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ActuallyOpenAI
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goingBALD

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  1. !_TAG_FILE_FORMAT	2	/extended format; --format=1 will not append ;" to lines/
    2. 

  2. !_TAG_FILE_SORTED	1	/0=unsorted, 1=sorted, 2=foldcase/
    3. 

  3. !_TAG_OUTPUT_FILESEP	slash	/slash or backslash/
    4. 

  4. !_TAG_OUTPUT_MODE	u-ctags	/u-ctags or e-ctags/
    5. 

  5. !_TAG_PROGRAM_AUTHOR	Universal Ctags Team	//
    6. 

  6. !_TAG_PROGRAM_NAME	Universal Ctags	/Derived from Exuberant Ctags/
    7. 

  7. !_TAG_PROGRAM_URL	https://ctags.io/	/official site/
    8. 

  8. !_TAG_PROGRAM_VERSION	0.0.0	/a3c87ab5/
    9. 

  9. <BOS>	tests/minimal.json	/^{"<UNK>": 0, "<BOS>": 1, "<EOS>": 2, "godzilla": 3, "spiderman": 4}/;"	n
    10. 

  10. <EOS>	tests/minimal.json	/^{"<UNK>": 0, "<BOS>": 1, "<EOS>": 2, "godzilla": 3, "spiderman": 4}/;"	n
    11. 

  11. <UNK>	tests/minimal.json	/^{"<UNK>": 0, "<BOS>": 1, "<EOS>": 2, "godzilla": 3, "spiderman": 4}/;"	n
    12. 

