facebookresearch / SpanBERT
File Size

The distribution of size of files (measured in lines of code).

Intro
  • File size measurements show the distribution of size of files.
  • Files are classified in four categories based on their size (lines of code): 1-100 (very small files), 101-200 (small files), 201-500 (medium size files), 501-1000 (long files), 1001+(very long files).
  • It is a good practice to keep files small. Long files may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.
Learn more...
File Size Overall
  • There are 83 files with 11,541 lines of code.
    • 0 very long files (0 lines of code)
    • 6 long files (4,260 lines of code)
    • 10 medium size files (2,975 lines of codeclsfd_ftr_w_mp_ins)
    • 17 small files (2,346 lines of code)
    • 50 very small files (1,960 lines of code)
0% | 36% | 25% | 20% | 16%
Legend:
1001+
501-1000
201-500
101-200
1-100


explore: zoomable circles | sunburst | 3D view
File Size per Extension
1001+
501-1000
201-500
101-200
1-100
py0% | 36% | 25% | 20% | 16%
File Size per Logical Decomposition
primary
1001+
501-1000
201-500
101-200
1-100
code0% | 81% | 15% | 3% | 0%
pretraining/fairseq/models0% | 81% | 0% | 0% | 18%
code/pytorch_pretrained_bert0% | 52% | 23% | 14% | 9%
pretraining/fairseq0% | 0% | 61% | 32% | 6%
pretraining/fairseq/data0% | 0% | 56% | 41% | 2%
pretraining0% | 0% | 45% | 31% | 22%
pretraining/fairseq/modules0% | 0% | 25% | 44% | 29%
pretraining/fairseq/optim0% | 0% | 0% | 33% | 66%
pretraining/fairseq/tasks0% | 0% | 0% | 60% | 39%
pretraining/fairseq/criterions0% | 0% | 0% | 0% | 100%
Longest Files (Top 50)
File# lines# units
run_squad.py
in code
 988 25
run_mrqa.py
in code
 806 16
run_glue.py
in code
 770 59
modeling.py
in code/pytorch_pretrained_bert
 615 60
pair_bert.py
in pretraining/fairseq/models
 558 59
hf_bert.py
in pretraining/fairseq/models
 523 55
run_tacred.py
in code
 487 14
span_bert_dataset.py
in pretraining/fairseq/data
 350 17
utils.py
in pretraining/fairseq
 302 26
options.py
in pretraining/fairseq
 298 17
train.py
in pretraining
 287 9
tokenization.py
in code/pytorch_pretrained_bert
 274 20
trainer.py
in pretraining/fairseq
 264 18
multihead_attention.py
in pretraining/fairseq/modules
 253 20
masking.py
in pretraining/fairseq/data
 242 20
indexed_dataset.py
in pretraining/fairseq/data
 218 33
preprocess.py
in pretraining
 200 5
no_nsp_span_bert_dataset.py
in pretraining/fairseq/data
 200 13
downsampled_multihead_attention.py
in pretraining/fairseq/modules
 197 8
file_utils.py
in code/pytorch_pretrained_bert
 175 11
dictionary.py
in pretraining/fairseq/data
 149 19
span_bert.py
in pretraining/fairseq/tasks
 145 11
adaptive_softmax.py
in pretraining/fairseq/modules
 136 10
progress_bar.py
in pretraining/fairseq
 131 27
iterators.py
in pretraining/fairseq/data
 131 22
bert_adam.py
in pretraining/fairseq/optim
 123 9
fp16_optimizer.py
in pretraining/fairseq/optim
 122 17
data_utils.py
in pretraining/fairseq/data
 113 8
tokenizer.py
in pretraining/fairseq
 112 7
bidirectional_multihead_attention.py
in pretraining/fairseq/modules
 105 7
