gensim-data | Data repository for pretrained NLP models and NLP corpora | Dataset library
kandi X-RAY | gensim-data Summary
kandi X-RAY | gensim-data Summary
Research datasets regularly disappear, change over time, become obsolete or come without a sane implementation to handle the data format reading and processing. For this reason, Gensim launched its own dataset storage, committed to long-term support, a sane standardized usage API and focused on datasets for unstructured text processing (no images or audio). This Gensim-data repository serves as that storage. There's no need for you to use this repository directly. Instead, simply install Gensim and use its download API (see the Quickstart below). It will "talk" to this repository automagically. When you use the Gensim download API, all data is stored in your ~/gensim-data home folder. Read more about the project rationale and design decisions in this article: New Download API for Pretrained NLP Models and Datasets.
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Trending Discussions on gensim-data
QUESTION
I'm currently trying to make a sentiment analysis on the IMDB review dataset as a part of homework assignment for my college, I'm required to firstly do some preprocessing e.g. : tokenization, stop words removal, stemming, lemmatization. then use different ways to convert this data to vectors to be classfied by different classfiers, Gensim FastText library was one of the required models to obtain word embeddings on the data I got from text pre-processing step.
the problem I faced with Gensim is that I firstly tried to train on my data using vectors of feature size (100,200,300) but yet they always fail at some point, I tried later to use many pre-trained Gensim data vectors, but none of them worked to find word embeddings for all of the words, they'd rather fail at some point with error
...ANSWER
Answered 2021-Dec-16 at 21:14If you train your own word-vector model, then it will contain vectors for all the words you told it to learn. If a word that was in your training data doesn't appear to have a vector, it likely did not appear the required min_count
number of times. (These models tend to improve if you discard rare words who few example usages may not be suitably-informative, so the default min_words=5
is a good idea.)
It's often reasonable for downstream tasks, like feature engineering using the text & set of word-vectors, to simply ignore words with no vector. That is, if some_rare_word in model.wv
is False
, just don't try to use that word – & its missing vector – for anything. So you don't necessarily need to find, or train, a set of word-vectors with every word you need. Just elide, rather than worry-about, the rare missing words.
Separate observations:
- Stemming/lemmatization & stop-word removal aren't always worth the trouble, with all corpora/algorithms/goals. (And, stemming/lemmatization may wind up creating pseudowords that limit the model's interpretability & easy application to any texts that don't go through identical preprocessing.) So if those are required parts of laerning exercise, sure, get some experience using them. But don't assume they're necessarily helping, or worth the extra time/complexity, unless you verify that rigrously.
- FastText models will also be able to supply synthetic vectors for words that aren't known to the model, based on substrings. These are often pretty weak, but may better than nothing - especially when they give vectors for typos, or rare infelcted forms, similar to morphologically-related known words. (Since this deduced similarity, from many similarly-written tokens, provides some of the same value as stemming/lemmatization via a different path that required the original variations to all be present during initial training, you'd especially want to pay attention to whether FastText & stemming/lemmatization mix well for your goals.) Beware, though: for very-short unknown words – for which the model learned no reusable substring vectors – FastText may still return an error or all-zeros vector.
- FastText has a
supervised
classification mode, but it's not supported by Gensim. If you want to experiment with that, you'd need to use the Facebook FastText implementation. (You could still use a traditional, non-supervised
FastText word vector model as a contributor of features for other possible representations.)
QUESTION
I am using Word2Vec and using a wiki trained model that gives out the most similar words. I ran this before and it worked but now it gives me this error even after rerunning the whole program. I tried to take off return_path=True
but im still getting the same error
ANSWER
Answered 2021-Aug-06 at 18:44You are probably looking for .wv.most_similar
, so please try:
QUESTION
Usually, I can use the following code to download the word vector package in jupyter lab:
...ANSWER
Answered 2020-Nov-18 at 18:36I would not use the gensim.downloader
facility at all, given the extra complexity/hidden-steps it introduces (which include what I consider an unnecessary security risk of downloading & running extra 'shim' Python code that's not in the normal Gensim release).
