In order to use the Latent semantic indexation method from gensim, I want to begin with a small "classique" example like :
import logging, gensim, bz2
id2word = gensim.corpora.Dictionary.load_from_text('wiki_en_wordids.txt')
mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm')
lsi = gensim.models.lsimodel.LsiModel(corpus=mm, id2word=id2word, num_topics=400)
etc..
My question is : How to get the corpus iterator 'wiki_en_tfidf.mm' ? Must I download it from somewhere ? I have searched on the Internet but I did not find anything. Help please ?
The first page of search results includes a link to:
https://radimrehurek.com/gensim/wiki.html
which says "First let’s load the corpus iterator and dictionary, created in the second step above."
Step 2 is
Convert the articles to plain text (process Wiki markup) and store the result as sparse TF-IDF vectors. In Python, this is easy to do
on-the-fly and we don’t even need to uncompress the whole archive to
disk. There is a script included in gensim that does just that, run:
$ python -m gensim.scripts.make_wiki
Related
I would like to download and load the pre-trained word2vec for analyzing Korean text.
I download the pre-trained word2vec here: https://drive.google.com/file/d/0B0ZXk88koS2KbDhXdWg1Q2RydlU/view?resourcekey=0-Dq9yyzwZxAqT3J02qvnFwg
from the Github Pre-trained word vectors of 30+ languages: https://github.com/Kyubyong/wordvectors
My gensim version is 4.1.0, thus I used:
KeyedVectors.load_word2vec_format('./ko.bin', binary=False) to load the model. But there was an error that :
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
I already tried many options including in stackoverflow and Github, but it still not work well.
Would you mind letting me the suitable solution?
Thanks,
While the page at https://github.com/Kyubyong/wordvectors isn't clear about the formats this author has chosen, by looking at their source code at...
https://github.com/Kyubyong/wordvectors/blob/master/make_wordvectors.py#L61
...shows it using the Gensim model .save() method.
Such saved models should be reloaded using the .load() class method of the same model class. For example, if a Word2Vec model was saved with...
model.save('language.bin')
...then it could be reloaded with...
loaded_model = Word2Vec.load('language.bin')
Note, through, that:
Models saved this way are often split over multiple files that should be kept together (and all start with the same root name) - but I don't see those here.
This work appears to be ~5 years old, based on a pre-1.0 version of Gensim – so there might be issues loading the models directly into the latest Gensim. If you do run into such issues, & absolutely need to make these vectors work, you might need to temporarily use a prior version of Gensim to .load() the model. Then, you could save the plain vectors out with .save_word2vec_format() for later reloading across any version. (Or, using the latest interim version that can load the model, re-save the model as .save(), then repeat the process with the latest version that can read that model, until you reach the current Gensim.)
But, you also might want to find a more recent & better-documented set of pretrained word-vectors.
For example, Facebook makes FastText pretrained vectors available in both a 'text' format and a 'bin' format for many languages at https://fasttext.cc/docs/en/pretrained-vectors.html (trained on Wikipedia only) or https://fasttext.cc/docs/en/crawl-vectors.html (trained on Wikipedia plus web crawl data).
The 'text' format should in fact be loadable with KeyedVectors.load_word2vec_format(filename, binary=False), but will only include full-word vectors. (It will also be relatively easy to view as text, or write simply code to massage into other formats.)
The 'bin' format is Facebook's own native FastText model format, and should be loadable with either the load_facebook_model() or load_facebook_vectors() utility methods. Then, the loaded model (or vectors) will be able to create the FastText algorithm's substring-based guesstimate vectors even for many words that weren't in the model or training data.
When trying to upload the fasttext model (cc.nl.300.bin) in gensim I get the following error:
!wget https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.nl.300.bin.gz
!gunzip cc.nl.300.bin.gz
model = FastText_gensim.load_fasttext_format('cc.nl.300.bin')
model.build_vocab(cleaned_text, update=True)
AttributeError: 'FastTextTrainables' object has no attribute 'syn1neg'
The code goes wrong when building the vocab with my own dataset. The format of that dataset is all right, as I already used it to build and train other (not pre-trained) Word2Vec and FastText models.
I saw other had the same error on this blog, however their solution did not work for me: https://github.com/RaRe-Technologies/gensim/issues/2588
Also, I read somewhere that I should use 'load_facebook_model'? However I was not able to import load_facebook_model at all? Is this even a good way to solve this problem?
Any other suggestions?
Are you sure you're using the latest version of Gensim, 4.0.1, with many improvements to the FastText implementation?
And, there you will definitely want to use .load_facebook_model() to load a full .bin Facebook-format model:
https://radimrehurek.com/gensim/models/fasttext.html#gensim.models.fasttext.load_facebook_model
But also note: the post-training expansion of the vocabulary is best considered an advanced & experimental function. It may not offer any improvement on typical tasks - indeed, without careful consideration of tradeoffs & balancing influence of later traiing against earlier, it can make things worse.
