scripts don't work.

issue#1

command : ./download_pretrained.sh bert-base error : HTTP request sent, awaiting response… 404 Not Found

---

issue#2

command : GPU=0 python train.py best error : Traceback (most recent call last): File “train.py”, line 22, in <module> model = util.get_model(config) File “/data/BERT-coref/coref/util.py”, line 21, in get_model return independent.CorefModel(config) File “/data/BERT-coref/coref/independent.py”, line 32, in init self.bert_config = modeling.BertConfig.from_json_file(config[“bert_config_file”]) File “/home/fairy_of_9/anaconda3/envs/bert/lib/python3.6/site-packages/pyhocon/config_tree.py”, line 366, in getitem val = self.get(item) File “/home/fairy_of_9/anaconda3/envs/bert/lib/python3.6/site-packages/pyhocon/config_tree.py”, line 209, in get return self._get(ConfigTree.parse_key(key), 0, default) File “/home/fairy_of_9/anaconda3/envs/bert/lib/python3.6/site-packages/pyhocon/config_tree.py”, line 151, in _get raise ConfigMissingException(u"No configuration setting found for key {key}".format(key=‘.’.join(key_path[:key_index + 1]))) pyhocon.exceptions.ConfigMissingException: ‘No configuration setting found for key bert_config_file’

---

experiments.conf

best {
  # Edit this
  data_dir = data_set
  model_type = independent
  # Computation limits.
  max_top_antecedents = 50
  max_training_sentences = 5
  top_span_ratio = 0.4
  max_num_speakers = 20
  max_segment_len = 64 #256

  # Learning
  bert_learning_rate = 1e-5
  task_learning_rate = 2e-4
  num_docs = 2802

  # Model hyperparameters.
  dropout_rate = 0.3
  ffnn_size = 500 #1000
  ffnn_depth = 1
  num_epochs = 20
  feature_size = 20
  max_span_width = 30
  use_metadata = true
  use_features = true
  use_segment_distance = true
  model_heads = false #true
  coref_depth = 2
  coarse_to_fine = true
  fine_grained = true
  use_prior = true

  # Other.
  train_path = data_set/train.english.jsonlines
  eval_path = data_set/dev.english.jsonlines
  conll_eval_path = data_set/dev.english.v4_gold_conll
  single_example = true
  genres = ["bc", "bn", "mz", "nw", "pt", "tc", "wb"]
  eval_frequency = 1000
  report_frequency = 100
  log_root = logs
  adam_eps = 1e-6
  task_optimizer = adam
}

bert_base = ${best}{
  num_docs = 2802
  bert_learning_rate = 1e-05
  task_learning_rate = 0.0002
  max_segment_len = 128
  ffnn_size = 3000
  train_path = data_set/train.english.128.jsonlines
  eval_path = data_set/dev.english.128.jsonlines
  conll_eval_path = data_set/dev.english.v4_gold_conll
  max_training_sentences = 11
  bert_config_file = ${best.log_root}/bert_base/bert_config.json
  vocab_file = ${best.log_root}/bert_base/vocab.txt
  tf_checkpoint = ${best.log_root}/bert_base/model.max.ckpt
  init_checkpoint = ${best.log_root}/bert_base/model.max.ckpt
}
...

---

Is there anything I missed?

[henlein]

Thank you for publishing the code. Is the correct general input format for predict.py like the above input or like in the readme? You also need the jsonkey: sentence_map. How is this input structured? Many thanks in advance.

[abhinandansrivastava]

Hey @mandarjoshi90 ,

Thanks for reply.

I tried making file in the format, still predict.py is still giving me error. ValueError: setting an array element with a sequence.

i ran below code: python predict.py bert_base cased_config_vocab/TestingFile.jsonlines cased_config_vocab/15.jsonlines

{“doc_key”: “wb”, “sentences”: [“[CLS]”, “who”, “was”, “jim”, “henson”, “?”, “jim”, “henson”, “was”, “a”, “puppet”, “##eer”, “.”, “[SEP]”], “speakers”: [“[SPL]”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “[SPL]”], “clusters”: [[]], “sentence_map”: [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2], “subtoken_map”: [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 10, 10]} {“doc_key”: “wb”, “sentences”: [“[CLS]”, “i”, “am”, “going”, “to”, “goa”, “and”, “love”, “biology”, “.”, “[SEP]”], “speakers”: [“[SPL]”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “-”, “[SPL]”], “clusters”: [[]], “sentence_map”: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], “subtoken_map”: [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 8]} TestingFile.zip