  12. ActiveLearningSamplerT	bald/data/samplers.py	/^class ActiveLearningSamplerT:$/;"	c
    13. 

  13. BOS_TOKEN	bald/data/constants.py	/^BOS_TOKEN = "<bos>"$/;"	v
    14. 

  14. BOW_TOKEN	bald/data/constants.py	/^BOW_TOKEN = "<bow>"$/;"	v
    15. 

  15. BatchIndicesSamplerFactory	bald/data/samplers.py	/^class BatchIndicesSamplerFactory:$/;"	c
    16. 

  16. CharEncoder	bald/model/model.py	/^class CharEncoder(nn.Module):$/;"	c
    17. 

  17. Charset	bald/data/indexer.py	/^class Charset(Indexer):$/;"	c
    18. 

  18. CoNLLNERDataset	bald/data/dataset.py	/^class CoNLLNERDataset(Dataset):$/;"	c
    19. 

  19. ConllDataset	bald/dataset.py	/^class ConllDataset(Dataset):$/;"	c
    20. 

  20. ConllModel	bald/simple_model.py	/^class ConllModel(nn.Module):$/;"	c
    21. 

  21. ConvBlock	bald/model/cnn.py	/^class ConvBlock(nn.Module):$/;"	c
    22. 

  22. ConvNet	bald/model/cnn.py	/^class ConvNet(nn.Module):$/;"	c
    23. 

  23. DATA_PROCESSED_DIR	scripts/train.py	/^DATA_PROCESSED_DIR = "artifacts\/data\/processed\/CoNLL2003\/"$/;"	v
    24. 

  24. Decoder	bald/model/model.py	/^class Decoder(nn.Module):$/;"	c
    25. 

  25. EOS_TOKEN	bald/data/constants.py	/^EOS_TOKEN = "<eos>"$/;"	v
    26. 

  26. EOW_TOKEN	bald/data/constants.py	/^EOW_TOKEN = "<eow>"$/;"	v
    27. 

  27. F	bald/conll_trainer.py	/^import torch.nn.functional as F$/;"	I
    28. 

  28. F	bald/model/model.py	/^import torch.nn.functional as F$/;"	I
    29. 

  29. F	bald/simple_model.py	/^import torch.nn.functional as F$/;"	I
    30. 

  30. Indexer	bald/data/indexer.py	/^class Indexer:$/;"	c
    31. 

  31. LabelVectorizer	bald/vectorizer.py	/^class LabelVectorizer:$/;"	c
    32. 

  32. MODEL_DIR	scripts/train.py	/^MODEL_DIR = "artifacts\/models"$/;"	v
    33. 

  33. MODEL_NAME	scripts/train.py	/^MODEL_NAME = "ConNLL_NERModel"$/;"	v
    34. 

  34. Model	bald/model/model.py	/^class Model(nn.Module):$/;"	c
    35. 

  35. PAD_TOKEN	bald/data/constants.py	/^PAD_TOKEN = "<pad>"$/;"	v
    36. 

  36. RandomALSampler	bald/data/samplers.py	/^class RandomALSampler(ActiveLearningSamplerT):$/;"	c
    37. 

  37. UNKNOWN_TOKEN	bald/data/constants.py	/^UNKNOWN_TOKEN = "<unk>"$/;"	v
    38. 

  38. Vectorizer	bald/vectorizer.py	/^class Vectorizer:$/;"	c
    39. 

  39. Vocab	bald/vocab.py	/^class Vocab:$/;"	c
    40. 

  40. Vocabulary	bald/data/indexer.py	/^class Vocabulary(Indexer):$/;"	c
    41. 

  41. WORD_EMBEDDING_SIZE	bald/parameters.py	/^WORD_EMBEDDING_SIZE = 300$/;"	v
    42. 

  42. WordEncoder	bald/model/model.py	/^class WordEncoder(nn.Module):$/;"	c
    43. 

  43. WordVectorizer	bald/vectorizer.py	/^class WordVectorizer:$/;"	c
    44. 

  44. _NEW_DOC_LINE	bald/data/conll2003_utils.py	/^_NEW_DOC_LINE = "-DOCSTART-"$/;"	v
    45. 

  45. _NEW_SENTENCE_LINE	bald/data/conll2003_utils.py	/^_NEW_SENTENCE_LINE = '\\n'$/;"	v
    46. 

  46. __all__	bald/__init__.py	/^__all__ = [$/;"	v
    47. 

  47. __getitem__	bald/data/dataset.py	/^    def __getitem__(self, key):$/;"	m	class:CoNLLNERDataset
    48. 

  48. __getitem__	bald/data/indexer.py	/^    def __getitem__(self, key):$/;"	m	class:Charset
    49. 

  49. __getitem__	bald/data/indexer.py	/^    def __getitem__(self, key):$/;"	m	class:Indexer
    50. 

  50. __getitem__	bald/data/indexer.py	/^    def __getitem__(self, key):$/;"	m	class:Vocabulary
    51. 

  51. __getitem__	bald/dataset.py	/^    def __getitem__(self,i):$/;"	m	class:ConllDataset
    52. 

  52. __init__	bald/data/dataset.py	/^    def __init__(self, sentence_data):$/;"	m	class:CoNLLNERDataset
    53. 

  53. __init__	bald/data/indexer.py	/^    def __init__(self):$/;"	m	class:Charset
    54. 

  54. __init__	bald/data/indexer.py	/^    def __init__(self):$/;"	m	class:Indexer
    55. 

  55. __init__	bald/data/indexer.py	/^    def __init__(self):$/;"	m	class:Vocabulary
    56. 

  56. __init__	bald/data/samplers.py	/^    def __init__($/;"	m	class:BatchIndicesSamplerFactory
    57. 

  57. __init__	bald/data/samplers.py	/^    def __init__(self, data: Dataset):$/;"	m	class:ActiveLearningSamplerT
    58. 

  58. __init__	bald/dataset.py	/^    def __init__(self,data_path,vectors,emb_dim):$/;"	m	class:ConllDataset
    59. 

  59. __init__	bald/model/cnn.py	/^    def __init__($/;"	m	class:ConvBlock
    60. 

  60. __init__	bald/model/cnn.py	/^    def __init__($/;"	m	class:ConvNet
    61. 

  61. __init__	bald/model/model.py	/^    def __init__($/;"	m	class:CharEncoder
    62. 

  62. __init__	bald/model/model.py	/^    def __init__($/;"	m	class:Decoder
    63. 

