learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss
46.6459	0.5714	1	43.1485
65.3532	1.5714	2	41.6135
62.735	2.5714	3	40.4950
61.766	3.5714	4	39.5923
57.7849	4.5714	5	38.7835
55.8212	5.5714	6	38.0234
55.277	6.5714	7	37.3084
53.3929	7.5714	8	36.6244
53.3042	8.5714	9	35.9682
51.6622	9.5714	10	35.3432
50.679	10.5714	11	34.7521
49.5895	11.5714	12	34.1904
48.219	12.5714	13	33.6558
47.7489	13.5714	14	33.1496
46.8016	14.5714	15	32.6729
45.2487	15.5714	16	32.2232
45.3148	16.5714	17	31.7991
44.3454	17.5714	18	31.3964
43.6766	18.5714	19	31.0145
42.8757	19.5714	20	30.6499
42.9431	20.5714	21	30.2991
41.7531	21.5714	22	29.9607
41.2977	22.5714	23	29.6343
40.7757	23.5714	24	29.3154
40.0453	24.5714	25	29.0031
39.602	25.5714	26	28.6966
39.7306	26.5714	27	28.3944
38.703	27.5714	28	28.0947
38.7594	28.5714	29	27.7978
38.3422	29.5714	30	27.5051
38.1699	30.5714	31	27.2152
37.9031	31.5714	32	26.9286
36.9734	32.5714	33	26.6434
37.0667	33.5714	34	26.3599
36.466	34.5714	35	26.0803
35.9406	35.5714	36	25.8051
35.6333	36.5714	37	25.5321
35.3167	37.5714	38	25.2619
35.3965	38.5714	39	24.9950
34.5731	39.5714	40	24.7318
34.2446	40.5714	41	24.4734
34.2668	41.5714	42	24.2188
33.6151	42.5714	43	23.9683
33.5523	43.5714	44	23.7208
33.0644	44.5714	45	23.4754
32.7476	45.5714	46	23.2334
32.9809	46.5714	47	22.9938
32.0039	47.5714	48	22.7560
32.0855	48.5714	49	22.5215
31.6121	49.5714	50	22.2902
31.5072	50.5714	51	22.0626
31.0177	51.5714	52	21.8383
30.7915	52.5714	53	21.6181
30.6385	53.5714	54	21.4022
30.0557	54.5714	55	21.1892
30.2011	55.5714	56	20.9796
29.8038	56.5714	57	20.7722
29.5406	57.5714	58	20.5675
29.2149	58.5714	59	20.3655
29.2198	59.5714	60	20.1660
28.8561	60.5714	61	19.9673
28.6416	61.5714	62	19.7716
28.8112	62.5714	63	19.5786
28.2165	63.5714	64	19.3885
27.8991	64.5714	65	19.2027
27.5846	65.5714	66	19.0196
27.2384	66.5714	67	18.8399
27.0191	67.5714	68	18.6630
27.1786	68.5714	69	18.4899
26.8166	69.5714	70	18.3193
26.5479	70.5714	71	18.1547
26.083	71.5714	72	17.9942
26.2608	72.5714	73	17.8381
26.4425	73.5714	74	17.6867
25.715	74.5714	75	17.5401
25.3579	75.5714	76	17.3974
25.4759	76.5714	77	17.2583
25.494	77.5714	78	17.1240
25.2856	78.5714	79	16.9947
24.9815	79.5714	80	16.8690
25.0225	80.5714	81	16.7476
24.868	81.5714	82	16.6309
24.7249	82.5714	83	16.5196
24.446	83.5714	84	16.4145
24.7399	84.5714	85	16.3154
24.1898	85.5714	86	16.2216
24.451	86.5714	87	16.1322
24.1094	87.5714	88	16.0485
23.9994	88.5714	89	15.9708
23.7422	89.5714	90	15.8995
23.967	90.5714	91	15.8342
23.7289	91.5714	92	15.7749
23.5241	92.5714	93	15.7217
23.5077	93.5714	94	15.6754
23.7215	94.5714	95	15.6356
23.5504	95.5714	96	15.6024
23.4054	96.5714	97	15.5761
23.4654	97.5714	98	15.5563
23.3449	98.5714	99	15.5431
23.5244	99.5714	100	15.5366

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.0
Tokenizers 0.21.0

devagonal/flan-t5-base-squad-qag

flan-t5-base-squad-qag

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

devagonal/
flan-t5-base-squad-qag