Python tokenization.FullTokenizer方法代碼示例

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def test_full_tokenizer(self):
        vocab_tokens = [
            "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
            "##ing", ","
        ]
        with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

            vocab_file = vocab_writer.name

        tokenizer = tokenization.FullTokenizer(vocab_file)
        os.unlink(vocab_file)

        tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
        self.assertAllEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])

        self.assertAllEqual(
            tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9]) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def __init__(self, id, args, worker_address, sink_address):
        super().__init__()
        self.model_dir = args.model_dir
        self.config_fp = os.path.join(self.model_dir, 'bert_config.json')
        self.checkpoint_fp = os.path.join(self.model_dir, 'bert_model.ckpt')
        self.vocab_fp = os.path.join(args.model_dir, 'vocab.txt')
        self.tokenizer = tokenization.FullTokenizer(vocab_file=self.vocab_fp)
        self.max_seq_len = args.max_seq_len
        self.worker_id = id
        self.daemon = True
        self.model_fn = model_fn_builder(
            bert_config=modeling.BertConfig.from_json_file(self.config_fp),
            init_checkpoint=self.checkpoint_fp,
            pooling_strategy=args.pooling_strategy,
            pooling_layer=args.pooling_layer
        )
        os.environ['CUDA_VISIBLE_DEVICES'] = str(self.worker_id)
        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        config.gpu_options.per_process_gpu_memory_fraction = args.gpu_memory_fraction
        self.estimator = Estimator(self.model_fn, config=RunConfig(session_config=config))
        self.exit_flag = multiprocessing.Event()
        self.logger = set_logger('WORKER-%d' % self.worker_id)
        self.worker_address = worker_address
        self.sink_address = sink_address 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def main():
    parser = argparse.ArgumentParser()

    parser.add_argument('--review-json-dir', type=str, default='../dat/PeerRead/arxiv.all/all/reviews')
    parser.add_argument('--parsedpdf-json-dir', type=str, default='../dat/PeerRead/arxiv.all/all/parsed_pdfs')
    parser.add_argument('--out-dir', type=str, default='../dat/PeerRead/proc')
    parser.add_argument('--out-file', type=str, default='arxiv-all.tf_record')
    parser.add_argument('--vocab-file', type=str, default='../../bert/pre-trained/uncased_L-12_H-768_A-12/vocab.txt')
    parser.add_argument('--max-abs-len', type=int, default=250)
    parser.add_argument('--venue', type=int, default=0)
    parser.add_argument('--year', type=int, default=2017)


    args = parser.parse_args()

    tokenizer = tokenization.FullTokenizer(
        vocab_file=args.vocab_file, do_lower_case=True)

    clean_PeerRead_dataset(args.review_json_dir, args.parsedpdf_json_dir,
                           args.venue, args.year,
                           args.out_dir, args.out_file,
                           args.max_abs_len, tokenizer, is_arxiv=True) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--data-dir', type=str, default=None)
    parser.add_argument('--out-dir', type=str, default='../dat/reddit')
    parser.add_argument('--out-file', type=str, default='proc.tf_record')
    parser.add_argument('--vocab-file', type=str, default='../../bert/pre-trained/uncased_L-12_H-768_A-12/vocab.txt')
    parser.add_argument('--max-abs-len', type=int, default=128)
    parser.add_argument('--subsample', type=int, default=0)
    parser.add_argument('--use-latest-reddit', type=bool, default=True)

    args = parser.parse_args()

    tokenizer = tokenization.FullTokenizer(
        vocab_file=args.vocab_file, do_lower_case=True)

    process_reddit_dataset(args.data_dir, args.out_dir, args.out_file,
                           args.max_abs_len, tokenizer, args.subsample, args.use_latest_reddit) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def __init__(self, model_fname="/notebooks/embedding/data/sentence-embeddings/bert/tune-ckpt",
                 bertconfig_fname="/notebooks/embedding/data/sentence-embeddings/bert/multi_cased_L-12_H-768_A-12/bert_config.json",
                 vocab_fname="/notebooks/embedding/data/sentence-embeddings/bert/multi_cased_L-12_H-768_A-12/vocab.txt",
                 max_seq_length=32, dimension=768, num_labels=2, use_notebook=False):

