I'm trying to translate a long sentence from English to Spanish using Huggingface's transformers model on translation task. And often the models have a max limit of input text length. However, I'd like the model to see as many previous sentences as possible so that it wisely picks the translation words. How could I do that using the transformers model?
I am using gensim to train a word2vec model. The problem is that my data is very large (about 10 million documents) so my session is crashing when I try to estimate the model.
Note that I am able to load all the data at once in the RAM in a Pandas dataframe df, which looks like:
text id
long long text 1
another long one 2
... ...
My simple approach is to do the following:
tokens = df['text'].str.split(r'[\s]+')
model = Word2Vec(tokens, min_count = 50)
However, my session crashed when it tries to create the tokens all at once. Is there a better way to proceed in gensim? Like feeding the data line by line?
Thanks!
Iterate over your dataframe row by row, tokenizing just one row at a time. Write each tokenized text to a file in turn, with spaces between the tokens, and a line-end at the end of each text.
You can then use the LineSentence utility class in Gensim to provide a read-from-disk iterable corpus to the Word2Vec model.
I've been searching for an answer to this specific question for a few hours and while I've learned a lot, I still haven't figured it out.
I have a dataset of ~70,000 sentences with subset of about 4,000 sentences that have been appropriately categorized, the rest are uncategorized. Currently I'm using a scikit pipeline with CountVectorizer and TfidfTransformer to vectorize the data, however I'm only vectorizing based off the 4,000 sentences and then testing various models via cross-validation.
I'm wondering if there is a way to use Word2Vec or something similar to vectorize the entire corpus of data and then use these vectors with my subset of 4,000 sentences. My intention is to increase the accuracy of my model predictions by using word vectors that incorporate all of the semantic data in the corpus rather than just data from the 4,000 sentences.
The code I'm currently using is:
svc = Pipeline([('vect', CountVectorizer(ngram_range=(3, 5))),
('tfidf', TfidfTransformer()),
('clf', LinearSVC()),
])
nb.fit(X_train, y_train)
y_pred = svc.predict(X_test)
Where X_train and y_train are my features and labels, respectively. I also have a list z_all which includes all remaining uncategorized features.
Just getting pointed in the right direction (or told whether or not this is possible) would be super helpful.
Thank you!
I would say that the answer is yes: you can use Word2Vec or another similar word-embedding method to get vectors of each sentence in your data, and then use these vectors both as training and testing data in a linear Support Vector Machine (SVC).
And yes, you can first create those vectors for your entire corpus of ~70,000 sentences before actually doing any training on your data.
It is however not as straightforward as the approach you're currently using.
There are many different ways to do this so I'll just go through one of them to help you get the basics of how this can be done.
Before we start and see what possible steps you can follow, let's remember that the goal here is to get one vector for each and every sentence of your corpus.
If you don't know what word-embeddings are, I highly suggest you to read about it, but in short this is just a way to link each word of a pre-defined vocabulary to a vector of a given dimension.
For instance, you would have:
# the vector associated with the word "cat" is the following vector of fixed-length
word_embeddings["cat"] = [0.0014, 0.6710, ..., 0.3281]
Now that you know this, here are the steps you could be following:
Tokenization - The first thing that you want to do is to tokenize each of your sentences. This can be done using a NLP library (SpaCy for instance) that will help you to:
split each sentence in a list of words
remove any punctuation from these words and converting them to lowercase
remove stopwords - optionally
lemmatize all the words - optionally
Train a word embedding model - Now that you have each sentence as a pre-processed list of words, you need to train a word-embedding model using your corpus. There are many different algorithms to do that. I would suggest using GenSim and Word2Vec or fastText. What you can also do is using pre-trained word embeddings, like GloVe or anything that best fits your corpus in terms of language/context. Either way, this will allow you to:
have one vector of pre-defined size for each and every word in your corpus' vocabulary
get a list of equally-sized vectors for each sentence in your corpus
Adopting a weighting method - Once you have a list of vectors for each sentence in your corpus, and mainly because your sentences vary in length (some have 6 words, some others have 13 words, etc.) what you want to do is getting a single vector for each and every sentence. To do this, what you can do is simply weighting the vectors corresponding to the words in each sentence. You can:
average all vectors
using weights like TF-IDF weights to give some words more importance than others
use other weighting methods...
