t5_recommendation_sports_equipment_english2

This model is a fine-tuned version of t5-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5359
• Rouge1: 74.1270
• Rouge2: 66.6667
• Rougel: 74.1270
• Rougelsum: 73.8095
• Gen Len: 4.0476

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: 0.0001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 80

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	1	9.9716	12.4868	0.0	12.5845	12.5051	19.0
No log	2.0	2	10.1466	9.9134	0.0	9.9471	9.8413	19.0
No log	3.0	3	8.3378	10.5739	0.0	10.6349	10.5291	19.0
No log	4.0	4	7.3021	10.5739	0.0	10.6349	10.5291	19.0
No log	5.0	5	6.3242	10.4605	0.0	10.5471	10.4567	19.0
No log	6.0	6	5.4331	10.2886	0.7937	10.2319	10.3793	19.0
No log	7.0	7	4.7152	10.8989	0.7937	10.8388	10.9525	18.9524
No log	8.0	8	3.9937	13.9421	3.7009	14.0590	13.9456	15.0952
No log	9.0	9	3.1163	16.0431	1.0025	15.7736	15.9707	6.4762
No log	10.0	10	2.3306	23.1746	7.1429	22.8571	23.6508	4.1429
No log	11.0	11	1.9695	21.2698	7.1429	20.9524	21.4286	4.0476
No log	12.0	12	1.5552	23.8095	7.1429	23.3333	23.8095	3.9048
No log	13.0	13	0.8986	9.0476	0.0	9.0476	9.0476	3.7619
No log	14.0	14	0.7398	17.4603	2.3810	18.2540	17.4603	4.1905
No log	15.0	15	0.6966	12.6984	0.0	12.6984	12.6984	3.6667
No log	16.0	16	0.6352	32.5397	14.2857	32.5397	32.5397	3.7619
No log	17.0	17	0.5722	43.6508	23.8095	43.6508	42.8571	4.0952
No log	18.0	18	0.5628	43.6508	23.8095	43.6508	42.8571	3.8571
No log	19.0	19	0.5526	43.1746	23.8095	43.1746	42.8571	3.8571
No log	20.0	20	0.5522	48.4127	38.0952	48.4127	48.4127	3.7619
No log	21.0	21	0.5201	42.8571	28.5714	42.8571	42.3810	4.2381
No log	22.0	22	0.5262	37.1429	19.0476	36.9841	36.9841	4.2857
No log	23.0	23	0.5093	37.6190	23.8095	37.6190	37.6190	4.1429
No log	24.0	24	0.4818	45.3175	33.3333	45.2381	45.2381	4.1429
No log	25.0	25	0.4547	50.7937	38.0952	50.7937	50.7937	4.1429
No log	26.0	26	0.4455	50.7937	38.0952	50.7937	50.7937	4.1429
No log	27.0	27	0.4660	53.1746	42.8571	53.1746	53.1746	4.0476
No log	28.0	28	0.4825	53.1746	42.8571	53.1746	53.1746	4.0
No log	29.0	29	0.4928	53.1746	42.8571	53.1746	53.1746	4.0476
No log	30.0	30	0.4838	57.7778	42.8571	57.2222	57.5397	4.0476
No log	31.0	31	0.4955	60.3175	47.6190	60.3175	60.3175	4.0476
No log	32.0	32	0.5066	62.6984	52.3810	62.6984	62.6984	4.1429
No log	33.0	33	0.5189	62.6984	52.3810	62.6984	62.6984	4.1905
No log	34.0	34	0.5234	62.6984	52.3810	62.6984	62.6984	4.1905
No log	35.0	35	0.5225	62.6984	52.3810	62.6984	62.6984	4.1905
No log	36.0	36	0.5225	62.6984	52.3810	62.6984	62.6984	4.1905
No log	37.0	37	0.5058	62.8571	52.3810	62.2222	62.6984	4.1429
No log	38.0	38	0.4861	69.8413	61.9048	69.8413	69.8413	4.1905
No log	39.0	39	0.4625	69.8413	61.9048	69.8413	69.8413	4.1905
No log	40.0	40	0.4438	72.2222	66.6667	72.2222	72.2222	4.0952
No log	41.0	41	0.4231	72.2222	66.6667	72.2222	72.2222	4.0952
No log	42.0	42	0.4073	72.2222	66.6667	72.2222	72.2222	4.0952
No log	43.0	43	0.3938	72.2222	66.6667	72.2222	72.2222	4.0952
No log	44.0	44	0.3912	72.2222	66.6667	72.2222	72.2222	4.0952
No log	45.0	45	0.3980	72.2222	66.6667	72.2222	72.2222	4.1429
No log	46.0	46	0.4062	72.2222	66.6667	72.2222	72.2222	4.1905
No log	47.0	47	0.4121	76.9841	71.4286	76.9841	76.9841	4.2857
No log	48.0	48	0.4150	76.9841	71.4286	76.9841	76.9841	4.1905
No log	49.0	49	0.4183	76.9841	71.4286	76.9841	76.9841	4.1429
No log	50.0	50	0.4205	76.9841	71.4286	76.9841	76.9841	4.1905
No log	51.0	51	0.4306	79.3651	76.1905	79.3651	79.3651	4.0952
No log	52.0	52	0.4411	76.5079	71.4286	76.5079	76.1905	4.0
No log	53.0	53	0.4526	76.5079	71.4286	76.5079	76.1905	4.0476
No log	54.0	54	0.4667	76.5079	71.4286	76.5079	76.1905	4.0
No log	55.0	55	0.4871	76.5079	71.4286	76.5079	76.1905	4.0
No log	56.0	56	0.5063	76.5079	71.4286	76.5079	76.1905	4.0
No log	57.0	57	0.5196	76.5079	71.4286	76.5079	76.1905	4.0
No log	58.0	58	0.5265	76.5079	71.4286	76.5079	76.1905	3.9524
No log	59.0	59	0.5308	76.5079	71.4286	76.5079	76.1905	3.9524
No log	60.0	60	0.5333	76.5079	71.4286	76.5079	76.1905	3.9524
No log	61.0	61	0.5344	76.5079	71.4286	76.5079	76.1905	3.9524
No log	62.0	62	0.5348	76.5079	71.4286	76.5079	76.1905	3.9524
No log	63.0	63	0.5354	76.5079	71.4286	76.5079	76.1905	3.9524
No log	64.0	64	0.5359	76.5079	71.4286	76.5079	76.1905	3.9524
No log	65.0	65	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	66.0	66	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	67.0	67	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	68.0	68	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	69.0	69	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	70.0	70	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	71.0	71	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	72.0	72	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	73.0	73	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	74.0	74	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	75.0	75	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	76.0	76	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	77.0	77	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	78.0	78	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	79.0	79	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476
No log	80.0	80	0.5359	74.1270	66.6667	74.1270	73.8095	4.0476

Framework versions

• Transformers 4.26.0
• Pytorch 2.3.0+cu121
• Datasets 2.8.0
• Tokenizers 0.13.3