary segmentation masks. The dataset is commonly used for training and evaluating deep learning models for automated brain tumour detection and segmentation. You can download this dataset on Kaggle by clicking . here 18. Multi-Human Parsing The Multi-Human Parsing PyTorch dataset is a large-scale human image dataset with pixel-level annotations for human part parsing. It contains over 26,000 images of humans, each segmented into 18 human part labels. The dataset is used for training and evaluating deep learning models for human pose estimation, segmentation and action recognition. To download the dataset, click . here 19. Charades This dataset is a large-scale video dataset for action recognition and localization. It comprises over 9,800 videos of daily activities, such as cooking, cleaning, and socializing, with an average length of 30 seconds per video. The dataset provides detailed annotations for each video, including temporal boundaries for actions and atomic visual concepts, making it suitable for training and evaluating deep learning models for action recognition, detection and segmentation. The Charades PyTorch Dataset is widely used in the computer vision research community and is freely available for and use. download 20. TU Berlin This dataset is a rich collection of high-resolution images and 3D object poses for object detection and pose estimation. It contains over 11,000 images of 60 object categories, with annotations for 2D and 3D poses. With its large size and diverse object categories, the TU Berlin PyTorch dataset provides an excellent testbed for developing robust and accurate object detection and pose estimation models. You can get the dataset directly from the website by clicking . here Common Use Cases for PyTorch Datasets Natural Language Processing Penn Treebank Stanford Question Answering Dataset (SQuAD) STS-B (Semantic Textual Similarity Benchmark) WMT'14 English-German Yelp Reviews Computer Vision CelebA Caltech 101 Stanford Dogs YCB-Video VOC dataset (Visual Object Classes) KITTI Multi-Human Parsing Charades TU Berlin Fashion-MNIST Medical Image Analysis Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) BraTS Human Activity Recognition UCF101 HMDB51 ActivityNet Final Thoughts PyTorch is useful for research and experimentation, where the focus is often on developing deep learning models and exploring new approaches. Additionally, PyTorch has gained a reputation as a research-focused framework with a growing community of developers and researchers contributing to the ecosystem. These datasets have applications in multiple fields and are also available for anyone to download and use freely. The lead image of this article was generated via HackerNoon's AI Stable Diffusion model using the prompt 'PyTorch superimposed images'.

20 Best PyTorch Datasets for Building Deep Learning Models

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The modern approach to API development is often associated with the use of the cloud. Therefore, it has become increasingly important to present novel ideas capable of improving users' and developers' digital experiences. With that in mind, the DynamicDocs API approach to PDF generation is to use LaTeX, a language that is designed for high-quality typesetting The DynamicDocs API provides an alternative to deploying a stand-alone PDF-generating solution for startups and enterprises. The API approach allows developers to test the solution quickly via Postman and generate large amounts of PDF while incorporating graphics and logic into the templates. In this article, we present a specific type of JSON to PDF template called , which allows users to send their own LaTeX source file and compile it via the API. TEX to PDF What Is LaTeX? LaTeX is a computer language used for document preparation, specifically PDF generation. LaTeX source files have a Tex extension and are typically compiled into PDFs. Although it is very popular in academia, where it is used for dissertations and journal articles, the language lends itself well to creating great-looking documents in general. One of its advantages is the separation of document content and the rules governing the look and feel of the document. A notable disadvantage of LaTeX is that the learning curve for writing the rules of the document layout can be steep and require a significant investment in time for the user. To circumvent this, DynamicDocs API has a library of ready-made PDF to JSON templates that do not require any knowledge of LaTeX. However, this article assumes that you already have a working Tex file that needs to be compiled. LaTeX Compilers To convert LaTeX source files into PDFs, one needs a compiler. DynamicDocs API offers two compilers, namely pdfLaTeX and LuaLaTeX. Tex to PDF Template range consists of two general templates allowing users to upload their own tex file and optionally include Base64 encoded images in the JSON payload. The templates correspond to the two compilers available. For more information on calling the two templates, visit Template and Template. Tex to PDF with pdfLaTeX Tex to PDF with LuaLaTeX Let's now look at the case when it makes sense to use TEX to PDF conversion with an API. Considerations for TEX to PDF with DynamicDocs API The following list explains the criteria for when using DynamicDocs API makes sense for TEX to PDF conversion: You need to generate several PDF documents and want to do it programmatically. You have (or can create) a Tex file that can be adjusted with the dynamic data. You can make a POST request to the API with your TeX file formatted as JSON. If any of those points do not apply to your use case, you will likely be better off using a simple online or . Tex to PDF converter Overleaf Tutorial to Convert TEX to PDF with DynamicDocs API Consider a situation where you have to create PDF name tags where the underlying data is stored in the system and can be accessed programmatically. The aim is to create a PDF document that looks as follows: To get started with DynamicDocs API, you will need to sign up for an account with ADVICEment. Without needing to give bank card information, the new accounts are automatically set up with a FREE plan which offers a limited number of API calls. Once the account email address is confirmed, users can log in and access the Dashboard, where they can get their account API key. We are now almost ready to make the API call. To create the PDF document above, we have written the following Tex file: \documentclass[10pt]{article}

