best_model_test_0423_small

This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.6341
Rouge1: 18.7681
Rouge2: 6.3762
Rougel: 18.6081
Rougelsum: 18.6173
Gen Len: 22.1086

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: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
5.8165	0.05	1000	3.6541	11.6734	3.9865	11.5734	11.5375	18.0056
4.306	0.1	2000	3.4291	12.0417	3.8419	11.9231	11.9223	16.8948
4.1091	0.16	3000	3.3643	13.661	4.5171	13.5123	13.5076	19.4016
3.9637	0.21	4000	3.2574	13.8443	4.1761	13.689	13.6927	18.4288
3.8205	0.26	5000	3.2434	13.5371	4.3639	13.3551	13.3552	21.5776
3.7262	0.31	6000	3.1690	14.3668	4.8048	14.2191	14.1906	21.5548
3.6887	0.36	7000	3.0657	14.3265	4.436	14.212	14.205	20.89
3.6337	0.42	8000	3.0318	14.6809	4.8345	14.5378	14.5331	20.3651
3.5443	0.47	9000	3.0554	15.3372	4.9163	15.1794	15.1781	21.7742
3.5203	0.52	10000	2.9793	14.9278	4.9656	14.7491	14.743	20.8113
3.4936	0.57	11000	3.0079	15.7705	5.1453	15.5582	15.5756	23.4274
3.4592	0.62	12000	2.9721	15.0201	5.1612	14.8508	14.8198	22.7007
3.377	0.67	13000	3.0112	15.9595	5.1133	15.78	15.7774	23.4427
3.4158	0.73	14000	2.9239	14.7984	5.051	14.6943	14.6581	21.6009
3.378	0.78	15000	2.8897	16.5128	5.1923	16.3523	16.3265	22.0828
3.3231	0.83	16000	2.9347	16.9997	5.5524	16.8534	16.8737	22.5807
3.3268	0.88	17000	2.9116	16.0261	5.4226	15.9234	15.914	23.6988
3.3127	0.93	18000	2.8610	16.6255	5.3554	16.4729	16.4569	22.9481
3.2664	0.99	19000	2.8606	17.7703	5.9475	17.6229	17.6259	23.4423
3.1718	1.04	20000	2.8764	17.301	5.6262	17.122	17.1104	23.0093
3.0987	1.09	21000	2.8282	16.4718	5.2077	16.3394	16.3401	20.9697
3.1486	1.14	22000	2.8235	18.5594	5.9469	18.3882	18.3799	22.7291
3.1435	1.19	23000	2.8261	18.111	6.0309	17.9593	17.9613	22.9612
3.1049	1.25	24000	2.8068	17.124	5.5675	16.9714	16.9876	22.5558
3.1357	1.3	25000	2.8014	17.3916	5.8671	17.2148	17.2502	23.0075
3.0904	1.35	26000	2.7790	17.419	5.6689	17.3125	17.3058	22.1492
3.0877	1.4	27000	2.7462	17.0605	5.4735	16.9414	16.9378	21.7522
3.0694	1.45	28000	2.7563	17.752	5.8889	17.5967	17.619	23.2005
3.0498	1.51	29000	2.7521	17.9056	5.7754	17.7624	17.7836	21.9369
3.0566	1.56	30000	2.7468	18.6531	6.0538	18.5397	18.5038	22.2358
3.0489	1.61	31000	2.7450	18.4869	5.9297	18.3139	18.3169	22.0108
3.0247	1.66	32000	2.7449	18.5192	5.9966	18.3721	18.3569	22.2071
2.9877	1.71	33000	2.7160	18.1655	5.9294	18.0304	18.0836	21.4595
3.0383	1.76	34000	2.7202	18.4959	6.2413	18.3363	18.3431	22.9732
3.041	1.82	35000	2.6948	17.5306	5.8119	17.4011	17.4149	21.9435
2.9285	1.87	36000	2.6957	18.6418	6.1394	18.514	18.4823	22.5174
3.0556	1.92	37000	2.7000	18.7387	6.0585	18.5761	18.574	22.9315
3.0033	1.97	38000	2.6974	17.9387	6.1387	17.8271	17.8111	22.4726
2.9207	2.02	39000	2.6998	18.6073	6.1906	18.3891	18.4103	23.0274
2.8922	2.08	40000	2.6798	18.4017	6.2244	18.2321	18.2296	22.0697
2.8938	2.13	41000	2.6666	18.8016	6.2066	18.6411	18.6353	21.7017
2.9124	2.18	42000	2.6606	18.7544	6.3533	18.5923	18.5739	21.4303
2.8597	2.23	43000	2.6947	18.8672	6.4526	18.7416	18.7482	22.3352
2.8435	2.28	44000	2.6738	18.9405	6.356	18.7791	18.7729	21.9081
2.8672	2.34	45000	2.6734	18.7509	6.3991	18.6175	18.5828	21.8869
2.899	2.39	46000	2.6575	18.5529	6.3489	18.4139	18.401	21.7694
2.8616	2.44	47000	2.6485	18.7563	6.268	18.6368	18.6253	21.5685
2.8937	2.49	48000	2.6486	18.6525	6.3426	18.5184	18.5129	22.3337
2.8446	2.54	49000	2.6572	18.6529	6.2655	18.4915	18.4764	22.3331
2.8676	2.59	50000	2.6608	19.0913	6.494	18.929	18.9233	22.132
2.8794	2.65	51000	2.6583	18.7648	6.459	18.6276	18.6125	22.2414
2.8836	2.7	52000	2.6512	18.7243	6.3865	18.5848	18.5763	22.2551
2.8174	2.75	53000	2.6409	18.9393	6.3914	18.7733	18.7715	22.1243
2.8494	2.8	54000	2.6396	18.6126	6.4389	18.4673	18.4516	21.7638
2.9025	2.85	55000	2.6341	18.7681	6.3762	18.6081	18.6173	22.1086
2.8754	2.91	56000	2.6388	19.0828	6.5203	18.9334	18.9285	22.3497
2.8489	2.96	57000	2.6375	18.9219	6.4922	18.763	18.7437	21.9321

Framework versions

Transformers 4.18.0
Pytorch 1.10.1+cu113
Datasets 2.0.0
Tokenizers 0.11.6