Instead, find the plain dataset you want, download it to somewhere you can, then use whatever other method you have for transferring files to your firewalled Windows Server.
Specifically, the 50d GLoVe vectors appear to be included as part of the glove.6B.zip
download available on the canonical GLoVe home page:
QUESTION
I am trying to understand what is going wrong in the following example.
To train on the 'text8' dataset as described in the docs, one only has to do the following:
...ANSWER
Answered 2020-Jun-23 at 21:09In your second example, you've created a training dataset with just a single text with the entire contents of the file. That's about 1.1 million word tokens, in a single list.
Word2Vec
(& other related algorithms) in gensim have an internal implementation limitation, in their optimized paths, of 10,000 tokens per text item. All additional tokens are ignored.
So, in your 2nd case, 99% of your data is being discarded. Training may seem instant, but very little actual training will have occurred. (Word-vectors for words that only appear past the 1st 10,000 tokens won't have been trained at all, having only their initial randomly-set values.) If you enable logging at the INFO level, you'll see more details about each step of the process, and discrepancies like this may be easier to identify.
Yes, the api.load()
variant takes extra steps to break the single-line-file into 10,000-token chunks. I believe it's using the LineSentence
utility class for this purpose, whose source can be examined here:
However, I recommend avoiding the api.load()
functionality entirely. It doesn't just download data; it also downloads a shim of additional outside-of-version-control Python code for prepping that data for extra operations. Such code is harder to browse & less well-reviewed than official gensim release code as packaged for PyPI/etc, which also presents a security risk. Each load target (by name like 'text8') might do something different, leaving you with a different object type as the return value.
It's much better for understanding to directly download precisely the data files you need, to known local paths, and do the IO/prep yourself, from those paths, so you know what steps have been applied, and the only code you're running is the officially versioned & released code.
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Install gensim-data
Python API Example: load a pre-trained model (gloVe word vectors): import gensim.downloader as api info = api.info() # show info about available models/datasets model = api.load("glove-twitter-25") # download the model and return as object ready for use model.most_similar("cat") """ output: [(u'dog', 0.9590819478034973), (u'monkey', 0.9203578233718872), (u'bear', 0.9143137335777283), (u'pet', 0.9108031392097473), (u'girl', 0.8880630135536194), (u'horse', 0.8872727155685425), (u'kitty', 0.8870542049407959), (u'puppy', 0.886769711971283), (u'hot', 0.8865255117416382), (u'lady', 0.8845518827438354)] """ Example: load a corpus and use it to train a Word2Vec model: from gensim.models.word2vec import Word2Vec import gensim.downloader as api corpus = api.load('text8') # download the corpus and return it opened as an iterable model = Word2Vec(corpus) # train a model from the corpus model.most_similar("car") """ output: [(u'driver', 0.8273754119873047), (u'motorcycle', 0.769528865814209), (u'cars', 0.7356342077255249), (u'truck', 0.7331641912460327), (u'taxi', 0.718338131904602), (u'vehicle', 0.7177008390426636), (u'racing', 0.6697118878364563), (u'automobile', 0.6657308340072632), (u'passenger', 0.6377975344657898), (u'glider', 0.6374964714050293)] """ Example: only download a dataset and return the local file path (no opening): import gensim.downloader as api print(api.load("20-newsgroups", return_path=True)) # output: /home/user/gensim-data/20-newsgroups/20-newsgroups.gz print(api.load("glove-twitter-25", return_path=True)) # output: /home/user/gensim-data/glove-twitter-25/glove-twitter-25.gz
The same operations, but from CLI, command line interface: python -m gensim.downloader --info # show info about available models/datasets python -m gensim.downloader --download text8 # download text8 dataset to ~/gensim-data/text8 python -m gensim.downloader --download glove-twitter-25 # download model to ~/gensim-data/glove-twitter-50/
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