A FastText model trained on a large, diverse corpus may already be able to synthesize better-than-nothing guess vectors for out-of-vocabulary words, via its subword vectors.
If there's some data with very-different words & word-senses you need to integrate, it will often be better to re-train from scratch, using an equal combination of all desired text influences. Then you'll be doing things in a standard and balanced way, without harder-to-tune and harder-to-evaluate improvised changes to usual practice.
What I want exactly is to cluster words and phrases, e.g.
knitting/knit loom/loom knitting/weaving loom/rainbow loom/home decoration accessories/loom knit/knitting loom/...And I don'd have corpus while I have only the words/phrases. Could I use a pre-trained model like the one from GoogleNews/Wikipedia/... to realise it?
I am trying now to use Gensim to load GoogleNews pre-trained model to get phrases similarity. I've been told that The GoogleNews model includes vectors of phrases and words. But I find that I could only get word-similarity while phrase-similarity fails with an error message that the phrase is not in the vocabulary. Please advise me. Thank you.
import gensim
from gensim.models import Word2Vec
from gensim.models.keyedvectors import KeyedVectors
GOOGLE_MODEL = '../GoogleNews-vectors-negative300.bin'
model = gensim.models.KeyedVectors.load_word2vec_format(GOOGLE_MODEL, binary=True)
# done well
model.most_similar("computer", topn=3)
# done with error message "computer_software" is not in the vocabulory.
model.most_similar("computer_software", topn=3)
The GoogleNews set does include many multi-word phrases, as created via some statistical analysis, but might not include something specific you're hoping it does, like 'computer_software'.
On the other hand, I see an online word-list suggesting that a phrase like 'composite_fillings' is in the GoogleNews vocabulary, so this will likely work for you:
model.most_similar("composite_fillings", topn=3)
With that vector-set, you're limited to what they chose to model as phrases. If you need similarly-strong vectors for other phrases, you'd likely need to train your own model, on a corpus where the phrases important to you have been combined into single tokens. (If you just need something-better-than-nothing, averaging together the constituent words' word-vectors would give you something to work with... but that's a pretty-crude stand-in for truly modeling the bigram/multigram against its unique contexts.)
I trained my model in Nvidia Digits 5 and I would now like to extract the accuracy and loss plots that were generated during training for a report. Is this data saved somewhere so that it would possible to extract the data for these plots so that I could plot it in Python and perhaps ultimately modify the plots to compare different models etc?
The best solution I have found is to either look at the HTML file or to scan the text file caffe_output.log that is produced by Caffe. The text file is usually stored in /var/digits/jobs/insert_your_job_id/ but you can also just run on linux systems:
locate caffe_output.log
Go to your DIGITS job folder and locate your job's subfolder. Inside you'll find a file status.pickle, which is a pickled object containing all your job's information.
You can load it in python like so:
import digits
import pickle
data = pickle.load(open('status.pickle','rb'))
This object is somewhat generic and may contain multiple tasks. For a typical classification task it will likely be just one, but you will still need to access it via data.tasks[0]. From there you can grab the plots:
data.tasks[0].combined_graph_data()
which returns a somewhat convoluted dict (unfortunately - since your network can produce many accuracy/loss outputs, as well as even custom ones). It contains everything you need though - I managed to plot accuracy with:
plt.plot( data.tasks[0].combined_graph_data()['columns'][2][1:] )
but it's likely that you'll have to write a bit of custom code. As always, dir() is your friend.
I have a huge amount of documents (mainly pdfs and doc's) I want to classify, so I can search over them according to certain tags. These tags could either be of my own (I put the tags to the document) or extracted from the text.
I've just seen a post related to this (Classify data using Apache Mahout), but perhaps there is something even more simple.
Mahout might be overkill for your problem - but you can get a fairly quick, easy solution by using OpenNLP.
http://opennlp.sourceforge.net/api/index.html
Specifically, look at the opennlp.tools.doccat package. Essentially, you have to go through and manually tag a small(ish) set of the items for each category you desire. If they are really distinct, you can get away with a small sample size.
You can use the DocumentCategorizerME.train() static function to train a collection of documents, where each requires a category tag and the text block to train on. Then, you can initialize the DocumentCategorizerME with the trained model and begin classifying all the rest of your documents.
Once you do this, you can (I think) write the model to a file so you don't have to ever do that again.
This post on extracting keywords and classifying webpages is related and may be helpful. In your example it sounds like you can use tags in lieu of the keyword extraction piece (although you may want to use both in combination). Weka is easy to use, I would definitely recommend giving it a look.