README.md

metadata

tags:
  - simplification
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: NASES-clara-med
    results: []

NASES-clara-med

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

• Loss: 3.2763
• Rouge1: 42.9986
• Rouge2: 25.2365
• Rougel: 37.0782
• Rougelsum: 37.278

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	2.1704	42.0923	24.011	36.2317	36.3819
No log	2.0	380	2.1260	42.3364	24.6464	36.8836	37.0023
1.9093	3.0	570	2.1582	43.7464	26.0481	38.1321	38.2575
1.9093	4.0	760	2.2436	43.1348	25.6313	37.5276	37.688
0.7294	5.0	950	2.3852	43.9276	26.2853	38.1775	38.3529
0.7294	6.0	1140	2.5096	42.6241	25.1825	36.9084	37.1236
0.7294	7.0	1330	2.5986	43.4603	25.7703	37.762	38.0026
0.2438	8.0	1520	2.6878	42.483	24.6796	36.7012	36.9424
0.2438	9.0	1710	2.7096	43.3953	25.6418	37.4906	37.8048
0.1422	10.0	1900	2.7879	43.1926	25.3773	37.2548	37.4858
0.1422	11.0	2090	2.8629	43.7788	25.7912	37.6712	37.8664
0.1422	12.0	2280	2.9139	43.5132	25.6003	37.5426	37.7154
0.0911	13.0	2470	2.9267	43.2335	25.5807	37.4857	37.6547
0.0911	14.0	2660	2.9826	42.4726	24.6801	36.8142	36.9149
0.0704	15.0	2850	2.9834	42.7464	25.0051	37.0043	37.188
0.0704	16.0	3040	3.0423	42.7331	25.1076	36.8757	37.1165
0.0704	17.0	3230	3.0602	43.5046	25.9845	37.9281	38.0868
0.0529	18.0	3420	3.0882	42.7186	25.0104	36.943	37.1559
0.0529	19.0	3610	3.0713	43.0051	25.3356	37.0809	37.2836
0.0383	20.0	3800	3.1547	43.2239	25.3545	37.2722	37.4304
0.0383	21.0	3990	3.1408	43.2171	25.266	37.1733	37.4219
0.0383	22.0	4180	3.1739	43.1094	25.2674	37.3491	37.5596
0.0252	23.0	4370	3.2036	43.0451	25.3833	37.2896	37.469
0.0252	24.0	4560	3.2291	43.2983	25.5308	37.6024	37.7772
0.0173	25.0	4750	3.2607	43.0005	25.0403	37.2126	37.367
0.0173	26.0	4940	3.2498	42.869	24.9531	37.0616	37.2307
0.0173	27.0	5130	3.3016	43.1913	25.1199	37.2238	37.4256
0.0135	28.0	5320	3.2813	43.1867	25.2193	37.2014	37.4029
0.0135	29.0	5510	3.2757	42.9765	25.2217	37.0312	37.2317
0.0113	30.0	5700	3.2763	42.9986	25.2365	37.0782	37.278

Framework versions

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