The second line under the menu bar includes race date, track code, race number, distance, surface dirt is brown, turf is green , type maiden, claiming, allowance, handicap, stakes , claiming price or purse value, age and gender eligibility requirements, and the current screen view is highlighted in red. The third line from left to right lists the criteria for the sort order from top to bottom and the various function keys F2-F10 available to sort the horses according to the factor underneath them.
The fourth line shows the factor being used to sort by color and to the right, the factors available to be sorted in this screen view. The factor highlighted indicates the current top to bottom sort order. If two or more horses have the same rating for the factor being sorted by color, they will all appear in the same color.
The fifth line shows the PAR ratings for this type of race for the factors listed above each. The horses are listed underneath the broken line and are shown in order from top to bottom according to the factor highlighted in the fourth line.
XRD file has been imported the order of finish and payoff values including exotics will be listed in the bottom section. Otherwise this section of the screen will appear blank. Many choices may be made from the VIEW screen the most important of which is the default configuration.
The default settings determine which factors are most important and determine which past pacelines are being selected to determine rankings.
These settings are dealt with in detail in the sections following this overview. This option allows the user to review the pacelines that have been automatically selected by the default setup. The user has the option to choose a different paceline or multiple pacelines up to 3 to be averaged. The user even has the option to modify a paceline if none of the past performance line are indicative of the race the horse is likely to run today. Another frequently used option from this screen is the VIEW option.
Choosing this option will allow you to choose different factors to view. Each of the various VIEW options are dealt with in detail in the section following this overview. By using this function you will be able to spot trends and dominant race factors that are occurring at each track you handicap. Adjust your configurations to emphasize the peculiarities at your track and see the difference in your ROI. That means you have to have an account in Google's Cloud Platform.
The actual pricing is complicated and depends on a lot of factors. If you're processing the data inside the Google Cloud, it is most likely free. TFDS is incompatible with requester-pays buckets, so you have to download the data locally before you can use it.
To do that, run this in your shell:. Not everyone likes the Tensorflow native format, and it is uncompressed, so the files sizes are much larger. For that reason, we also prepared the data in JSON format. Thank you! Huggingface uses Git Large File Storage to actually store the data, so you will need to install that on your machine to get to the files.
This will download 13TB to your local drive. If you want to be more precise with what you are downloading, follow these commands instead:. The git clone command in this variant will download a bunch of stub files that Git LFS uses, so you can see all the filenames that exist that way. You can then convert the stubs into their real files with git lfs pull --include " For example, if you wanted all the Dutch documents from the multilingual set, you would run.
Big ups to the good folks at Common Crawl whose data made this possible consider donating! By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. This is great news! And thanks for making it available. C4 is arguably an expensive and difficult dataset to get, but even more so the multilingual part. So if y'all have extracted that data and there is a way to access it or transfer it that would be awesome :. We just released it!
Check out the updated instructions, or look at the post at I get the following error. Thanks for making it available. We just downloaded the. The multilingual version isn't filtered anyways, so there isn't a block list to remove Would it be possible to separately provide a manifest of the files with MD5 checksums for validation purposes? When you get the files from Huggingface's git repository, they already have SHA1 checksums. Does MD5 provide additional value to you?
If so, I'd be happy to add that. Is AllenNLP the entity that should be credited when we attribute this content? Thanks for putting it together in any case! I guess credit Google for developing the code and AI2 for running it and publishing the results? A heads up that you'll have to specify your GCP project to perform requester-pays.
So it should look like. You can just download and process everything for free for any language thanks to the Common Crawl team! Pardon for my bluntness, buy this looks like a shameless ad for Google, their proritary formats and algorithms.
The C4 dataset is based on common crawl, but it is not the same. C4 cleans the data, discarding duplicates, spam, offensive content, etc. Also, C4 is the dataset used to train the T5 model, so you might need that exact data to do comparisons or baselines. This works. The tutorial in the main post should include the git lfs pull step for those of us new to LFS :P.
It might also have needed git switch main as I'd run that before the lfs pull. Just in case. Go check it out! For reference, these are the sizes of the sets:. It seems only 3. Are there any plans to offer other versions? Sorry, there are no plans to release the earlier variants. It's quite expensive to process these, so we don't want to do it again for a small difference in versions.
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