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Nicholas Nguyen
Nicholas Nguyen

Download 500k Mix Txt


IRMNG data are available for download in the Darwin Core Archive (DwCA) format which is described more fully elsewhere on the web, e.g. see The data are in tab-delimited format with no text qualifiers, and after unzipping are provided as .txt files that also can be opened as .csv if desired (simply rename e.g. taxon.txt to taxon.csv, reference.txt to reference.csv, speciesprofile.txt to speciesprofile.csv, ignoring any associated warnings).




Download 500k Mix txt



Note, the IRMNG DwCA "taxon" table can be opened in a text editor, but is likely too large (e.g. >500k rows) to be opened completely in MS Excel. If you import it to a database program e.g. MS Access it will be easier to manage and also rapid to review, sort/filter by any desired field or value, etc. The method below has been developed using MS Access on a Windows PC but other options are of course available.


Please note that the web version of IRMNG may contain additional information on any taxon (for example notes fields) as well as child records (species) for genera which are not included in the download file. (Species data in IRMNG are not maintained as currently as genus records and may be out-of-date and/or contain errors not yet rectified, also some originate from systems that do not permit unrestricted onward distribution at this time). In addition, new content may have been added to the master version of IRMNG on the web which post-dates any specific data dump, but will be picked up next time an export file is created, typically once or twice per year.


For example, using rate=1m,500k would limit reads to 1MiB/sec and writes to500KiB/sec. Capping only reads or writes can be done with rate=,500k orrate=500k, where the former will only limit writes (to 500KiB/sec) and thelatter will only limit reads.


During clone and fetch operations, Git downloads the complete contentsand history of the repository. This includes all commits, trees, andblobs for the complete life of the repository. For extremely largerepositories, clones can take hours (or days) and consume 100+GiB of diskspace.


large binary assets. For example, in a repository where large buildartifacts are checked into the tree, we can avoid downloading allprevious versions of these non-mergeable binary assets and onlydownload versions that are actually referenced.


Partial clone allows us to avoid downloading such unneeded objects inadvance during clone and fetch operations and thereby reduce downloadtimes and disk usage. Missing objects can later be "demand fetched"if/when needed.


Lingo4G will download superuser.com questions from the Internet (about 187 MB) and then prepare them for clustering. If behind a firewall, download and decompress the required archives manually. The whole process may take a few minutes, depending on the speed of your machine and Internet connection. When indexing completes successfully, you should see a message similar to:


The L4G_HOME/datasets directory contains a number of project descriptors you can use to index and analyze selected publicly available document sets. With the exception of the PubMed data set, Lingo4G will attempt to download the data set from the Internet (if behind a firewall, download and unpack the data sets manually). The following table summarizes the available example data sets.


Due to the large size of the original data set, Lingo4G does not download it automatically by default. Please see datasets/dataset-pubmed/README.txt for detailed instructions. Statistics accurate for the dataset dump as of March, 2018.


Due to the large size of the original data set (nearly 140 GB of compressed XML files), Lingo4G does not download it automatically by default. Please see datasets/dataset-uspto/README.txt for detailed instructions.


Due to the large size of the original data set, Lingo4G does not download it automatically by default. Please see datasets/dataset-wikipedia/README.txt for detailed instructions. Statistics accurate for the dataset dump as of March, 2018.


The URLs listed in onMissing section provide several alternative locations for downloading the same set of files. So, in the example above, only the first archive needs to be downloaded and uncompressed (research.gov.7z), the second array contains URLs pointing at essentially the same data (but not compressed, so it'll take longer to download).


If the unpack attribute is set to true (which is also the default value, if missing), Lingo4G will extract files from the downloaded archives automatically. You can perform this step manually using the built-in command unpack or using any other utilities applicable for a given archive type.


Click the Export button to initiate the export file download. Please note that for large export files it may take several seconds for the download to begin. Click the Copy as curl button to copy to clipboard the curl command invocation that will fetch the configured export content directly from Lingo4G REST API. Click the Copy as JSON button to copy the JSON request specification you can then use to request the result as configured in the export dialog.


The default vector size of 96 is sufficient for most small projects with not more than 500k labels used for embedding. For larger projects with more than 500k labels, a vector size of 128 may increase the quality of embeddings. For largest projects, with more than 1M label embeddings, vector size of 160 may further increase the quality of embedding, at the cost of longer learning time. 041b061a72


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