amazon-reviews-input-output-1.3b

This model is a fine-tuned version of facebook/opt-1.3b on the AlekseyKorshuk/amazon-reviews-input-output dataset. It achieves the following results on the evaluation set:

• Loss: 3.5488
• Accuracy: 0.0355
• Samples: 100
• Perplexity: 34.7725
• Table: <wandb.data_types.Table object at 0x7ffa3c3fd700>

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 64
• total_eval_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.2024	0.06	1	2.9121	0.0385
3.1226	0.12	2	2.9121	0.0385
3.1321	0.19	3	2.8477	0.0394
2.9875	0.25	4	2.8477	0.0394
2.9717	0.31	5	2.8555	0.0391
2.9341	0.38	6	2.8438	0.0392
3.0376	0.44	7	2.8184	0.0396
2.8164	0.5	8	2.7988	0.0395
3.0857	0.56	9	2.7988	0.0394
2.9492	0.62	10	2.7969	0.0395
2.8633	0.69	11	2.7969	0.0395
2.8994	0.75	12	2.7910	0.0398
3.0024	0.81	13	2.7812	0.0401
2.937	0.88	14	2.7812	0.0399
2.9963	0.94	15	2.7812	0.0399
3.0168	1.0	16	2.7754	0.04
2.2589	1.06	17	2.7715	0.0397
2.2568	1.12	18	2.7793	0.0395
2.3138	1.19	19	2.8027	0.0393
2.2759	1.25	20	2.8184	0.0393
2.5137	1.31	21	2.8262	0.0390
2.2997	1.38	22	2.8320	0.0388
2.2693	1.44	23	2.8359	0.0392
2.204	1.5	24	2.8379	0.0387
2.3713	1.56	25	2.8359	0.0391
2.3448	1.62	26	2.8340	0.0391
2.217	1.69	27	2.8359	0.0391
2.3082	1.75	28	2.8379	0.0385
2.2878	1.81	29	2.8379	0.0386
2.2429	1.88	30	2.8379	0.0385
2.2838	1.94	31	2.8359	0.0385
2.4038	2.0	32	2.8379	0.0387
1.8481	2.06	33	2.8555	0.0384
1.657	2.12	34	2.8965	0.0382
1.6996	2.19	35	2.9590	0.0380
1.6741	2.25	36	3.0312	0.0379
1.594	2.31	37	3.0410	0.0380
1.5201	2.38	38	3.0156	0.0381
1.5149	2.44	39	3.0137	0.0380
1.5521	2.5	40	3.0176	0.0379
1.5364	2.56	41	3.0273	0.0378
1.5385	2.62	42	3.0391	0.0380
1.4794	2.69	43	3.0488	0.0380
1.4313	2.75	44	3.0527	0.0378
1.5071	2.81	45	3.0469	0.0378
1.4799	2.88	46	3.0449	0.0378
1.521	2.94	47	3.0371	0.0380
1.4603	3.0	48	3.0410	0.0379
1.25	3.06	49	3.0859	0.0381
1.0411	3.12	50	3.1797	0.0375
1.0385	3.19	51	3.2969	0.0371
1.0254	3.25	52	3.3613	0.0367
0.9656	3.31	53	3.3633	0.0368
1.036	3.38	54	3.3359	0.0366
0.9366	3.44	55	3.2949	0.0366
0.9712	3.5	56	3.2695	0.0367
1.0066	3.56	57	3.2676	0.0366
0.9952	3.62	58	3.2773	0.0368
1.0352	3.69	59	3.2891	0.0367
1.0212	3.75	60	3.3164	0.0362
0.9468	3.81	61	3.3203	0.0360
0.9155	3.88	62	3.3223	0.0366
0.8552	3.94	63	3.3262	0.0370
0.9575	4.0	64	3.3340	0.0370
0.6384	4.06	65	3.375	0.0370
0.6436	4.12	66	3.4453	0.0364
0.5752	4.19	67	3.5391	0.0358
0.6542	4.25	68	3.6016	0.0354
0.6724	4.31	69	3.6016	0.0354
0.591	4.38	70	3.5938	0.0359
0.5346	4.44	71	3.5801	0.0361
0.5112	4.5	72	3.5762	0.0361
0.5443	4.56	73	3.5840	0.0362
0.5689	4.62	74	3.6152	0.0358
0.5667	4.69	75	3.6328	0.0358
0.554	4.75	76	3.6348	0.0357
0.6087	4.81	77	3.625	0.0355
0.5236	4.88	78	3.6152	0.0355
0.5458	4.94	79	3.5781	0.0355
0.5702	5.0	80	3.5488	0.0355

Framework versions

• Transformers 4.25.0.dev0
• Pytorch 1.12.1+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1