Using different word embbedings from other sources and applying them on other other HF models
Context: For what I’ve gathered, we usually transform our sentences into tokens which will then generate a word embbeding and feed into the model (and this model has been pre-trained on a given corpus such as BERT word embbedings) as shown in this schema:image1839×748 25.4 KBNow, what if we want to use different word embbedings coming from other sources? For example, I’m trying to do sentiment analysis with 3 labels (positive, neutral, negative) in several languages and for that I may want to use a “glove.840B.300d.txt” or multi language from fasttext (such as this oneWord vectors for 157 languages · fastText) which may not have an associated model in the HF hubWithout transformers, I’d do something like this to create a embbedding matrix:# load the pre-trained word-embedding vectors 

embeddings_index = {}

for i, line in enumerate(open('glove.840B.300d.txt', encoding="utf8")):

    values = line.split()

    try:

      embeddings_index[values[0]] = np.asarray(values[1:], dtype='float32')

    except:

      continue

# create a tokenizer 

token = Tokenizer(num_words = 10000, oov_token="<OOV>") #out of vocabulary tokens will be represented as "1" in the ..._seq_x variables. index 0 is reserved for padding and index 1 is reserved for "out of vocabulary" tokens.

token.fit_on_texts(df3[colToUse]) #The fit_on_texts function is used to fit the Tokenizer on the training set once it has been instantiated with the preferred parameters. Required before using texts_to_sequences

word_index = token.word_index #The word_index can be used to show the mapping of the words to numbers. Each word will have a number

# convert text to sequence of tokens and pad them to ensure equal length vectors  -> Each sub-list is a row in the dataframe encoded. This is done by texts_to_sequences

train_seq_x = sequence.pad_sequences(token.texts_to_sequences(train_x), maxlen=248)

valid_seq_x = sequence.pad_sequences(token.texts_to_sequences(valid_x), maxlen=248)

# create token-embedding mapping

embedding_matrix = np.zeros((len(word_index) + 1, 300)) #initiate the matrix with all 0s. This will be later on updated with the value for each word

for word, i in word_index.items():

    embedding_vector = embeddings_index.get(word)

    if embedding_vector is not None:

        embedding_matrix[i] = embedding_vectorWhich would yield a training sequence (padded) like this:array([[   0,    0,    0,    0,    ...,
           0,    0,    0,    0,   38,  352, 5391,    2,   56,  352,  316,
         343,  436,  195,  285,   30,   18]], dtype=int32)Is it possible then to pass different embbeding matrices (coming from different word embbedings) to different models? For example, passing an embbeding matrix as input coming from the multilanguage fasttext (possibly using the AutoTokenizer??) and using themodel = AutoModelForSequenceClassification.from_pretrained(tokenizer.tokenize(sequence))with a bert-uncased for example?Thank you in advance (I’m really new to all this)