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ai-forever / ner-bert

BERT-NER (nert-bert) with google bert https://github.com/google-research.
MIT License
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Key Error creating NerData #12

Closed MNCTTY closed 5 years ago

MNCTTY commented 5 years ago

Hello again!

I have a strange error while I run data = NerData.create(train_path, valid_path, vocab_file)

KeyError Traceback (most recent call last) /opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 3063 try: -> 3064 return self._engine.get_loc(key) 3065 except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: '1'

During handling of the above exception, another exception occurred:

KeyError Traceback (most recent call last)

in ----> 1 data = NerData.create(train_path, valid_path, vocab_file) ~/ner-bert-master/modules/data/bert_data.py in create(cls, train_path, valid_path, vocab_file, batch_size, cuda, is_cls, data_type, max_seq_len, is_meta) 389 raise NotImplementedError("No requested mode :(.") 390 return cls(train_path, valid_path, vocab_file, data_type, *fn( --> 391 train_path, valid_path, vocab_file, batch_size, cuda, is_cls, do_lower_case, max_seq_len, is_meta), 392 batch_size=batch_size, cuda=cuda, is_meta=is_meta) ~/ner-bert-master/modules/data/bert_data.py in get_bert_data_loaders(train, valid, vocab_file, batch_size, cuda, is_cls, do_lower_case, max_seq_len, is_meta, label2idx, cls2idx) 279 tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case) 280 train_f, label2idx = get_data( --> 281 train, tokenizer, label2idx, cls2idx=cls2idx, is_cls=is_cls, max_seq_len=max_seq_len, is_meta=is_meta) 282 if is_cls: 283 label2idx, cls2idx = label2idx ~/ner-bert-master/modules/data/bert_data.py in get_data(df, tokenizer, label2idx, max_seq_len, pad, cls2idx, is_cls, is_meta) 145 all_args.extend([df["1"].tolist(), df["0"].tolist(), df["2"].tolist()]) 146 else: --> 147 all_args.extend([df["1"].tolist(), df["0"].tolist()]) 148 if is_meta: 149 all_args.append(df["3"].tolist()) /opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key) 2686 return self._getitem_multilevel(key) 2687 else: -> 2688 return self._getitem_column(key) 2689 2690 def _getitem_column(self, key): /opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in _getitem_column(self, key) 2693 # get column 2694 if self.columns.is_unique: -> 2695 return self._get_item_cache(key) 2696 2697 # duplicate columns & possible reduce dimensionality /opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in _get_item_cache(self, item) 2484 res = cache.get(item) 2485 if res is None: -> 2486 values = self._data.get(item) 2487 res = self._box_item_values(item, values) 2488 cache[item] = res /opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals.py in get(self, item, fastpath) 4113 4114 if not isna(item): -> 4115 loc = self.items.get_loc(item) 4116 else: 4117 indexer = np.arange(len(self.items))[isna(self.items)] /opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 3064 return self._engine.get_loc(key) 3065 except KeyError: -> 3066 return self._engine.get_loc(self._maybe_cast_indexer(key)) 3067 3068 indexer = self.get_indexer([key], method=method, tolerance=tolerance) pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: '1'

May be you've faced it too. I can't google something similar to my case

king-menin commented 5 years ago

please see u csv file, that u pass to model