ingredient_prune

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

• Loss: 0.0255
• Rouge1: 88.3061
• Rouge2: 76.6099
• Rougel: 88.3242
• Rougelsum: 88.2429
• Gen Len: 10.5872

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.9499	0.09	10	1.3100	33.1645	23.9561	32.6647	32.7137	14.7431
1.9454	0.18	20	0.6787	30.1119	21.203	29.5079	29.6061	13.8349
1.309	0.28	30	0.5147	25.3399	17.694	24.4102	24.4425	11.6514
1.0307	0.37	40	0.4398	17.4522	11.66	16.2846	16.3817	8.5413
0.9574	0.46	50	0.4302	16.6745	10.6799	15.8568	16.4301	8.0092
0.7183	0.55	60	0.3818	14.4343	9.4646	13.9825	14.1979	6.9725
0.5636	0.64	70	0.3096	9.4156	5.2844	9.0143	9.239	5.5596
0.4603	0.73	80	0.2664	8.6106	4.7574	7.9285	8.4429	5.0917
0.4607	0.83	90	0.2319	6.7868	3.9309	6.1844	6.7007	3.8349
0.352	0.92	100	0.1991	6.2965	3.5572	5.3616	5.9941	3.2661
0.3426	1.01	110	0.1735	6.1795	3.1174	5.3783	5.9261	3.3119
0.2901	1.1	120	0.1553	5.5031	2.739	4.9926	5.5079	3.1376
0.3619	1.19	130	0.1452	4.1403	1.8462	4.0877	4.1877	3.0092
0.2509	1.28	140	0.1338	4.1399	1.8019	3.9836	4.1506	2.9541
0.1938	1.38	150	0.1187	2.9515	1.2174	2.7845	3.0192	2.2569
0.1987	1.47	160	0.1068	4.8991	3.4459	4.7552	4.9489	2.1284
0.1702	1.56	170	0.0983	8.7082	5.5788	8.5531	8.8267	3.4587
0.1535	1.65	180	0.0871	11.5572	7.6669	11.4688	11.5381	4.6972
0.1629	1.74	190	0.0771	16.33	11.587	16.0842	16.1965	6.6055
0.1618	1.83	200	0.0690	21.4186	14.9296	21.2789	21.2002	8.367
0.1617	1.93	210	0.0628	27.6198	19.8907	27.4479	27.4515	10.3394
0.1136	2.02	220	0.0572	36.7416	28.2903	36.7181	36.719	12.3578
0.1278	2.11	230	0.0526	46.9007	36.6481	47.1002	46.8623	13.7064
0.0915	2.2	240	0.0486	56.1238	45.5624	56.3372	56.0369	14.1101
0.0736	2.29	250	0.0448	63.3857	51.8889	63.6163	63.2685	13.4771
0.0855	2.39	260	0.0420	72.669	59.9359	72.7393	72.6055	12.3486
0.0921	2.48	270	0.0388	78.2224	65.2581	78.2789	77.9532	11.3578
0.077	2.57	280	0.0364	82.3881	68.397	82.4999	82.3175	10.5872
0.0848	2.66	290	0.0347	85.4014	72.793	85.495	85.3917	10.633
0.0978	2.75	300	0.0332	86.0947	72.9678	86.1325	86.0028	10.5138
0.0635	2.84	310	0.0323	86.158	73.833	86.2727	86.1471	10.5596
0.0555	2.94	320	0.0314	86.0306	73.8297	86.0421	85.9571	10.5688
0.0792	3.03	330	0.0305	87.5066	75.3885	87.6496	87.3874	10.3761
0.0536	3.12	340	0.0297	88.0844	75.8754	88.1956	87.9164	10.4954
0.063	3.21	350	0.0290	88.0844	75.8754	88.1956	87.9164	10.4954
0.0563	3.3	360	0.0283	88.0783	75.989	88.2233	87.9578	10.5138
0.0547	3.39	370	0.0279	88.1265	76.3196	88.3078	88.0765	10.6147
0.0635	3.49	380	0.0275	86.9846	74.8237	87.0556	86.9021	10.5872
0.0835	3.58	390	0.0271	86.933	75.3277	87.0357	86.931	10.6147
0.0628	3.67	400	0.0269	87.5981	75.5811	87.6905	87.4594	10.6789
0.0554	3.76	410	0.0267	88.0124	76.5633	88.174	87.9292	10.578
0.0342	3.85	420	0.0266	88.0124	76.5633	88.174	87.9292	10.578
0.0396	3.94	430	0.0263	88.0064	76.6947	88.1712	87.9434	10.5872
0.045	4.04	440	0.0262	87.7466	76.3605	87.8932	87.6273	10.5505
0.0566	4.13	450	0.0262	87.8577	76.5633	88.0399	87.7835	10.6055
0.0582	4.22	460	0.0261	87.8103	76.1351	87.9277	87.7032	10.6697
0.051	4.31	470	0.0260	87.8103	76.1351	87.9277	87.7032	10.6697
0.0398	4.4	480	0.0258	88.1974	76.4006	88.2158	88.0622	10.6789
0.0364	4.5	490	0.0257	88.3353	76.5513	88.3291	88.2557	10.633
0.0498	4.59	500	0.0257	88.4083	76.5513	88.4132	88.35	10.6147
0.0406	4.68	510	0.0256	88.3061	76.6099	88.3242	88.2429	10.5872
0.0403	4.77	520	0.0256	88.3061	76.6099	88.3242	88.2429	10.5872
0.0421	4.86	530	0.0255	88.3061	76.6099	88.3242	88.2429	10.5872
0.0271	4.95	540	0.0255	88.3061	76.6099	88.3242	88.2429	10.5872

Framework versions

• Transformers 4.38.2
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.2