def load_sentences(file, skip_first=True, single_sentence=False):
    """ Loads sentences into process-friendly format for a given file path.
    Inputs
    -------------------
    file    - str or pathlib.Path. The file path which needs to be processed
    skip_first      - bool. If True, skips the first line.
    single_sentence - bool. If True, Only the hypothesis statement is chosen. 
                            Else, both the premise and hypothesis statements are 
                            considered. This is useful for hypothesis bias experiments.

    Outputs
    --------------------
    rows    - List[dict]. Consists of all data samples. Each data sample is a 
                    dictionary containing- uid, hypothesis, premise (except hypothesis
                    bias experiment), and the NLI label for the pair
    """
    rows = []
    with open(file, encoding="ISO-8859-1") as f:
        for line in f:
            if skip_first:
                skip_first = False
                continue
            blocks = line.strip().split('\t')   # Converts to list
            
            # Takes the relevant elements of the row necessary. Putting them in a dict,
            lab = int(blocks[-1])
            if single_sentence:
                sample = {'uid': int(blocks[0]), 'hypothesis': blocks[4], 'label': lab}
            else:   
                sample = {'uid': int(blocks[0]), 'premise': blocks[3], 'hypothesis': blocks[4], 'label': lab}
            rows.append(sample) # Append the loaded sample
    return rows