PB 13S Zip
The first thing we might want to do is see how much each store purchases and rank themfrom the largest to the smallest. We have limited resources so we should focus on those placeswhere we get the best bang for the buck. It will be easier for us to call on a couple ofbig corporate accounts instead of a lot of mom and pop stores.
PB 13S zip
There are several good resources that I used to learn how to use np.select. Thisarticle from Dataquest is a good overview. I also found this presentation from Nathan Cheeververy interesting and information. I encourage you to check both of these out.
The other change I made with the generalize function is that the original value will be preservedif there is no default value provided. Instead of using combine_first, thefunction will take care of it all. Finally, I turned off the regex match by default for asmall performance improvement.
The reason this works is pretty straightforward. When pandas converts a column to acategorical type, pandas will only call the expensive str.contains() functionon each unique text value. Because this data set has a lot of repeated data, we get a hugeperformance boost.
However, as the data grows in size (imagine doing this analysis for 50 states worth of data),you will need to understand how to use pandas in an efficient manner for text cleaning. Myhope is that you bookmark this article and come back to it when you face a similar problem.
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Hasnaoui, S.E.; Fahr, M.; Zouine, M.; Smouni, A. De Novo Transcriptome Assembly, Gene Annotations, and Characterization of Functional Profiling Reveal Key Genes for Lead Alleviation in the Pb Hyperaccumulator Greek Mustard (Hirschfeldia incana L.). Curr. Issues Mol. Biol. 2022, 44, 4658-4675.
Hasnaoui SE, Fahr M, Zouine M, Smouni A. De Novo Transcriptome Assembly, Gene Annotations, and Characterization of Functional Profiling Reveal Key Genes for Lead Alleviation in the Pb Hyperaccumulator Greek Mustard (Hirschfeldia incana L.). Current Issues in Molecular Biology. 2022; 44(10):4658-4675.
Hasnaoui, Said El, Mouna Fahr, Mohamed Zouine, and Abdelaziz Smouni. 2022. "De Novo Transcriptome Assembly, Gene Annotations, and Characterization of Functional Profiling Reveal Key Genes for Lead Alleviation in the Pb Hyperaccumulator Greek Mustard (Hirschfeldia incana L.)" Current Issues in Molecular Biology 44, no. 10: 4658-4675.
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The main advantage of a subword tokenizer is that it interpolates between word-based and character-based tokenization. Common words get a slot in the vocabulary, but the tokenizer can fall back to word pieces and individual characters for unknown words.
This tutorial builds a Wordpiece vocabulary in a top down manner, starting from existing words. This process doesn't work for Japanese, Chinese, or Korean since these languages don't have clear multi-character units. To tokenize these languages consider using text.SentencepieceTokenizer, text.UnicodeCharTokenizer or this approach.
This section generates a wordpiece vocabulary from a dataset. If you already have a vocabulary file and just want to see how to build a text.BertTokenizer or text.WordpieceTokenizer tokenizer with it then you can skip ahead to the Build the tokenizer section.
There are many arguments you can set to adjust its behavior. For this tutorial, you'll mostly use the defaults. If you want to learn more about the options, first read about the algorithm, and then have a look at the code.
If you replace the token IDs with their text representations (using tf.gather) you can see that in the first example the words "searchability" and "serendipity" have been decomposed into "search ##ability" and "s ##ere ##nd ##ip ##ity":
This tutorial builds the text tokenizer and detokenizer used by the Transformer tutorial. This section adds methods and processing steps to simplify that tutorial, and exports the tokenizers using tf.saved_model so they can be imported by the other tutorials.
It's worth noting here that there are two versions of the WordPiece algorithm: Bottom-up and top-down. In both cases goal is the same: "Given a training corpus and a number of desiredtokens D, the optimization problem is to select D wordpieces such that the resulting corpus is minimal in thenumber of wordpieces when segmented according to the chosen wordpiece model."
TensorFlow Text's vocabulary generator follows the top-down implementation from BERT. Starting with words and breaking them down into smaller components until they hit the frequency threshold, or can't be broken down further. The next section describes this in detail. For Japanese, Chinese and Korean this top-down approach doesn't work since there are no explicit word units to start with. For those you need a different approach.
The algorithm is iterative. It is run for k iterations, where typically k = 4, but only the first two are really important. The third and fourth (and beyond) are just identical to the second. Note that each step of the binary search runs the algorithm from scratch for k iterations.
However, there is a problem: This algorithm will severely overgenerate wordpieces. The reason is that we only subtract off counts of prefix tokens.Therefore, if we keep the word human, we will subtract off the count for h,hu, hu, huma, but not for ##u, ##um, ##uma, ##uman and so on. So we mightgenerate both human and ##uman as word pieces, even though ##uman willnever be applied.
So why not subtract off the counts for every substring, not just everyprefix? Because then we could end up subtracting off the counts multipletimes. Let's say that we're processing s of length 5 and we keep both(##denia, 129) and (##eniab, 137), where 65 of those counts came from theword undeniable. If we subtract off from every substring, we would subtract65 from the substring ##enia twice, even though we should only subtractonce. However, if we only subtract off from prefixes, it will correctly only besubtracted once.
Subsequent iterations are identical to the first, with one importantdistinction: In step 2, instead of considering every substring, we apply theWordPiece tokenization algorithm using the vocabulary from the previousiteration, and only consider substrings which start on a split point.
For example, let's say that we're performing step 2 of the algorithm andencounter the word undeniable. In the first iteration, we would consider everysubstring, e.g., u, un, und, ..., undeniable, ##n, ##nd, ..., ##ndeniable,....
The WordPiece algorithm will segment this into un ##deni ##able (see thesection Applying WordPiece for more information). In thiscase, we will only consider substrings that start at a segmentation point. Wewill still consider every possible end position. So during the seconditeration, the set of s for undeniable is:
The algorithm is otherwise identical. In this example, in the first iteration,the algorithm produces the spurious tokens ##ndeni and ##iable. Now, thesetokens are never considered, so they will not be generated by the seconditeration. We perform several iterations just to make sure the results converge(although there is no literal convergence guarantee).
Eventually, we will either find a subtoken in our vocabulary, or get down to asingle character subtoken. (In general, we assume that every character is in ourvocabulary, although this might not be the case for rare Unicode characters. Ifwe encounter a rare Unicode character that's not in the vocabulary we simply mapthe entire word to ). 041b061a72