  63. __init__	bald/model/model.py	/^    def __init__($/;"	m	class:WordEncoder
    64. 

  64. __init__	bald/model/model.py	/^    def __init__(self, charset_size, char_embedding_size, char_channels,$/;"	m	class:Model
    65. 

  65. __init__	bald/simple_model.py	/^    def __init__(self,max_seq_len,emb_dim,num_labels):$/;"	m	class:ConllModel
    66. 

  66. __init__	bald/vectorizer.py	/^    def __init__(self, vectorizer: BaseWordVectorizer):$/;"	m	class:WordVectorizer
    67. 

  67. __init__	bald/vectorizer.py	/^    def __init__(self, vocab: Vocab):$/;"	m	class:Vectorizer
    68. 

  68. __init__	bald/vocab.py	/^    def __init__(self, pre_made: Dict[str,int] = None):$/;"	m	class:Vocab
    69. 

  69. __len__	bald/data/dataset.py	/^    def __len__(self):$/;"	m	class:CoNLLNERDataset
    70. 

  70. __len__	bald/data/indexer.py	/^    def __len__(self):$/;"	m	class:Indexer
    71. 

  71. __len__	bald/dataset.py	/^    def __len__(self):$/;"	m	class:ConllDataset
    72. 

  72. __len__	bald/vocab.py	/^    def __len__(self):$/;"	m	class:Vocab
    73. 

  73. _parse_NER_tag	bald/data/conll2003_utils.py	/^def _parse_NER_tag(raw_tag: str) -> str:$/;"	f
    74. 

  74. _parse_single_line	bald/data/conll2003_utils.py	/^def _parse_single_line(line: str) -> Dict:$/;"	f
    75. 

  75. add	bald/data/indexer.py	/^    def add(self, key):$/;"	m	class:Indexer
    76. 

  76. add_token	bald/vocab.py	/^    def add_token(self,token: str) -> int:$/;"	m	class:Vocab
    77. 

  77. args	scripts/train.py	/^args = parser.parse_args()$/;"	v
    78. 

  78. char_channels	scripts/train.py	/^char_channels = [args.emsize] + [args.char_nhid] * args.char_layers$/;"	v
    79. 

  79. charset	scripts/train.py	/^charset = Charset()$/;"	v
    80. 

  80. compute_max_seq_len	bald/dataset.py	/^    def compute_max_seq_len(self):$/;"	m	class:ConllDataset
    81. 

  81. compute_output_dim	bald/simple_model.py	/^    def compute_output_dim(self,h_in,padding,kernel_size):$/;"	m	class:ConllModel
    82. 

  82. criterion	scripts/train.py	/^criterion = nn.NLLLoss(weight, size_average=False)$/;"	v
    83. 

  83. data_dir	bald/__init__.py	/^data_dir = Path(__file__).parent.parent \/ "data"$/;"	v
    84. 

  84. dependencies	setup.py	/^dependencies = [$/;"	v
    85. 

  85. encoding	bald/vectorizer.py	/^    encoding = {$/;"	v	class:LabelVectorizer
    86. 

  86. epoch_run	bald/utils.py	/^def epoch_run($/;"	f
    87. 

  87. evaluate_model	scripts/train.py	/^def evaluate_model(test_data):$/;"	f
    88. 

  88. fh	scripts/train.py	/^fh = logging.FileHandler("artifacts\/logs\/train.log") # TODO add timestamp$/;"	v
    89. 

  89. forward	bald/model/cnn.py	/^    def forward(self, inputs):$/;"	m	class:ConvBlock
    90. 

  90. forward	bald/model/cnn.py	/^    def forward(self, inputs):$/;"	m	class:ConvNet
    91. 

  91. forward	bald/model/model.py	/^    def forward(self, inputs):$/;"	m	class:CharEncoder
    92. 

  92. forward	bald/model/model.py	/^    def forward(self, inputs):$/;"	m	class:Decoder
    93. 

  93. forward	bald/model/model.py	/^    def forward(self, word_input, char_input):$/;"	m	class:Model
    94. 

