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I am working with ArSarcasm dataset from Hugging Face. I have cleaned the tweets from all the noise with a few preprocessing steps, tokenized the tweets and lemmatized the tokens (snippet of code can be provided upon request, but I don't think it is necessary at the moment). I decided to use a pre-trained distributed word embedding AraVec model. I downloaded a model from AraVec repository and exported it to a Word2Vec format, as it was suggested here https://github.com/bakrianoo/aravec/blob/master/aravec-with-spacy.ipynb. Afterwards, I initialized a spaCy model using the AraVec vectors, as in instructions:

# load AraVec Spacy model
nlp = spacy.load("./spacy.aravec.model/")

# Define the preprocessing Class
class Preprocessor:
    def __init__(self, tokenizer, **cfg):
        self.tokenizer = tokenizer

    def __call__(self, text):
        preprocessed = text_preprocessing_no_emojis(text)
        return self.tokenizer(preprocessed)

# Apply the `Preprocessor` Class
nlp.tokenizer = Preprocessor(nlp.tokenizer)

Then, I applied the nlp model to my dataset to obtain word embeddings:

df_train['aravec_tweet'] = df_train['lemmatized_tokens'].apply(lambda x: [nlp(word).vector for word in x])
df_test['aravec_tweet'] = df_test['lemmatized_tokens'].apply(lambda x: [nlp(word).vector for word in x])

At the end I obtained a vector of size 300x1 for each word in a tweet, as follows:

