mt5-small-clara-med

This model is a fine-tuned version of google/mt5-small on the CLARA-MeD dataset. It achieves the following results on the evaluation set:

• Loss: 1.9850
• Rouge1: 33.0363
• Rouge2: 19.0613
• Rougel: 30.295
• Rougelsum: 30.2898
• SARI: 40.7094

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: 5.6e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
No log	1.0	190	3.0286	18.0709	7.727	16.1995	16.3348
No log	2.0	380	2.4754	24.9167	13.0501	22.3889	22.4724
6.79	3.0	570	2.3542	29.9908	15.9829	26.3751	26.4343
6.79	4.0	760	2.2894	30.4435	16.3176	27.1801	27.1926
3.1288	5.0	950	2.2440	30.8602	16.8033	27.8195	27.8355
3.1288	6.0	1140	2.1772	31.4202	17.3253	28.3394	28.3699
3.1288	7.0	1330	2.1584	31.5591	17.7302	28.618	28.6189
2.7919	8.0	1520	2.1286	31.6211	17.7423	28.7218	28.7462
2.7919	9.0	1710	2.1031	31.9724	18.017	29.0754	29.0744
2.6007	10.0	1900	2.0947	32.1588	18.2474	29.2957	29.2956
2.6007	11.0	2090	2.0914	32.4959	18.4197	29.6052	29.609
2.6007	12.0	2280	2.0726	32.6673	18.8962	29.9145	29.9122
2.4911	13.0	2470	2.0487	32.4461	18.6804	29.6224	29.6274
2.4911	14.0	2660	2.0436	32.8393	19.0315	30.1024	30.1097
2.4168	15.0	2850	2.0229	32.8235	18.9549	30.0699	30.0605
2.4168	16.0	3040	2.0253	32.8584	18.8602	30.0582	30.0712
2.4168	17.0	3230	2.0177	32.7145	18.9059	30.0436	30.0771
2.3452	18.0	3420	2.0151	32.6874	18.8339	29.9739	30.0004
2.3452	19.0	3610	2.0138	32.516	18.6562	29.7823	29.7951
2.302	20.0	3800	2.0085	32.8117	18.8208	30.0902	30.1282
2.302	21.0	3990	2.0043	32.7633	18.8364	30.0619	30.0781
2.302	22.0	4180	1.9972	32.9786	19.0354	30.2166	30.2286
2.2641	23.0	4370	1.9927	33.0222	19.0501	30.2716	30.2951
2.2641	24.0	4560	1.9905	32.9557	18.9958	30.1988	30.2004
2.2366	25.0	4750	1.9897	33.0429	18.9806	30.2861	30.3012
2.2366	26.0	4940	1.9850	33.047	19.118	30.3437	30.3368
2.2366	27.0	5130	1.9860	33.0736	19.0805	30.3311	30.3476
2.2157	28.0	5320	1.9870	33.0698	19.0649	30.2959	30.3093
2.2157	29.0	5510	1.9844	33.0376	19.0397	30.299	30.2839
2.2131	30.0	5700	1.9850	33.0363	19.0613	30.295	30.2898

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0
• Datasets 2.8.0
• Tokenizers 0.12.1