  94. forward	bald/model/model.py	/^    def forward(self, word_inputs, char_embedding_inputs):$/;"	m	class:WordEncoder
    95. 

  95. forward	bald/simple_model.py	/^    def forward(self,x_raw,apply_softmax=False,verbose=False):$/;"	m	class:ConllModel
    96. 

  96. from_json	bald/vocab.py	/^    def from_json(cls,path: str):$/;"	m	class:Vocab
    97. 

  97. generate_NER_tag_set	bald/data/conll2003_utils.py	/^def generate_NER_tag_set($/;"	f
    98. 

  98. get_data_sampler	bald/data/samplers.py	/^    def get_data_sampler(self) -> BatchSampler:$/;"	m	class:BatchIndicesSamplerFactory
    99. 

  99. godzilla	tests/minimal.json	/^{"<UNK>": 0, "<BOS>": 1, "<EOS>": 2, "godzilla": 3, "spiderman": 4}/;"	n
    100. 

  100. init_weight	bald/model/model.py	/^    def init_weight(self):$/;"	m	class:Decoder
    101. 

  101. init_weights	bald/model/cnn.py	/^    def init_weights(self):$/;"	m	class:ConvBlock
    102. 

  102. init_weights	bald/model/model.py	/^    def init_weights(self):$/;"	m	class:CharEncoder
    103. 

  103. len_sorted_raw_sentences	scripts/train.py	/^len_sorted_raw_sentences = sorted(train_raw_sentences, key=len, reverse=True)$/;"	v
    104. 

  104. load	bald/data/indexer.py	/^    def load(self, f):$/;"	m	class:Indexer
    105. 

  105. load_ner_dataset	bald/load_ner_dataset.py	/^def load_ner_dataset(path):$/;"	f
    106. 

  106. load_ner_dataset_old	bald/load_ner_dataset.py	/^def load_ner_dataset_old(path):$/;"	f
    107. 

  107. load_raw_dataset	bald/data/conll2003_utils.py	/^def load_raw_dataset(fpath: str) -> List[List[Dict]]:$/;"	f
    108. 

  108. logger	scripts/train.py	/^logger = logging.getLogger("train_logger")$/;"	v
    109. 

  109. lookup_id	bald/vocab.py	/^    def lookup_id(self,token: str) -> int:$/;"	m	class:Vocab
    110. 

  110. lookup_token	bald/vocab.py	/^    def lookup_token(self,j: int) -> str:$/;"	m	class:Vocab
    111. 

  111. loss_fun	bald/conll_trainer.py	/^def loss_fun(input,target):$/;"	f
    112. 

  112. max_seq_len	bald/conll_trainer.py	/^max_seq_len = max(train_ds.max_seq_len,test_ds.max_seq_len)$/;"	v
    113. 

  113. model	bald/conll_trainer.py	/^model = ConllModel($/;"	v
    114. 

  114. model	scripts/train.py	/^    model = torch.load(f)$/;"	v
    115. 

  115. model	scripts/train.py	/^model = Model($/;"	v
    116. 

  116. nn	bald/model/cnn.py	/^import torch.nn as nn$/;"	I
    117. 

  117. nn	bald/model/model.py	/^import torch.nn as nn$/;"	I
    118. 

  118. nn	scripts/train.py	/^import torch.nn as nn$/;"	I
    119. 

  119. np	bald/model/model.py	/^import numpy as np$/;"	I
    120. 

  120. np	scripts/train.py	/^import numpy as np$/;"	I
    121. 

  121. num_epochs	bald/conll_trainer.py	/^num_epochs = 30$/;"	v
    122. 

  122. optim	scripts/train.py	/^import torch.optim as optim$/;"	I
    123. 

  123. optimizer	bald/conll_trainer.py	/^optimizer = torch.optim.Adam(model.parameters())$/;"	v
    124. 

  124. optimizer	scripts/train.py	/^optimizer = torch.optim.Adam(model.parameters())$/;"	v
    125. 

