Training Loss	Epoch	Step	Validation Loss
0.3825	4.28	5000	0.1047
0.0883	8.56	10000	0.0668
0.0633	12.84	15000	0.0505
0.0545	17.12	20000	0.0472
0.0503	21.4	25000	0.0441
0.0477	25.68	30000	0.0420
0.0457	29.97	35000	0.0408
0.0444	34.25	40000	0.0401
0.0435	38.53	45000	0.0399
0.0428	42.81	50000	0.0391
0.0423	47.09	55000	0.0388
0.0418	51.37	60000	0.0386
0.0414	55.65	65000	0.0384
0.0411	59.93	70000	0.0381
0.0408	64.21	75000	0.0381
0.0405	68.49	80000	0.0379
0.0403	72.77	85000	0.0376
0.0401	77.05	90000	0.0375
0.0399	81.34	95000	0.0375
0.0398	85.62	100000	0.0374
0.0396	89.9	105000	0.0373
0.0395	94.18	110000	0.0373
0.0393	98.46	115000	0.0372
0.0392	102.74	120000	0.0370
0.0391	107.02	125000	0.0369
0.039	111.3	130000	0.0370
0.0389	115.58	135000	0.0369
0.0388	119.86	140000	0.0369
0.0387	124.14	145000	0.0368
0.0386	128.42	150000	0.0367
0.0385	132.71	155000	0.0367
0.0388	136.99	160000	0.0511
0.0384	141.27	165000	0.0367
0.0383	145.55	170000	0.0366
0.0382	149.83	175000	0.0365
0.0381	154.11	180000	0.0365
0.0381	158.39	185000	0.0364
0.038	162.67	190000	0.0364
0.0379	166.95	195000	0.0364
0.0378	171.23	200000	0.0365
0.0378	175.51	205000	0.0363
0.0377	179.79	210000	0.0362
0.0377	184.08	215000	0.0362
0.0376	188.36	220000	0.0362
0.0375	192.64	225000	0.0361
0.0375	196.92	230000	0.0361
0.0374	201.2	235000	0.0361
0.0373	205.48	240000	0.0360
0.0373	209.76	245000	0.0360
0.0372	214.04	250000	0.0360
0.0372	218.32	255000	0.0359
0.0371	222.6	260000	0.0359
0.0371	226.88	265000	0.0359
0.037	231.16	270000	0.0359
0.037	235.45	275000	0.0358
0.0375	239.73	280000	0.0358
0.0369	244.01	285000	0.0357
0.0368	248.29	290000	0.0357
0.0367	252.57	295000	0.0358
0.0367	256.85	300000	0.0357
0.0368	261.13	305000	0.0356
0.0366	265.41	310000	0.0357
0.0366	269.69	315000	0.0356
0.0365	273.97	320000	0.0355
0.0364	278.25	325000	0.0356
0.0364	282.53	330000	0.0356
0.0363	286.81	335000	0.0355
0.0363	291.1	340000	0.0355
0.0362	295.38	345000	0.0354
0.0362	299.66	350000	0.0355
0.0361	303.94	355000	0.0354
0.0361	308.22	360000	0.0354
0.036	312.5	365000	0.0354
0.036	316.78	370000	0.0354
0.036	321.06	375000	0.0353
0.0359	325.34	380000	0.0353
0.0359	329.62	385000	0.0354
0.0358	333.9	390000	0.0353
0.0358	338.18	395000	0.0353
0.0357	342.47	400000	0.0352
0.0358	346.75	405000	0.0351
0.0356	351.03	410000	0.0352
0.0356	355.31	415000	0.0352
0.0356	359.59	420000	0.0352
0.0355	363.87	425000	0.0352
0.0355	368.15	430000	0.0351
0.0355	372.43	435000	0.0351
0.0354	376.71	440000	0.0351
0.0354	380.99	445000	0.0350
0.0354	385.27	450000	0.0351
0.0353	389.55	455000	0.0351
0.0353	393.84	460000	0.0350
0.0353	398.12	465000	0.0350
0.0352	402.4	470000	0.0350
0.0352	406.68	475000	0.0350
0.0352	410.96	480000	0.0350
0.0352	415.24	485000	0.0350
0.0351	419.52	490000	0.0350
0.0351	423.8	495000	0.0349
0.0351	428.08	500000	0.0349
0.0351	432.36	505000	0.0349
0.0351	436.64	510000	0.0349
0.035	440.92	515000	0.0349
0.035	445.21	520000	0.0349
0.035	449.49	525000	0.0349
0.035	453.77	530000	0.0349
0.035	458.05	535000	0.0349
0.035	462.33	540000	0.0349
0.0349	466.61	545000	0.0349
0.0349	470.89	550000	0.0349
0.0349	475.17	555000	0.0349
0.0349	479.45	560000	0.0349
0.0349	483.73	565000	0.0349
0.0349	488.01	570000	0.0349
0.0349	492.29	575000	0.0349
0.0349	496.58	580000	0.0349

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu117
Datasets 2.8.0
Tokenizers 0.13.2

nikaashpuri/gpt-expt-sp-v3-K-300-9-mixed-with-tv

gpt-expt-sp-v3-K-300-9-mixed-with-tv

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nikaashpuri/
gpt-expt-sp-v3-K-300-9-mixed-with-tv