OMG: An Open MetaGenomic Dataset

The OMG is a 3.1T base pair metagenomic pretraining dataset, combining EMBL's MGnify and JGI's IMG databases. The combined data is pre-processed into a mixed-modality dataset, with translated amino acids for protein coding sequences, and nucleic acids for intergenic sequences. We make two additional datasets available on the HuggingFace Hub:

OG: A subset of OMG consisting of high quality genomes with taxonomic information. OMG_prot50: A protein-only… See the full description on the dataset page: https://huggingface.co/datasets/tattabio/OMG.

Model

GP-GTP is an open-weight genetic-phenotype knowledge language model. For "medical-genetic-information". Arvix version: arXiv:2409.09825

  Attention

Collected and generated by rules. Not fully manually corrected. For general training purpose.

  Usage

Folder s0 for Fill-mask task Folder s1 for QA task

  Cite

Please cite the arXiv version at arXiv:2409.09825

open_genome_packing

Sample packed open genome

  Dataset Description

This dataset was processed using the data-preproc package for vision-language model training.

  Processing Configuration

Base Model: gpt2 Tokenizer: gpt2 Sequence Length: 128000 Processing Type: Vision Language (VL)

  Dataset Features

input_ids: Tokenized input sequences attention_mask: Attention masks for the sequences labels: Labels for language modeling images: PIL Image objects… See the full description on the dataset page: https://huggingface.co/datasets/penfever/open_genome_packing.

Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations, especially where the full context is not available to enable the unambiguous translation in standard machine translation. Despite the increasing popularity of such technique, it lacks sufficient and qualitative datasets to maximize the full extent of its potential. Hausa, a Chadic language, is a member of the Afro-Asiatic language family. It is estimated that about 100 to 150 million people speak the language, with more than 80 million indigenous speakers. This is more than any of the other Chadic languages. Despite the large number of speakers, the Hausa language is considered as a low resource language in natural language processing (NLP). This is due to the absence of enough resources to implement most of the tasks in NLP. While some datasets exist, they are either scarce, machine-generated or in the religious domain. Therefore, there is the need to create training and evaluation data for implementing machine learning tasks and bridging the research gap in the language. This work presents the Hausa Visual Genome (HaVG), a dataset that contains the description of an image or a section within the image in Hausa and its equivalent in English. The dataset was prepared by automatically translating the English description of the images in the Hindi Visual Genome (HVG). The synthetic Hausa data was then carefully postedited, taking into cognizance the respective images. The data is made of 32,923 images and their descriptions that are divided into training, development, test, and challenge test set. The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, image description, among various other natural language processing and generation tasks.