codeparrot-ds2

GPT-2 style trained on a filtered set of The Stack, specific to data science related code. Things like pandas, numpy, matplotlib, etc.

• Loss: 1.0584

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 200
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
2.2038	0.01	500	2.1062
2.0551	0.02	1000	2.0109
1.9622	0.02	1500	1.9219
1.9512	0.03	2000	1.8461
1.8817	0.04	2500	1.7903
1.8341	0.05	3000	1.7401
1.7877	0.05	3500	1.7022
1.7586	0.06	4000	1.6694
1.7271	0.07	4500	1.6457
1.7034	0.08	5000	1.6193
1.6756	0.08	5500	1.5978
1.6576	0.09	6000	1.5772
1.6377	0.1	6500	1.5611
1.6211	0.11	7000	1.5453
1.6033	0.11	7500	1.5317
1.591	0.12	8000	1.5193
1.5765	0.13	8500	1.5053
1.5661	0.14	9000	1.4966
1.5548	0.15	9500	1.4846
1.5429	0.15	10000	1.4729
1.5347	0.16	10500	1.4641
1.5215	0.17	11000	1.4557
1.5151	0.18	11500	1.4454
1.5059	0.18	12000	1.4381
1.499	0.19	12500	1.4288
1.4906	0.2	13000	1.4210
1.4849	0.21	13500	1.4143
1.4765	0.21	14000	1.4085
1.4708	0.22	14500	1.4026
1.4602	0.23	15000	1.3936
1.4533	0.24	15500	1.3896
1.4523	0.25	16000	1.3818
1.4415	0.25	16500	1.3748
1.4417	0.26	17000	1.3701
1.4311	0.27	17500	1.3645
1.4282	0.28	18000	1.3585
1.4223	0.28	18500	1.3531
1.4165	0.29	19000	1.3473
1.4105	0.3	19500	1.3419
1.3993	0.31	20000	1.3374
1.4034	0.31	20500	1.3322
1.3982	0.32	21000	1.3278
1.3951	0.33	21500	1.3225
1.3806	0.34	22000	1.3180
1.3781	0.34	22500	1.3121
1.3761	0.35	23000	1.3082
1.3662	0.36	23500	1.3038
1.3631	0.37	24000	1.2995
1.3549	0.38	24500	1.2955
1.3577	0.38	25000	1.2912
1.3498	0.39	25500	1.2851
1.3428	0.4	26000	1.2807
1.342	0.41	26500	1.2768
1.3365	0.41	27000	1.2720
1.3313	0.42	27500	1.2678
1.3309	0.43	28000	1.2629
1.3221	0.44	28500	1.2594
1.3214	0.44	29000	1.2558
1.3099	0.45	29500	1.2510
1.31	0.46	30000	1.2449
1.31	0.47	30500	1.2414
1.305	0.48	31000	1.2390
1.2975	0.48	31500	1.2358
1.2882	0.49	32000	1.2311
1.2831	0.5	32500	1.2251
1.2836	0.51	33000	1.2212
1.2817	0.51	33500	1.2178
1.2772	0.52	34000	1.2130
1.2651	0.53	34500	1.2080
1.2683	0.54	35000	1.2048
1.2581	0.54	35500	1.1999
1.263	0.55	36000	1.1972
1.255	0.56	36500	1.1924
1.2466	0.57	37000	1.1884
1.2448	0.57	37500	1.1860
1.2413	0.58	38000	1.1804
1.2362	0.59	38500	1.1782
1.2309	0.6	39000	1.1732
1.2289	0.61	39500	1.1687
1.2208	0.61	40000	1.1649
1.2225	0.62	40500	1.1605
1.2178	0.63	41000	1.1555
1.208	0.64	41500	1.1533
1.2069	0.64	42000	1.1490
1.206	0.65	42500	1.1453
1.2013	0.66	43000	1.1414
1.2003	0.67	43500	1.1374
1.1867	0.67	44000	1.1337
1.187	0.68	44500	1.1302
1.188	0.69	45000	1.1270
1.179	0.7	45500	1.1237
1.1866	0.71	46000	1.1204
1.173	0.71	46500	1.1173
1.1706	0.72	47000	1.1134
1.1645	0.73	47500	1.1099
1.1641	0.74	48000	1.1063
1.1623	0.74	48500	1.1032
1.1561	0.75	49000	1.1006
1.1531	0.76	49500	1.0977
1.1569	0.77	50000	1.0950
1.1505	0.77	50500	1.0927
1.1473	0.78	51000	1.0902
1.1428	0.79	51500	1.0870
1.1412	0.8	52000	1.0844
1.1452	0.8	52500	1.0823
1.1391	0.81	53000	1.0805
1.1329	0.82	53500	1.0783
1.1295	0.83	54000	1.0764
1.125	0.84	54500	1.0746
1.1295	0.84	55000	1.0730
1.1247	0.85	55500	1.0711
1.1225	0.86	56000	1.0696
1.1235	0.87	56500	1.0680
1.1192	0.87	57000	1.0670
1.1189	0.88	57500	1.0654
1.1196	0.89	58000	1.0646
1.1152	0.9	58500	1.0635
1.1133	0.9	59000	1.0628
1.1126	0.91	59500	1.0619
1.1142	0.92	60000	1.0610
1.1112	0.93	60500	1.0605
1.1137	0.93	61000	1.0599
1.1127	0.94	61500	1.0595
1.1111	0.95	62000	1.0592
1.1121	0.96	62500	1.0588
1.1114	0.97	63000	1.0587
1.1121	0.97	63500	1.0585
1.1078	0.98	64000	1.0584
1.1104	0.99	64500	1.0584
1.1057	1.0	65000	1.0584

Framework versions

• Transformers 4.30.2
• Pytorch 1.13.1
• Datasets 2.13.1
• Tokenizers 0.13.3