        super().__init__("bert", dimension, use_notebook)
        config = BertConfig.from_json_file(bertconfig_fname)
        self.max_seq_length = max_seq_length
        self.tokenizer = FullTokenizer(vocab_file=vocab_fname, do_lower_case=False)
        self.model, self.input_ids, self.input_mask, self.segment_ids, self.probs = make_bert_graph(config,
                                                                                                    max_seq_length,
                                                                                                    1.0,
                                                                                                    num_labels,
                                                                                                    tune=False)
        saver = tf.train.Saver(tf.global_variables())
        self.sess = tf.Session()
        checkpoint_path = tf.train.latest_checkpoint(model_fname)
        saver.restore(self.sess, checkpoint_path) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def get_bert(BERT_PT_PATH, bert_type, do_lower_case, no_pretraining):


    bert_config_file = os.path.join(BERT_PT_PATH, f'bert_config_{bert_type}.json')
    vocab_file = os.path.join(BERT_PT_PATH, f'vocab_{bert_type}.txt')
    init_checkpoint = os.path.join(BERT_PT_PATH, f'pytorch_model_{bert_type}.bin')



    bert_config = BertConfig.from_json_file(bert_config_file)
    tokenizer = tokenization.FullTokenizer(
        vocab_file=vocab_file, do_lower_case=do_lower_case)
    bert_config.print_status()

    model_bert = BertModel(bert_config)
    if no_pretraining:
        pass
    else:
        model_bert.load_state_dict(torch.load(init_checkpoint, map_location='cpu'))
        print("Load pre-trained parameters.")
    model_bert.to(device)

    return model_bert, tokenizer, bert_config 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def get_bert(BERT_PT_PATH, bert_type, do_lower_case, no_pretraining):
    bert_config_file = os.path.join(BERT_PT_PATH, f'bert_config_{bert_type}.json')
    vocab_file = os.path.join(BERT_PT_PATH, f'vocab_{bert_type}.txt')
    init_checkpoint = os.path.join(BERT_PT_PATH, f'pytorch_model_{bert_type}.bin')

    bert_config = BertConfig.from_json_file(bert_config_file)
    tokenizer = tokenization.FullTokenizer(
        vocab_file=vocab_file, do_lower_case=do_lower_case)
    bert_config.print_status()

    model_bert = BertModel(bert_config)
    if no_pretraining:
        pass
    else:
        model_bert.load_state_dict(torch.load(init_checkpoint, map_location='cpu'))
        print("Load pre-trained parameters.")
    model_bert.to(device)

    return model_bert, tokenizer, bert_config 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def __init__(self, label_map, vocab_file,
               max_seq_length, do_lower_case,
               converter):
    """Initializes an instance of BertExampleBuilder.

    Args:
      label_map: Mapping from tags to tag IDs.
      vocab_file: Path to BERT vocabulary file.
      max_seq_length: Maximum sequence length.
      do_lower_case: Whether to lower case the input text. Should be True for
        uncased models and False for cased models.
      converter: Converter from text targets to tags.
    """
    self._label_map = label_map
    self._tokenizer = tokenization.FullTokenizer(vocab_file,
                                                 do_lower_case=do_lower_case)
    self._max_seq_length = max_seq_length
    self._converter = converter
    self._pad_id = self._get_pad_id()
    self._keep_tag_id = self._label_map['KEEP'] 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def test_full_tokenizer(self):
    vocab_tokens = [
        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
        "##ing", ","
    ]
    with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
      if six.PY2:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
      else:
        vocab_writer.write("".join(
            [x + "\n" for x in vocab_tokens]).encode("utf-8"))

      vocab_file = vocab_writer.name

    tokenizer = tokenization.FullTokenizer(vocab_file)
    os.unlink(vocab_file)

    tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
    self.assertAllEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])

    self.assertAllEqual(
        tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9]) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def create_tokenizer_from_hub_module():
  """Get the vocab file and casing info from the Hub module."""
  with tf.Graph().as_default():
    bert_module = hub.Module(BERT_MODEL_HUB)
    tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
    with tf.Session() as sess:
      vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
                                            tokenization_info["do_lower_case"]])
      
  return FullTokenizer(
      vocab_file=vocab_file, do_lower_case=do_lower_case) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def __init__(self, vocab_file=None, **kwargs):
        super().__init__()
        if vocab_file is None:
            raise ValueError(
                'Vocabulary file is required to initialize BERT tokenizer'
            )

        try:
            from bert.tokenization import FullTokenizer
        except ImportError:
            raise ValueError(
                "Please install bert-tensorflow: pip install bert-tensorflow"
            )

        self.tokenizer = FullTokenizer(vocab_file) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def main(_):
  tf.logging.set_verbosity(tf.logging.INFO)

  tokenizer = tokenization.FullTokenizer(
      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