array([ 1.18501878e+00,  2.02996850e+00,  3.57224345e-01, -8.89488980e-02,
    -1.69773567e+00,  1.61816865e-01,  3.65314364e-01,  3.32285970e-01,
     6.07847869e-02,  6.44849837e-01,  5.23011744e-01,  1.35321689e+00,
     2.95223260e+00,  1.92520380e+00, -8.02338600e-01, -2.16084048e-02,
     8.30635786e-01, -4.83143376e-03, -2.00588512e+00, -3.17294270e-01,
     6.75427735e-01,  1.38516799e-01,  2.06860399e+00, -9.68082845e-01,
     7.79255390e-01,  1.86405718e+00,  5.90481400e-01, -5.17130077e-01,
     5.64814329e-01,  2.91672885e-01,  1.49555743e+00,  5.99287271e-01,
    -3.51198292e+00, -2.89015722e+00, -2.16006613e+00,  3.37497056e-01,
    -7.74498105e-01,  7.57846594e-01, -1.43979740e+00,  1.55529416e+00,
    -1.35614383e+00,  1.18351865e+00,  1.66904783e+00,  1.09944201e+00,
     3.41281503e-01, -1.08024485e-01,  1.24287283e+00,  1.24132276e+00,
     1.01752388e+00, -9.19422030e-01, -1.17511563e-01,  1.45627785e+00,
     1.71857700e-01, -1.04888082e+00,  6.55263841e-01,  9.00007308e-01,
    -1.71516910e-01, -2.40407482e-01,  2.83514202e-01,  1.62543571e+00,
     7.76902795e-01, -5.51940084e-01, -7.00472414e-01,  3.14607352e-01,
     1.75362420e+00, -1.15076625e+00,  1.61424863e+00,  6.45062983e-01,
    -1.01377159e-01, -1.51060045e+00,  1.62624454e+00,  6.26824260e-01,
    -9.99889314e-01, -4.00414169e-01,  2.60122031e-01, -6.71754181e-01,
     1.09269333e+00, -2.16043258e+00, -1.90052998e+00,  2.34455442e+00,
     7.83860743e-01,  8.62264156e-01,  3.15464497e+00,  3.67914110e-01,
    -5.97500801e-01,  1.23887527e+00, -7.09300220e-01,  8.00884247e-01,
     9.90329862e-01,  3.69867206e-01,  1.32181954e+00,  5.59119821e-01,
    -2.49398813e-01,  6.63751364e-02, -3.88794661e-01, -1.80622078e-02,
     6.40985310e-01, -5.10499954e-01, -7.79359877e-01, -1.56341553e-01,
    -1.96792758e+00, -1.99348950e+00,  1.92497134e+00,  1.56886423e+00,
     5.55809379e-01, -4.74920779e-01,  4.62501854e-01,  3.68922591e-01,
    -2.53095657e-01,  1.04221879e-02, -1.19028711e+00, -4.35500890e-02,
     2.51273179e+00,  1.63365030e+00,  1.95849085e+00, -2.83527040e+00,
    -1.99665499e+00, -1.86731255e+00, -7.22154006e-02,  8.77141207e-02,
     1.13918841e-01,  6.17255569e-01, -8.10367703e-01, -5.34381159e-02,
    -4.25921947e-01, -2.43420792e+00, -3.41339946e+00,  8.57619107e-01,
    -3.25962961e-01, -2.05256224e+00,  2.72880644e-01, -7.77022362e-01,
     3.63010615e-02, -2.21492934e+00,  3.01347494e-01,  3.82316649e-01,
     1.73162863e-01,  1.02163148e+00,  1.61056495e+00, -1.45305604e-01,
     5.12216663e+00,  6.51066676e-02,  9.05446291e-01, -3.38285118e-01,
     4.70555633e-01, -6.37923717e-01, -1.01741903e-01,  1.28871962e-01,
     1.18350232e+00,  3.00627910e-02,  1.65252292e+00, -4.14045244e-01,
    -2.01923513e+00, -2.48064971e+00,  2.84098774e-01, -1.16428542e+00,
     3.45168173e-01, -8.27252567e-01,  9.41152871e-01,  6.09542549e-01,
    -1.70102626e-01, -8.87252331e-01,  8.09590936e-01,  1.28366721e+00,
    -1.15011132e+00, -3.12820268e+00, -8.83560479e-01, -1.01001954e+00,
    -7.95460999e-01, -1.08073461e+00,  1.07002878e+00, -2.86776686e+00,
    -1.30195844e+00,  6.71077371e-02, -1.48900378e+00,  6.80345178e-01,
     1.18722603e-01, -4.51215893e-01, -3.97531569e-01,  2.58355474e+00,
    -2.09001750e-02,  8.71567547e-01, -2.43087813e-01,  4.52688754e-01,
    -3.12238395e-01,  1.21787786e+00, -5.88869035e-01, -1.95281178e-01,
     2.55353069e+00, -2.89073050e-01,  1.23965073e+00, -5.52937329e-01,
     1.60986519e+00,  1.53671300e+00,  3.11231911e-01,  2.86276221e-01,
     4.58456427e-01,  1.43427002e+00, -2.11872384e-01,  3.25756222e-01,
     1.60850072e+00,  1.67616057e+00, -4.19651940e-02, -2.32964158e+00,
     1.20741332e+00, -1.19211912e+00,  4.32185978e-01, -1.00784719e+00,
    -1.72911775e+00, -1.38866842e+00,  8.83582830e-01, -7.84302413e-01,
    -1.37820935e+00, -9.40127611e-01,  1.59083283e+00,  6.98838413e-01,
     1.41477156e+00, -1.49352717e+00,  2.80335808e+00, -1.16071272e+00,
    -1.66782045e+00,  7.02325106e-01,  2.39623761e+00, -1.07484925e+00,
    -2.78331656e-02,  1.81383741e+00, -1.78831232e+00, -2.03968763e+00,
     1.11220455e+00,  2.40832996e+00, -1.43199801e+00,  3.21234393e+00,
    -2.10891843e+00,  1.52330995e+00,  4.66105849e-01, -2.53052622e-01,
     9.37901437e-01, -1.47750986e+00,  1.64100075e+00, -2.12111211e+00,
     1.42237413e+00,  3.70414466e-01, -1.95484972e+00, -2.89914966e-01,
     6.99954778e-02,  1.18943311e-01, -3.80559973e-02,  8.72039855e-01,
    -1.89078569e+00, -1.58324373e+00,  2.71625668e-01,  3.93420458e-03,
    -8.96333516e-01,  3.76043940e+00, -4.36416477e-01, -1.13790178e+00,
     8.24326515e-01, -9.73615527e-01,  9.23004985e-01, -4.98284608e-01,
     1.36028159e+00,  1.84094131e+00, -6.56110704e-01, -1.52525592e+00,
    -9.88076568e-01, -1.04155529e+00,  4.16077971e-01, -9.24946845e-01,
    -9.92394865e-01, -8.54468703e-01,  2.36839914e+00,  2.24374866e+00,
     4.13455702e-02,  8.78034115e-01, -7.94358671e-01, -1.41547933e-01,
    -5.89911103e-01,  2.08969876e-01, -5.16288638e-01,  9.75951910e-01,
     1.11161876e+00,  1.04077971e+00,  1.76900375e+00,  9.82571304e-01,
    -6.51893377e-01,  1.49017453e-01,  1.26838610e-01,  6.44093037e-01,
     2.89026052e-01, -1.53185844e+00, -2.47551250e+00,  3.87931049e-01,
    -2.04899359e+00,  9.07741070e-01,  6.57939970e-01,  1.65561843e+00,
    -3.40096615e-02, -4.71346706e-01,  1.89021623e+00, -6.24606490e-01],
   dtype=float32),

I am not sure if this is the correct way to produce word embeddings with AraVec model. And if it is, how this lists of vectors can be used for further text classification (e.g. in Logistic Regression, SVC, bi-LSTM, or Random Forest).

I will be very thankful for the guidance.

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