• 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.

• There are 142 files with 46,109 lines of code.
• 12 very long files (23,149 lines of code)
• 14 long files (9,824 lines of code)
• 26 medium size files (9,271 lines of codeclsfd_ftr_w_mp_ins)
• 16 small files (2,129 lines of code)
• 74 very small files (1,736 lines of code)

There are 47 files with lines longer than 120 characters. In total, there are 1271 long lines.

File	# lines	# units	# long lines
packedBERT_single_label_text_classification.ipynb in notebooks/packed_bert	1549	-	87
packedBERT_question_answering.ipynb in notebooks/packed_bert	1177	-	82
question_answering.ipynb in notebooks	1691	-	79
packedBERT_multi_label_text_classification.ipynb in notebooks/packed_bert	1474	-	78
text_classification.ipynb in notebooks	1046	-	61
image_classification.ipynb in notebooks	7405	-	58
token_classification.ipynb in notebooks	1184	-	56
introduction_to_optimum_graphcore.ipynb in notebooks	2771	-	54
language_modelling_from_scratch.ipynb in notebooks	1102	-	51
sentiment_analysis.ipynb in notebooks	816	-	48
wav2vec2-fine-tuning-checkpoint.ipynb in notebooks/wav2vec2	803	-	45
language_modeling.ipynb in notebooks	1074	-	43
name-entity-extraction.ipynb in notebooks	515	-	42
flan_t5_inference.ipynb in notebooks	539	-	39
multiple_choice.ipynb in notebooks	932	-	39
translation.ipynb in notebooks	921	-	38
summarization.ipynb in notebooks	933	-	38
text-embeddings-on-ipu.ipynb in notebooks/text_embeddings_models	619	-	35
whisper-quantized-example.ipynb in notebooks	425	-	29
wav2vec2-inference-checkpoint.ipynb in notebooks/wav2vec2	409	-	26
whisper-example.ipynb in notebooks	432	-	25
whisper_finetuning.ipynb in notebooks	594	-	24
external_model.ipynb in notebooks	693	-	24
mt5_xnli.ipynb in notebooks	415	-	20
text_to_image.ipynb in notebooks/stable_diffusion	333	-	17
inpainting.ipynb in notebooks/stable_diffusion	377	-	17
image_to_image.ipynb in notebooks/stable_diffusion	363	-	17
managing_ipu_resources.ipynb in notebooks	445	-	17
deberta-blog-notebook.ipynb in notebooks	373	-	16
text_to_image_sd2.ipynb in notebooks/stable_diffusion	335	-	15
training_args.py in optimum/graphcore	452	13	10
modeling_utils.py in optimum/graphcore	479	37	6
ipu_configuration.py in optimum/graphcore	394	21	5
trainer.py in optimum/graphcore	1497	54	5
utils.py in optimum/graphcore/generation	1179	36	4
modeling_bart.py in optimum/graphcore/models/bart	807	26	4
packed_bert.py in notebooks/packed_bert/pipeline	342	8	3
dataset_creator.py in notebooks/packed_bert/utils/packing	225	4	2
config.py in notebooks/text_embeddings_models	78	1	2
attention_mixin.py in optimum/graphcore/generation	300	21	2
modeling_whisper.py in optimum/graphcore/models/whisper	511	24	2
modelcard.py in optimum/graphcore	105	2	1
__init__.py in optimum/graphcore/pipelines	400	6	1
modeling_t5.py in optimum/graphcore/models/t5	569	19	1
modeling_groupbert.py in optimum/graphcore/models/groupbert	572	32	1
group_quantize_decompress.cpp in optimum/graphcore/custom_ops/group_quantize_decompress	77	5	1
pipeline_stable_diffusion_mixin.py in optimum/graphcore/diffusers/pipelines/stable_diffusion	337	22	1