\usepackage[paperheight=3in, paperwidth=4in, left=0mm, right=0mm, top=0mm, bottom=0mm, noheadfoot]{geometry}
\usepackage[T1]{fontenc}
\usepackage[sfdefault,lf]{carlito}
\usepackage{graphicx}
\usepackage{ragged2e}
\usepackage{xcolor}
\usepackage{tikz}
\usetikzlibrary{calc}
\usepackage[none]{hyphenat}

%%%%%%%%%%%%%%%%%%%%%%% content definition %%%%%%%%%%%%%%%%%%%%%%%
\makeatletter\let\thetitle\@title\makeatother
\usepackage[pdfencoding=auto,unicode, bookmarks=false, colorlinks=false, pdfborder={0 0 0},pdftitle={Name Tag}, pdfauthor={Advicement}, pdfsubject={Name Tag}, pdfkeywords={Name Tag}]{hyperref}

%%%%%%%%%%%%%%%%%%%%%% color definitions %%%%%%%%%%%%%%%%%%%%%%
\definecolor{White}{HTML}{FFFFFF} 
\definecolor{Black}{HTML}{000000} 
\definecolor{AdvGreen}{HTML}{0DBB72} 
\definecolor{AdvGray}{HTML}{383838}
\definecolor{AdvDarkGray}{HTML}{303030}