  125. pad_embedding	scripts/train.py	/^pad_embedding = torch.empty(1, word_embedding_size).uniform_(-0.5, 0.5)$/;"	v
    126. 

  126. parse_single_line	bald/load_ner_dataset.py	/^def parse_single_line(line):$/;"	f
    127. 

  127. parser	scripts/train.py	/^parser = argparse.ArgumentParser()$/;"	v
    128. 

  128. plt	bald/conll_trainer.py	/^import matplotlib.pyplot as plt$/;"	I
    129. 

  129. pre_vectorize	bald/vectorizer.py	/^    def pre_vectorize(cls,sequence: List[str]) -> List[int]:$/;"	m	class:LabelVectorizer
    130. 

  130. pre_vectorize	bald/vectorizer.py	/^    def pre_vectorize(self,sequence: List[str]) -> List[int]:$/;"	m	class:Vectorizer
    131. 

  131. raw_data_to_train_data	scripts/train.py	/^def raw_data_to_train_data(sentences: List[List[str]]):$/;"	f
    132. 

  132. record2charidx	scripts/train.py	/^    def record2charidx(record):$/;"	f	function:raw_data_to_train_data	file:
    133. 

  133. record2idx	scripts/train.py	/^    record2idx = lambda record: vocab_set[record['word']]$/;"	f	function:raw_data_to_train_data	file:
    134. 

  134. record2tag	scripts/train.py	/^    record2tag = lambda record: tag_set[record['NER_tag']]$/;"	f	function:raw_data_to_train_data	file:
    135. 

  135. run_d	bald/conll_trainer.py	/^    run_d = epoch_run($/;"	v
    136. 

  136. sample_pool	bald/data/samplers.py	/^    def sample_pool(self, n_elements: int) -> None:$/;"	m	class:ActiveLearningSamplerT
    137. 

  137. sample_pool	bald/data/samplers.py	/^    def sample_pool(self, n_elements: int) -> None:$/;"	m	class:RandomALSampler
    138. 

  138. save	bald/data/indexer.py	/^    def save(self, f):$/;"	m	class:Indexer
    139. 

  139. score_fun	bald/conll_trainer.py	/^def score_fun(input,target):$/;"	f
    140. 

  140. set_max_seq_len	bald/dataset.py	/^    def set_max_seq_len(self,val: int):$/;"	m	class:ConllDataset
    141. 

  141. sh	scripts/train.py	/^sh = logging.StreamHandler(sys.stdout)$/;"	v
    142. 

  142. spiderman	tests/minimal.json	/^{"<UNK>": 0, "<BOS>": 1, "<EOS>": 2, "godzilla": 3, "spiderman": 4}/;"	n
    143. 

  143. tag_set	scripts/train.py	/^tag_set = Indexer()$/;"	v
    144. 

  144. test_char_vectorizer	tests/vectorizer_test.py	/^def test_char_vectorizer():$/;"	f
    145. 

  145. test_dl	bald/conll_trainer.py	/^test_dl = DataLoader(test_ds, batch_size=32, shuffle=False)$/;"	v
    146. 

  146. test_ds	bald/conll_trainer.py	/^test_ds = ConllDataset(data_path=test_path,vectors=vectors,emb_dim=300)$/;"	v
    147. 

  147. test_json	tests/vocab_test.py	/^def test_json():$/;"	f
    148. 

  148. test_label_vectorizer	tests/vectorizer_test.py	/^def test_label_vectorizer():$/;"	f
    149. 

  149. test_losses	bald/conll_trainer.py	/^test_losses = []$/;"	v
    150. 

  150. test_path	bald/conll_trainer.py	/^test_path = os.path.join(data_dir,"raw","CoNLL2003","eng.testa")$/;"	v
    151. 

  151. test_raw_sentences	scripts/train.py	/^test_raw_sentences = load_raw_dataset("artifacts\/data\/raw\/CoNLL2003\/eng.train")$/;"	v
    152. 

  152. test_sentences	scripts/train.py	/^test_sentences = CoNLLNERDataset(test_raw_sentences)$/;"	v
    153. 

