Training Loss	Epoch	Step	Validation Loss
6.7147	0.27	500	5.6451
5.3609	0.54	1000	5.2108
5.0162	0.81	1500	4.9585
4.7627	1.08	2000	4.8126
4.5775	1.35	2500	4.7013
4.4856	1.62	3000	4.6034
4.4038	1.89	3500	4.5175
4.2252	2.16	4000	4.4775
4.1408	2.42	4500	4.4236
4.1136	2.69	5000	4.3721
4.0852	2.96	5500	4.3281
3.87	3.23	6000	4.3418
3.8651	3.5	6500	4.3062
3.8601	3.77	7000	4.2781
3.8091	4.04	7500	4.2785
3.5972	4.31	8000	4.2888
3.6301	4.58	8500	4.2678
3.6398	4.85	9000	4.2396
3.4906	5.12	9500	4.2803
3.3704	5.39	10000	4.2849
3.4008	5.66	10500	4.2718
3.4029	5.93	11000	4.2491
3.1804	6.2	11500	4.3116
3.1361	6.47	12000	4.3119
3.1532	6.73	12500	4.3067
3.1591	7.0	13000	4.3072
2.8974	7.27	13500	4.3563
2.9167	7.54	14000	4.3589
2.9248	7.81	14500	4.3580
2.8683	8.08	15000	4.3791
2.741	8.35	15500	4.3939
2.7503	8.62	16000	4.3968
2.7573	8.89	16500	4.3983
2.6961	9.16	17000	4.4075
2.6562	9.43	17500	4.4101
2.6653	9.7	18000	4.4107
2.667	9.97	18500	4.4109

NasimB/gpt2-2-dp-mod-aochild-cut

gpt2-2-dp-mod-aochild-cut