meters.py
in pretraining/fairseq
 103 21
distributed_utils.py
in pretraining/fairseq
 103 8
mrqa_official_eval.py
in code
 101 8
optimization.py
in code/pytorch_pretrained_bert
 100 6
fairseq_model.py
in pretraining/fairseq/models
 98 20
mlm_nsp_sbo_loss.py
in pretraining/fairseq/criterions
 85 3
spanbert_loss.py
in pretraining/fairseq/criterions
 72 3
adam.py
in pretraining/fairseq/optim
 67 5
bert_loss.py
in pretraining/fairseq/criterions
 63 3
cosine_lr_scheduler.py
in pretraining/fairseq/optim/lr_scheduler
 61 4
legacy_distributed_data_parallel.py
in pretraining/fairseq
 59 5
fairseq_task.py
in pretraining/fairseq/tasks
 59 12
multiprocessing_train.py
in pretraining
 59 6
sinusoidal_positional_embedding.py
in pretraining/fairseq/modules
 56 5
adaptive_input.py
in pretraining/fairseq/modules
 55 4
adaptive_inputs.py
in pretraining/fairseq/modules
 55 4
distributed_fairseq_model.py
in pretraining/fairseq/models
 52 1
composite_loss.py
in pretraining/fairseq/criterions
 52 8
bpe_tokenize.py
in pretraining
 50 4
mlm_loss.py
in pretraining/fairseq/criterions
 50 3
Files With Most Units (Top 20)
File# lines# units
modeling.py
in code/pytorch_pretrained_bert
 615 60
run_glue.py
in code
 770 59
pair_bert.py
in pretraining/fairseq/models
 558 59
hf_bert.py
in pretraining/fairseq/models
 523 55
indexed_dataset.py
in pretraining/fairseq/data
 218 33
progress_bar.py
in pretraining/fairseq
 131 27
utils.py
in pretraining/fairseq
 302 26
run_squad.py
in code
 988 25
iterators.py
in pretraining/fairseq/data
 131 22
meters.py
in pretraining/fairseq
 103 21
tokenization.py
in code/pytorch_pretrained_bert
 274 20
fairseq_model.py
in pretraining/fairseq/models
 98 20
multihead_attention.py
in pretraining/fairseq/modules
 253 20
masking.py
in pretraining/fairseq/data
 242 20
dictionary.py
in pretraining/fairseq/data
 149 19
trainer.py
in pretraining/fairseq
 264 18
options.py
in pretraining/fairseq
 298 17
fp16_optimizer.py
in pretraining/fairseq/optim
 122 17
span_bert_dataset.py
in pretraining/fairseq/data
 350 17
run_mrqa.py
in code
 806 16
Files With Long Lines (Top 18)

There are 18 files with lines longer than 120 characters. In total, there are 47 long lines.

File# lines# units# long lines
pair_bert.py
in pretraining/fairseq/models
 558 59 10
masking.py
in pretraining/fairseq/data
 242 20 8
span_bert_dataset.py
in pretraining/fairseq/data
 350 17 4
bpe_tokenize.py
in pretraining
 50 4 3
hf_bert.py
in pretraining/fairseq/models
 523 55 3
modeling.py
in code/pytorch_pretrained_bert
 615 60 2
run_squad.py
in code
 988 25 2
run_mrqa.py
in code
 806 16 2
span_bert.py
in pretraining/fairseq/tasks
 145 11 2
multihead_attention.py
in pretraining/fairseq/modules
 253 20 2
no_nsp_span_bert_dataset.py
in pretraining/fairseq/data
 200 13 2
run_tacred.py
in code
 487 14 1
run_glue.py
in code
 770 59 1
optimization.py
in code/pytorch_pretrained_bert
 100 6 1
options.py
in pretraining/fairseq
 298 17 1
polynomial_decay_schedule.py
in pretraining/fairseq/optim/lr_scheduler
 43 5 1
utils.py
in pretraining/fairseq
 302 26 1
train.py
in pretraining
 287 9 1