\begin{document} 
\pagecolor{White}
\begin{tikzpicture}[remember picture,overlay]
\node (rec1) [rectangle, inner sep=0.0cm, outer sep=0, draw = AdvGreen, anchor=north west, fill = AdvGreen, minimum width = 9.16cm, minimum height = 2cm] at ([xshift=0.5cm, yshift=-0.5cm]current page.north west) {}; 
\node (rec2) [rectangle, inner sep=0.0cm, outer sep=0, draw = AdvGreen, anchor=south west, fill = AdvGreen, minimum width = 9.16cm, minimum height = 0.3cm] at ([xshift=0.5cm, yshift=0.5cm]current page.south west) {}; 
\node (image) [anchor=north] at ([xshift=0cm, yshift=-0.4cm]rec1.north) {\advGetImage{key = image1, width=3.5cm, type = png}}; 
\node (name) [anchor=north, inner sep=0.0cm, outer sep=0] at ([xshift=0cm, yshift=-3.75cm]current page.north) {\color{AdvGreen} \bfseries \Huge{Name}};
\node (rec3) [rectangle, inner sep=0.0cm, outer sep=0, draw = AdvGreen, anchor=north, fill = AdvGreen, minimum width = 1cm, minimum height = 0.01cm] at ([xshift=0.0cm, yshift=-0.25cm]name.south) {}; 
\node (role) [anchor=north, inner sep=0.0cm, outer sep=0] at ([xshift=0cm, yshift=-0.25cm]rec3.north) {\color{AdvGreen} \LARGE{Title}};
\end{tikzpicture} 
\end{document} Changing this Tex file by including different names and titles for each name tag can be done programmatically. Although at first glance, this looks like a normal Tex file, notice the function, which is a custom function used within DynamicDocs API to convert the image from JSON payload and insert it into the file. \advGetImage Notice that it references the key in the JSON payload below. For more information on this function, visit our page. image1 API documentation Now, we are ready to formulate our JSON payload, which looks as follows: {
"texFileContent": "\\documentclass[10pt]{article}\n\n\\usepackage[paperheight=3in, paperwidth=4in, left=0mm, right=0mm, top=0mm, bottom=0mm, noheadfoot]{geometry}\n\\usepackage[T1]{fontenc}\n\\usepackage[sfdefault,lf]{carlito}\n\\usepackage{graphicx}\n\\usepackage{ragged2e}\n\\usepackage{xcolor}\n\\usepackage{tikz}\n\\usetikzlibrary{calc}\n\\usepackage[none]{hyphenat}\n\n%%%%%%%%%%%%%%%%%%%%%%% content definition %%%%%%%%%%%%%%%%%%%%%%%\n\\makeatletter\\let\\thetitle\\@title\\makeatother\n\\usepackage[pdfencoding=auto,unicode, bookmarks=false, colorlinks=false, pdfborder={0 0 0},pdftitle={Name Tag}, pdfauthor={Advicement}, pdfsubject={Name Tag}, pdfkeywords={Name Tag}]{hyperref}\n\n%%%%%%%%%%%%%%%%%%%%%% color definitions %%%%%%%%%%%%%%%%%%%%%%\n\\definecolor{White}{HTML}{FFFFFF} \n\\definecolor{Black}{HTML}{000000} \n\\definecolor{AdvGreen}{HTML}{0DBB72} \n\\definecolor{AdvGray}{HTML}{383838}\n\\definecolor{AdvDarkGray}{HTML}{303030}\n\n\\begin{document} \n\\pagecolor{White}\n\\begin{tikzpicture}[remember picture,overlay]\n\\node (rec1) [rectangle, inner sep=0.0cm, outer sep=0, draw = AdvGreen, anchor=north west, fill = AdvGreen, minimum width = 9.16cm, minimum height = 2cm] at ([xshift=0.5cm, yshift=-0.5cm]current page.north west) {}; \n\\node (rec2) [rectangle, inner sep=0.0cm, outer sep=0, draw = AdvGreen, anchor=south west, fill = AdvGreen, minimum width = 9.16cm, minimum height = 0.3cm] at ([xshift=0.5cm, yshift=0.5cm]current page.south west) {}; \n\\node (image) [anchor=north] at ([xshift=0cm, yshift=-0.4cm]rec1.north) {\\advGetImage{key = image1, width=3.5cm, type = png}}; \n\\node (name) [anchor=north, inner sep=0.0cm, outer sep=0] at ([xshift=0cm, yshift=-3.75cm]current page.north) {\\color{AdvGreen} \\bfseries \\Huge{Name}};\n\\node (rec3) [rectangle, inner sep=0.0cm, outer sep=0, draw = AdvGreen, anchor=north, fill = AdvGreen, minimum width = 1cm, minimum height = 0.01cm] at ([xshift=0.0cm, yshift=-0.25cm]name.south) {}; \n\\node (role) [anchor=north, inner sep=0.0cm, outer sep=0] at ([xshift=0cm, yshift=-0.25cm]rec3.north) {\\color{AdvGreen} \\LARGE{Title}};\n\\end{tikzpicture} \n\\end{document}",      
"image1": 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"
} Note that the stringified Tex file has to be put under the key in the JSON. Furthermore, the value contains double backslashes where single backslashes were previously used in the Tex file. texFileContent texFileContent The double backslashes are a result of stringifying the TEX file. Finally, the JSON contains a Base64 encoded image which is referenced in the function above. This allows users to make images dynamic in the documents. \advGetImage We are now ready to make the API call and POST the JSON above to TEX to PDF with the pdfLaTeX template endpoint: https://api.advicement.io/v1/templates/pub-tex-to-pdf-with-pdflatex-v1/compile To do so successfully, you will have to authenticate your API request by including the API key as a header parameter as follows: Header Key Value Adv-Security-Token {{Your Account API Key}} To make the API call above, we suggest using and viewing the for example. Postman DynamicDocs API – Public Workspace The response of the API call is a simple JSON with the location of the Status JSON. To see whether the PDF was generated successfully, we can GET the Status JSON using the URL found in the response. If the PDF is generated successfully, the Status JSON will look as follows and contain the link to download the PDF: {
"id": "47c2451a-8aa8-47eb-b5db-842dc770b53a",
"startedAt": "Tue Oct 09 2022 19:03:57 GMT+0000 (Coordinated Universal Time)",
"statusCode": 201,
"statusDescription": "document created successfully",
"calculationLogUrl": "https://advicement-prod-api-calls.s3.eu-west-1.amazonaws.com/test/blank-template/47c2451a-8aa8-47eb-b5db-842dc770b53a/output/log/calcLog.txt?AWSAccessKeyId=ASIA2L...",
"latexLogUrl": "https://advicement-prod-api-calls.s3.eu-west-1.amazonaws.com/test/blank-template/47c2451a-8aa8-47eb-b5db-842dc770b53a/output/log/inputFile.log?AWSAccessKeyId=ASIA2L...",
"documentUrl": "https://advicement-prod-api-calls.s3.eu-west-1.amazonaws.com/test/blank-template/47c2451a-8aa8-47eb-b5db-842dc770b53a/output/inputFile.pdf?AWSAccessKeyId=ASIA2L..."
} We have completed the LaTeX to PDF process using an API. Of course, the Tex file above can be replaced with any working LaTeX source file. The approach above gives developers a powerful option of creating a large variety of different document types. Final Remarks As outlined in this article, DynamicDocs API caters to a specific use case when doing TEX to PDF conversion (see the Consideration section to see whether your use case fits). In an event where you have ticked all the conditions, TEX to PDF templates give the option of creating different document types, provided users can create their own working Tex file and dynamically populate it with their data. DynamicDocs API runs a full TeX Live 2021 Distribution, meaning that users have access to an extensive range of the latest LaTeX packages and fonts. For quick integration, follow the steps above or visit page. DynamicDocs API – Public Workspace via Postman

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