def evaluate_srl_2_steps(no_repeat=False, find_preds_automatically=False, gold_file=None):
    # load boundary identification network and reader 
    md_boundary = Metadata.load_from_file('srl_boundary')
    nn_boundary = taggers.load_network(md_boundary)
    reader_boundary = taggers.create_reader(md_boundary, gold_file)
    itd_boundary = reader_boundary.get_inverse_tag_dictionary()
    
    # same for arg classification
    md_classify = Metadata.load_from_file('srl_classify')
    nn_classify = taggers.load_network(md_classify)
    reader_classify = taggers.create_reader(md_classify, gold_file)
    itd_classify = reader_classify.get_inverse_tag_dictionary()
    
    if find_preds_automatically:
        tagger = taggers.SRLTagger()
    else:
        iter_predicates = iter(reader_boundary.predicates)
    
    actual_sentences = [actual_sentence for actual_sentence, _ in reader_boundary.sentences]
    
    for sent in actual_sentences:
        
        if find_preds_automatically:
            pred_pos = tagger.find_predicates(sent)
        else:
            pred_pos = iter_predicates.next()
        
        verbs = [(position, sent[position].word) for position in pred_pos]
        sent_bound_codified = np.array([reader_boundary.converter.convert(t) for t in sent])
        sent_class_codified = np.array([reader_classify.converter.convert(t) for t in sent])
        
        answers = nn_boundary.tag_sentence(sent_bound_codified, pred_pos)
        boundaries = [[itd_boundary[x] for x in pred_answer] for pred_answer in answers]
        
        arg_limits = [utils.boundaries_to_arg_limits(pred_boundaries) 
                      for pred_boundaries in boundaries]
        
        answers = nn_classify.tag_sentence(sent_class_codified, 
                                           pred_pos, arg_limits,
                                           allow_repeats=not no_repeat)
        
        arguments = [[itd_classify[x] for x in pred_answer] for pred_answer in answers]        
        tags = join_2_steps(boundaries, arguments)        
        
        print(prop_conll(verbs, tags, len(sent)))
Prints the output of a 2-step SRL system in CoNLL style for evaluating.
dataset/ETHPy150Open erickrf/nlpnet/bin/nlpnet-test.py/evaluate_srl_2_steps