SMOL

SMOL (Set for Maximal Overall Leverage) is a collection of professional translations into 221 Low-Resource Languages, for the purpose of training translation models, and otherwise increasing the representations of said languages in NLP and technology. Please read the SMOL Paper and the GATITOS Paper for a much more thorough description! There are four resources in this directory:

SmolDoc: document-level translations into 100 languages SmolSent: sentence-level translations into… See the full description on the dataset page: https://huggingface.co/datasets/google/smol.

The benchmark dataset are consisted of 2

Dataset Card for Evaluation run of google/mt5-small

Dataset automatically created during the evaluation run of model google/mt5-small The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/google_mt5-small-details.

Tips from Google about marketing a small business online.

A May 2024 study analyzed the small towns in Italy with a population of under **** thousand with the highest average monthly number of Google searches in 2023. Based on the analysis, *** Sicilian destinations, Favignana and San Vito Lo Capo, recorded the highest figure, each with an average of ****** monthly Google searches in 2023. Portofino in Liguria followed in the ranking, with ****** monthly Google searches on average that year.

Images of small objects for small instance detections. Currently four object types are available. ![]() We collect four datasets of small objects from images/videos on the Internet (e.g.YouTube or Google). Fly Dataset: contains 600 video frames with an average of 86 ± 39 flies per frame (648×72 @ 30 fps). 32 images are used for training (1:6:187) and 50 images for testing (301:6:600). Honeybee Dataset: contains 118 images with an average of 28 ± 6 honeybees per image (640×480). The dataset is divided evenly for training and test sets. Only the first 32 images are used for training. Fish Dataset: contains 387 frames of video with an average of 56±9 fish per frame (300×410 @ 30 fps). 32 images are used for training (1:3:94) and 65 for testing (193:3:387). Seagull Dataset: contains three high-resolution images (624×964) with an average of 866±107 seagulls per image. The first image is used for training, and the res

Dataset Card for Evaluation run of google/flan-t5-small

Dataset automatically created during the evaluation run of model google/flan-t5-small The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/google_flan-t5-small-details.

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Modern dog and cat owners increasingly use internet resources to obtain information on pet health issues. While access to online information can improve owners’ knowledge of patient care and inform conversations with their veterinarian during consultations, there is also a risk that owners will misinterpret online information or gain a false impression of current standards in veterinary medicine. This in turn can cause problems or tensions, for example if the owner delays consulting their veterinarian about necessary treatment, or questions the veterinarian’s medical advice. Based on an online questionnaire aimed at dog and cat owners in Austria, Denmark and the United Kingdom (N = 2117) we investigated the use of internet resources to find veterinary medical information, the type of internet resources that were used, and whether owner beliefs explain how often they used the internet to find medical information about their pet. Approximately one in three owners reported that they never used internet resources prior to (31.7%) or after (37.0%) a consultation with their veterinarian. However, when owners do make use of the internet, our results show that they were more likely to use it before than after the consultation. The most common internet resources used by owners were practice websites (35.0%), veterinary association websites (24.0%), or ‘other’ websites providing veterinary information (55.2%). Owners who believe that the use of internet resources enables them to have a more informed discussion with their veterinarians more often use internet resources prior to a consultation, whereas owners who believed that internet resources help them to make the right decision for their animal more often use internet resources after a consultation. The results suggest that veterinarians should actively ask pet owners if they use internet resources, and what resources they use, in order to facilitate open discussion about information obtained from the internet. Given that more than a third of pet owners use practice websites, the findings also suggest that veterinarians should actively curate their own websites where they can post information that they consider accurate and trustworthy.

As of January 2024, the majority of Google employees worldwide, almost 66 percent, were male. The distribution of male and female employees at Google hasn’t seen a big change over the recent years. In 2014 the share of female employees at Google was 30.6 percent. In 2021 this number has increased by only 3 percent. Considering that the total number of Google employees increased greatly between the years 2007 and 2020, the female quota among the employees had seen rather a small increase. Google as a company Google is a diverse internet company that provides a wide range of digital products and services. In 2022, the company’s global revenue was over 279 billion U.S. dollars. Most of its revenue, around 305 billion U.S. dollars, was from advertising. Among its services, the most popular ones are YouTube and Google Play. Male and female employees at tech companies Google is not the only tech company with a lower number of female employees. This pattern can be seen in other big tech companies too. In 2019, in a ranking of 20 leading tech companies worldwide, only 23andMe had more than a 50 percent share of female employees. The majority of tech companies in the ranking have far more male than female employees.

