SlowGuess/NAACL_2021_OCR huggingface dataset:
Embedding	Model	Device	Batch Size	Min Time (s)	Max Time (s)
GloVe	STL	CPU	1	10.24	10.45
8	7.00	7.21
16	6.67	6.91
32	6.35	6.51
GPU	1	9.24	9.26
8	6.58	6.67
16	6.34	6.36
32	6.12	6.26
MTL	CPU	1	10.06	10.26
8	7.05	7.19
16	6.90	6.99
32	6.41	6.46
GPU	1	9.43	9.49
8	6.60	6.70
16	6.26	6.55
32	6.10	6.20
TL	STL	CPU	1	64.79	71.26
8	43.62	49.70
16	47.06	48.41
32	56.76	62.77
GPU	1	23.26	23.79
8	8.82	9.09
16	8.57	8.86
32	8.45	9.78
MTL	CPU	1	64.01	67.90
8	49.05	50.00
16	53.47	56.42
32	55.33	55.79
GPU	1	23.81	23.97
8	9.19	9.49
16	8.54	8.92
32	8.43	8.70
Table 20: Run/inference times for STL and MTL models that have been trained on the Laptop dataset using either GloVe or TL embeddings. Each model was timed in seconds (s) to generate predictions for 800 sentences, that were taken from the Laptop test split, of which this process was repeated five times and here we report the minimum (min) and maximum (max) time to generate predictions for those 800 sentences. We report these timings across different model configurations based on different batch sizes at prediction time and different devices. The trained MTL model used in this experiment was the MTL  $(\mathrm{NEG}_{SFU})$  version, this was chosen as it contains the largest number of total parameters as shown in Table 19. Further all of these times were based on the model already loaded into memory and using the Python timeit library for timings. Additionally the GPU used was a GeForce GTX 1060 6GB GPU, CPU was an AMD Ryzen 5 1600 CPU, and the computer had 16GB of RAM.