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Implementation of visdata v2
This commit is contained in:
Patrick Jentsch
parent
e6d8d72e52
commit
972f514e6b
@ -49,62 +49,131 @@ def cqi_corpora_corpus_update_db(cqi_client: cqi.CQiClient, corpus_name: str):
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@socketio_login_required
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@cqi_over_socketio
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def cqi_corpora_corpus_get_visualization_data(cqi_client: cqi.CQiClient, corpus_name: str):
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cqi_corpus = cqi_client.corpora.get(corpus_name)
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corpus = cqi_client.corpora.get(corpus_name)
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text = corpus.structural_attributes.get('text')
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s = corpus.structural_attributes.get('s')
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ent = corpus.structural_attributes.get('ent')
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word = corpus.positional_attributes.get('word')
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lemma = corpus.positional_attributes.get('lemma')
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pos = corpus.positional_attributes.get('pos')
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simple_pos = corpus.positional_attributes.get('simple_pos')
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payload = {}
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payload['num_tokens'] = cqi_corpus.size
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cqi_word_attr = cqi_corpus.positional_attributes.get('word')
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payload['num_unique_words'] = cqi_word_attr.lexicon_size
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payload['word_freqs'] = dict(zip(cqi_word_attr.values_by_ids(list(range(0, cqi_word_attr.lexicon_size))), cqi_word_attr.freqs_by_ids(list(range(0, cqi_word_attr.lexicon_size)))))
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# payload['word_freqs'].sort(key=lambda a: a[1], reverse=True)
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# payload['word_freqs'] = {k: v for k, v in payload['word_freqs']}
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cqi_lemma_attr = cqi_corpus.positional_attributes.get('lemma')
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payload['num_unique_lemmas'] = cqi_lemma_attr.lexicon_size
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payload['lemma_freqs'] = dict(zip(cqi_lemma_attr.values_by_ids(list(range(0, cqi_lemma_attr.lexicon_size))), cqi_lemma_attr.freqs_by_ids(list(range(0, cqi_lemma_attr.lexicon_size)))))
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# payload['lemma_freqs'].sort(key=lambda a: a[1], reverse=True)
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# payload['lemma_freqs'] = {k: v for k, v in payload['lemma_freqs']}
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cqi_s_attr = cqi_corpus.structural_attributes.get('s')
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payload['num_sentences'] = cqi_s_attr.size
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# assuming all tokens are in a sentence
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payload['average_sentence_length'] = payload['num_tokens'] / payload['num_sentences'] if payload['num_sentences'] != 0 else 0
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# payload['average_sentence_length'] = 0
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# for s_id in range(0, cqi_s_attr.size):
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# s_lbound, s_rbound = cqi_s_attr.cpos_by_id(s_id)
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# payload['average_sentence_length'] += s_rbound - s_lbound + 1
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# payload['average_sentence_length'] /= payload['num_sentences']
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cqi_ent_type_attr = cqi_corpus.structural_attributes.get('ent_type')
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payload['num_ent_types'] = cqi_ent_type_attr.size
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payload['ent_type_freqs'] = dict(Counter(cqi_ent_type_attr.values_by_ids(list(range(0, cqi_ent_type_attr.size)))))
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payload['num_unique_ent_types'] = len(payload['ent_type_freqs'])
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payload['texts'] = []
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cqi_text_attr = cqi_corpus.structural_attributes.get('text')
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for text_id in range(0, cqi_text_attr.size):
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text_lbound, text_rbound = cqi_text_attr.cpos_by_id(text_id)
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text_cpos_list = list(range(text_lbound, text_rbound + 1))
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text_payload = {}
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text_payload['num_tokens'] = text_rbound - text_lbound + 1
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text_word_ids = cqi_word_attr.ids_by_cpos(text_cpos_list)
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print(text_word_ids)
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text_payload['num_unique_words'] = len(set(text_word_ids))
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text_payload['word_freqs'] = dict(Counter(cqi_word_attr.values_by_ids(text_word_ids)))
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text_lemma_ids = cqi_lemma_attr.ids_by_cpos(text_cpos_list)
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text_payload['num_unique_lemmas'] = len(set(text_lemma_ids))
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text_payload['lemma_freqs'] = dict(Counter(cqi_word_attr.values_by_ids(text_lemma_ids)))
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text_s_attr_ids = list(filter(lambda x: x != -1, cqi_s_attr.ids_by_cpos(text_cpos_list)))
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text_payload['num_sentences'] = len(set(text_s_attr_ids))
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# assuming all tokens are in a sentence
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text_payload['average_sentence_length'] = text_payload['num_tokens'] / text_payload['num_sentences'] if text_payload['num_sentences'] != 0 else 0
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# text_payload['average_sentence_length'] = 0
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# for text_s_id in range(0, cqi_s_attr.size):
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# text_s_lbound, text_s_rbound = cqi_s_attr.cpos_by_id(text_s_id)
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# text_payload['average_sentence_length'] += text_s_rbound - text_s_lbound + 1
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# text_payload['average_sentence_length'] /= text_payload['num_sentences']
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text_ent_type_ids = list(filter(lambda x: x != -1, cqi_ent_type_attr.ids_by_cpos(text_cpos_list)))
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text_payload['num_ent_types'] = len(set(text_ent_type_ids))
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text_payload['ent_type_freqs'] = dict(Counter(cqi_ent_type_attr.values_by_ids(text_ent_type_ids)))
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text_payload['num_unique_ent_types'] = len(text_payload['ent_type_freqs'])
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for text_sub_attr in cqi_corpus.structural_attributes.list(filters={'part_of': cqi_text_attr}):
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text_payload[text_sub_attr.name[(len(cqi_text_attr.name) + 1):]] = text_sub_attr.values_by_ids([text_id])[0]
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payload['texts'].append(text_payload)
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payload['corpus'] = {'lexicon': {}, 'values': []}
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payload['corpus']['lexicon'][0] = {
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'bounds': [0, corpus.size - 1],
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'counts': {
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'text': text.size,
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's': s.size,
