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Nevermind, the solution was trivial. Since I had the .bin file I could just open it in binary form. If somebody doesn't really have the .bin file, they could consider converting the .txt file to .bin and solve further.


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You may encode each level and concatenate - If we ignore the path till file1 as its same across all the names. Then we need 1 digit for subfile and 2 digit for targetfile c:/users/file1/subfile1/targetfile_0 - [0 00] c:/users/file1/subfile1/targetfile_1 - [0 01] c:/users/file1/subfile1/targetfile_2 - [0 10] c:/users/file1/subfile2/targetfile_0 - [1 00] ...


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Small addition, as this was still not mentioned, and I was also searching for it. There is an explanation here: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html for the drop parameter: drop{‘first’, ‘if_binary’} or a array-like of shape (n_features,), default=None Specifies a methodology to use to drop one of the ...


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ELMo does not lookup the embeddings from a pre-precomputed table as Word2Vec and GloVe. Embeddings from ELMo are hidden states of an LSTM-based language model, i.e., they are computed on the fly when you give a sentence to the network. ELMo even does use standard word embeddings as the LSTM input. Words are treated as character sequences and those are ...


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col = [] for c in df.columns: if df[c].dtypes=='object': col.append(c) df_dummies = pd.get_dummies(df , columns=col, drop_first=True) ## get dummies part It is a good practice to use the drop_first parameter as it would avoid the model getting overfitted.


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As the error says: there is no categorical_values parameter for OneHotEncoder. It was removed at the same time that OneHotEncoder was extended to deal with strings directly, and you may want to use ColumnTransformer for selecting out the categorical column(s). For example, https://datascience.stackexchange.com/a/57383/55122


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Its easier to perform this by method get_dummies: X_enc = pd.get_dummies(X) Reference: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html


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When you concatenate, you have to define a priori the size of each vector to be concatenated. This means that, if we were to concatenate the token embedding and the positional embedding, we would have to define two dimensionalities, $d_t$ for the token and $d_p$ for the position, with the total dimensionality $d = d_t + d_p$, so $d>d_t$ and $d>d_p$. We ...


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So the question is about why positional embeddings are directly added to word embeddings instead of concatenated. This is a particularly interesting question. To answer this question, I will need to firstly separate the differences between sequential networks like RNNs and Transformers, which then introduces this problem nicely. In RNNs, we feed in data (let'...


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