Training and testing - Finally, all you're left to do is training a model using these vectors, for instance with a linear Support Vector Machine (SVC), and testing the accuracy of your model on a test dataset (you can also use a validation dataset).
My opinion is, if you are going to use a word2vec embedding, use one pre-trained or used generic text to generate it.
Word2vec embedding are usually used to give meaning and context to your text data, if you train an embedding using only your data, it might be biased and not represent a language. And that means it vectors doesn't carry any meaning.
After having your embedding working, you also has to think about what to do with your words, because a sentence has 1 or more words (embedding works at word level), and you want to feed your models with 1 sentence -> 1 vector. not 1 sentences -> N vectors.
People usually average or multiply those vectors so for example, for the sentence "Hello there" and an embedding of 5 dims:
Hello -> [0, 0, .2, .3, .8]
there -> [.1, .2, 0, 0, .5]
AVG Hello there -> [.05, .1, .1, .15, .65]
This is what you want to use for your models!
So instead of using TF-IDF to generate your sentence vectors, use word2vec like this and you shouldn't have any problem. I already work in a text calssification project and we ended usind a self-trained w2v embedding an ExtraTrees model with amazing results.
How to get most frequent context words from pretrained fasttext model?
For example:
For word 'football' and corpus ["I like playing football with my friends"]
Get list of context words: ['playing', 'with','my','like']
I try to use
model_wiki = gensim.models.KeyedVectors.load_word2vec_format("wiki.ru.vec")
model.most_similar("блок")
But it's not satisfied for me
The plain model doesn't retain any such co-occurrence statistics from the original corpus. It just has the trained results: vectors per word.
So, the ranked list of most_similar() vectors – which isn't exactly words that appeared-together, but strongly correlates to that – is the best you'll get from that file.
Only going back to the original training corpus would give you exactly what you've requested.
I'm looking for a way to dinamically add pre-trained word vectors to a word2vec gensim model.
I have a pre-trained word2vec model in a txt (words and their embedding) and I need to get Word Mover's Distance (for example via gensim.models.Word2Vec.wmdistance) between documents in a specific corpus and a new document.
To prevent the need to load the whole vocabulary, I would want to load only the subset of the pre-trained model's words that are found in the corpus. But if the new document has words that are not found in the corpus but they are in the original model vocabulary add them to the model so they are considered in the computation.
What I want is to save RAM, so possible things that would help me:
Is there a way to add the word vectors directly to the model?
Is there a way to load to gensim from a matrix or another object? I could have that object in RAM and append to it the new words before loading them in the model
I don't need it to be on gensim, so if you know a different implementation for WMD that gets the vectors as input that would work (though I do need it in Python)
Thanks in advance.
METHOD 1:
You can just use keyedvectors from gensim.models.keyedvectors. They are very easy to use.
from gensim.models.keyedvectors import WordEmbeddingsKeyedVectors
w2v = WordEmbeddingsKeyedVectors(50) # 50 = vec length
w2v.add(new_words, their_new_vecs)
METHOD 2:
AND if you already have built a model using gensim.models.Word2Vec you can just do this. suppose I want to add the token <UKN> with a random vector.
model.wv["<UNK>"] = np.random.rand(100) # 100 is the vectors length
The complete example would be like this:
import numpy as np
import gensim.downloader as api
from gensim.models import Word2Vec
dataset = api.load("text8") # load dataset as iterable
model = Word2Vec(dataset)
model.wv["<UNK>"] = np.random.rand(100)