  input_files = []
  for input_pattern in FLAGS.input_file.split(","):
    input_files.extend(tf.gfile.Glob(input_pattern))

  tf.logging.info("*** Reading from input files ***")
  for input_file in input_files:
    tf.logging.info("  %s", input_file)

  rng = random.Random(FLAGS.random_seed)
  instances = create_training_instances(
      input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor,
      FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq,
      rng)

  output_files = FLAGS.output_file.split(",")
  tf.logging.info("*** Writing to output files ***")
  for output_file in output_files:
    tf.logging.info("  %s", output_file)

  write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length,
                                  FLAGS.max_predictions_per_seq, output_files) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def buzzy_title_based_sim_dfs(treat_strength, con_strength, noise_level, setting="simple", seed=0,
                            base_output_dir='../dat/sim/peerread_buzzytitle_based/'):

    labeler = make_buzzy_based_simulated_labeler(treat_strength, con_strength, noise_level, setting=setting, seed=seed)

    num_splits = 10
    dev_splits = [0]
    test_splits = [0]

    # data_file = '../dat/reddit/proc.tf_record'
    # vocab_file = "../../bert/pre-trained/uncased_L-12_H-768_A-12/vocab.txt"
    tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=True)

    input_dataset_from_filenames = make_input_fn_from_file(data_file,
                                                           250,
                                                           num_splits,
                                                           dev_splits,
                                                           test_splits,
                                                           tokenizer,
                                                           is_training=False,
                                                           filter_test=False,
                                                           shuffle_buffer_size=25000,
                                                           seed=seed,
                                                           labeler=labeler)

    output_df = dataset_fn_to_df(input_dataset_from_filenames)
    output_df = output_df.rename(index=str, columns={'theorem_referenced': 'treatment'})

    output_dir = os.path.join(base_output_dir, "mode{}".format(setting))
    os.makedirs(output_dir, exist_ok=True)
    output_path = os.path.join(output_dir, "beta0{}.beta1{}.gamma{}.tsv".format(treat_strength, con_strength, noise_level))

    output_df.to_csv(output_path, '\t') 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--datasets-dir', type=str, default='../dat/PeerRead')
    parser.add_argument('--vocab-file', type=str, default='../../bert/pre-trained/uncased_L-12_H-768_A-12/vocab.txt')
    args = parser.parse_args()

    datasets_dir = args.datasets_dir
    tokenizer = tokenization.FullTokenizer(
        vocab_file=args.vocab_file, do_lower_case=True)

    def proc_dataset(dataset):
        all_dir = os.path.join(datasets_dir, dataset_paths[dataset], 'all')
        review_json_dir = os.path.join(all_dir, 'reviews')
        parsedpdf_json_dir = os.path.join(all_dir, 'parsed_pdfs')

        venue = dataset_venues[dataset]
        year = dataset_years[dataset]

        out_dir = os.path.join(datasets_dir, 'proc')
        out_file = dataset + '.tf_record'
        max_abs_len = 250

        clean_PeerRead_dataset(review_json_dir, parsedpdf_json_dir, venue, year, out_dir, out_file, max_abs_len,
                               tokenizer)

    # pool = mp.Pool(4)
    # pool.map(proc_dataset, dataset_names)

    for dataset in dataset_names:
        proc_dataset(dataset) 

# 需要導入模塊: from bert import tokenization [as 別名]
# 或者: from bert.tokenization import FullTokenizer [as 別名]
def main():

    parser = argparse.ArgumentParser()
    parser.add_argument('--review-json-dir', type=str, default=None)
    parser.add_argument('--vocab-file', type=str, default=None)

    args = parser.parse_args()

    tokenizer = tokenization.FullTokenizer(
        vocab_file=args.vocab_file, do_lower_case=True)

    review_json_dir = args.review_json_dir

    print('Reading reviews from...', review_json_dir)
    paper_json_filenames = sorted(glob.glob('{}/*.json'.format(review_json_dir)))

    paper_json_filename = paper_json_filenames[0]
    with io.open(paper_json_filename) as json_file:
        loaded = json.load(json_file)
    abstract = loaded['abstract']
    print(abstract)
    tokens = tokenizer.tokenize(abstract)
    print(tokens)
    print(tokenizer.convert_tokens_to_ids(tokens))

    # for idx, paper_json_filename in enumerate(paper_json_filenames):
    #     with io.open(paper_json_filename) as json_file:
    #         loaded = json.load(json_file)
    #
    #     print(loaded['abstract'])