  153. test_vocab	tests/vocab_test.py	/^def test_vocab():$/;"	f
    154. 

  154. time_display	bald/log_utils.py	/^def time_display(s):$/;"	f
    155. 

  155. to_json	bald/vocab.py	/^    def to_json(self,path: str):$/;"	m	class:Vocab
    156. 

  156. train_dl	bald/conll_trainer.py	/^train_dl = DataLoader(train_ds, batch_size=32, shuffle=True)$/;"	v
    157. 

  157. train_ds	bald/conll_trainer.py	/^train_ds = ConllDataset(data_path=train_path,vectors=vectors,emb_dim=300)$/;"	v
    158. 

  158. train_losses	bald/conll_trainer.py	/^train_losses = []$/;"	v
    159. 

  159. train_model	scripts/train.py	/^def train_model(epoch):$/;"	f
    160. 

  160. train_path	bald/conll_trainer.py	/^train_path = os.path.join(data_dir,"raw","CoNLL2003","eng.train")$/;"	v
    161. 

  161. train_raw_sentences	scripts/train.py	/^    train_raw_sentences = train_raw_sentences[:100]$/;"	v
    162. 

  162. train_raw_sentences	scripts/train.py	/^train_raw_sentences = load_raw_dataset("artifacts\/data\/raw\/CoNLL2003\/eng.train")$/;"	v
    163. 

  163. train_sentences	scripts/train.py	/^train_sentences = CoNLLNERDataset(len_sorted_raw_sentences)$/;"	v
    164. 

  164. type	bald/data/indexer.py	/^    def type(char):$/;"	m	class:Charset
    165. 

  165. unk_embedding	scripts/train.py	/^unk_embedding = torch.empty(1, word_embedding_size).uniform_(-0.5, 0.5)$/;"	v
    166. 

  166. vectorize	bald/vectorizer.py	/^    def vectorize(cls,sequence: List[str]) -> Tensor:$/;"	m	class:LabelVectorizer
    167. 

  167. vectorize	bald/vectorizer.py	/^    def vectorize(self, sequence: List[str]) -> Tensor:$/;"	m	class:WordVectorizer
    168. 

  168. vectorize	bald/vectorizer.py	/^    def vectorize(self,sequence: List[str]) -> Tensor:$/;"	m	class:Vectorizer
    169. 

  169. vectors	bald/conll_trainer.py	/^vectors = GloVe(cache=vectors_dir)$/;"	v
    170. 

  170. vectors_dir	bald/__init__.py	/^vectors_dir = Path(__file__).parent.parent \/ ".word_vectors_cache"$/;"	v
    171. 

  171. vocab_set	scripts/train.py	/^vocab_set = Vocabulary()$/;"	v
    172. 

  172. weight	scripts/train.py	/^weight = [args.weight] * len(tag_set)$/;"	v
    173. 

  173. weight	scripts/train.py	/^weight = torch.Tensor(weight)$/;"	v
    174. 

  174. word_channels	scripts/train.py	/^word_channels = [word_embedding_size + args.char_nhid] + [args.word_nhid] * args.word_layers$/;"	v
    175. 

  175. word_embedding_size	scripts/train.py	/^word_embedding_size = word_embeddings.size(1)$/;"	v
    176. 

  176. word_embeddings	scripts/train.py	/^word_embeddings = torch.Tensor($/;"	v
    177. 

  177. word_embeddings	scripts/train.py	/^word_embeddings = torch.cat([pad_embedding, unk_embedding, word_embeddings])$/;"	v
    178.
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Integrate Google Cloud Storage
Use Google Storage
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Use Google Cloud Storage!

Browsing data directories saved to Google Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

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Service Account Key

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goingBALD is now integrated with Google Cloud Storage!

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Integrate AWS S3
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Use AWS S3 as storage!

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Integrate S3 compatible storage
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Use any S3 compatible storage!

Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

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Integrate Azure Cloud Storage
Use Azure Storage
Select bucket
Set key
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Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

goingBALD is now integrated with Azure Cloud Storage!

Delete Storage Key

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Yes