The Google Workspace Business Tools market, encompassing applications like Gmail, Docs, Sheets, and Drive, is experiencing robust growth fueled by the increasing adoption of cloud-based solutions and the rising demand for collaborative work environments. The market's expansion is driven by several key factors, including the enhanced productivity and efficiency offered by integrated tools, the accessibility provided by mobile and web interfaces, and the growing need for secure data storage and sharing. While precise market sizing data is not provided, considering the extensive market penetration of Google Workspace and the overall growth in the SaaS (Software as a Service) market, a reasonable estimate for the 2025 market value would be in the range of $10 billion to $15 billion, potentially reaching $20 billion by 2030. This estimate considers factors such as the robust growth in cloud computing, the increasing number of businesses adopting digital workspaces, and the global expansion of internet connectivity. Growth is primarily driven by adoption among small and medium-sized enterprises (SMEs), given Google Workspace's competitive pricing and ease of use compared to more complex enterprise solutions. However, large enterprises contribute significantly to the overall market value due to their higher purchasing power and complex business needs that Google Workspace addresses with its advanced features and integrations. The market faces some challenges, including competition from established players like Microsoft 365 and Salesforce, as well as security concerns related to data breaches and privacy. However, Google's continuous innovation, ongoing improvements to security protocols, and strategic partnerships are mitigating these risks. Future growth will likely be driven by further integration with other Google services, the expansion of AI-powered features, and increasing demand for tailored solutions for specific industries. This will solidify Google Workspace's position as a leading provider of collaborative business tools and further expand its market share. Regionally, North America and Europe will continue to dominate the market, owing to high levels of digitalization and adoption of cloud technologies. However, rapid growth is anticipated in the Asia-Pacific region driven by increasing internet penetration and economic growth in emerging markets.

This statistic shows the leading search engine providers used by small to medium sized enterprise (SME) owners in the United States in order to be found more quickly as of *************. During the Statista survey conducted in *************, ** percent of responding SME owners said that they had paid or were considering to pay Google in order to be found more quickly in their search engine.

All of the data together is around 41GB. It's the last hidden states of 131,072 samples from refinedweb padded/truncated to 512 tokens on the left, fed through google/flan-t5-small. Structure: { "encoding": List, shaped (512, 512) aka (tokens, d_model), "text": String, the original text that was encoded, "attention_mask": List, binary mask to pass to your model with encoding to not attend to pad tokens }

just a tip, you cannot load this with the RAM in the free ver of google colab, not… See the full description on the dataset page: https://huggingface.co/datasets/crumb/flan-t5-small-embed-refinedweb.

This is a set of one-second .wav audio files, each containing a single spoken English word or background noise. These words are from a small set of commands, and are spoken by a variety of different speakers. This data set is designed to help train simple machine learning models. This dataset is covered in more detail at https://arxiv.org/abs/1804.03209.

Version 0.01 of the data set (configuration "v0.01") was released on August 3rd 2017 and contains 64,727 audio files.

In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".

In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".

In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation it is marked by True value of "is_unknown" feature). Their function is to teach a model to distinguish core words from unrecognized ones.

The _silence_ class contains a set of longer audio clips that are either recordings or a mathematical simulation of noise.

Kokborok Digitalisation Project

The Kokborok Digitalisation Project is an initiative to curate and enhance parallel data for the Kokborok-English language pair. This project builds upon the SMOL dataset by Google, available on Hugging Face, and involves modifying and correcting it to better reflect the nuances of the local Kokborok dialect.

  From the Author

"Language is a living, breathing entity—constantly evolving, shaping cultures, and connecting generations. When we… See the full description on the dataset page: https://huggingface.co/datasets/sdmy/kokborok.