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'ent': ent.size,
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'token': corpus.size
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},
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'freqs': {
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'word': dict(
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zip(
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range(0, word.lexicon_size),
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word.freqs_by_ids(list(range(0, word.lexicon_size)))
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)
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),
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'lemma': dict(
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zip(
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range(0, lemma.lexicon_size),
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lemma.freqs_by_ids(list(range(0, lemma.lexicon_size)))
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)
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),
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'pos': dict(
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zip(
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range(0, pos.lexicon_size),
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pos.freqs_by_ids(list(range(0, pos.lexicon_size)))
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)
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),
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'simple_pos': dict(
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zip(
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range(0, simple_pos.lexicon_size),
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simple_pos.freqs_by_ids(list(range(0, simple_pos.lexicon_size)))
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)
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)
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}
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}
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payload['text'] = {'lexicon': {}, 'values': None}
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for text_id in range(0, text.size):
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text_lbound, text_rbound = text.cpos_by_id(text_id)
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text_cpos_range = range(text_lbound, text_rbound + 1)
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text_s_ids = s.ids_by_cpos(list(text_cpos_range))
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text_ent_ids = ent.ids_by_cpos(list(text_cpos_range))
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payload['text']['lexicon'][text_id] = {
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'bounds': [text_lbound, text_rbound],
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'counts': {
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's': len([x for x in text_s_ids if x != -1]),
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'ent': len([x for x in text_ent_ids if x != -1]),
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'token': text_rbound - text_lbound + 1
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},
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'freqs': {
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'word': dict(
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Counter(word.ids_by_cpos(list(text_cpos_range)))
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),
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'lemma': dict(
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Counter(lemma.ids_by_cpos(list(text_cpos_range)))
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),
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'pos': dict(
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Counter(pos.ids_by_cpos(list(text_cpos_range)))
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),
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'simple_pos': dict(
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Counter(simple_pos.ids_by_cpos(list(text_cpos_range)))
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)
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}
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}
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payload['text']['values'] = [
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sub_attr.name[(len(text.name) + 1):]
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for sub_attr in corpus.structural_attributes.list(filters={'part_of': text})
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]
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payload['s'] = {'lexicon': {}, 'values': None}
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for s_id in range(0, s.size):
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payload['s']['lexicon'][s_id] = {
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# 'bounds': s.cpos_by_id(s_id)
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}
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payload['s']['values'] = [
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sub_attr.name[(len(s.name) + 1):]
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for sub_attr in corpus.structural_attributes.list(filters={'part_of': s})
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]
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payload['ent'] = {'lexicon': {}, 'values': None}
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for ent_id in range(0, ent.size):
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payload['ent']['lexicon'][ent_id] = {
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# 'bounds': ent.cpos_by_id(ent_id)
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}
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payload['ent']['values'] = [
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sub_attr.name[(len(ent.name) + 1):]
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for sub_attr in corpus.structural_attributes.list(filters={'part_of': ent})
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]
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payload['lookups'] = {
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'corpus': {},
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'text': {},
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's': {},
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'ent': {},
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'word': dict(
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zip(
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range(0, word.lexicon_size),
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word.values_by_ids(list(range(0, word.lexicon_size)))
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)
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),
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'lemma': dict(
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zip(
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range(0, lemma.lexicon_size),
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lemma.values_by_ids(list(range(0, lemma.lexicon_size)))
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)
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),
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'pos': dict(
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zip(
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range(0, pos.lexicon_size),
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pos.values_by_ids(list(range(0, pos.lexicon_size)))
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)
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),
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'simple_pos': dict(
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zip(
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range(0, simple_pos.lexicon_size),
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simple_pos.values_by_ids(list(range(0, simple_pos.lexicon_size)))
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)
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)
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}
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# print(payload)
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return {'code': 200, 'msg': 'OK', 'payload': payload}
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