marimari-r2r-mlsum

CLARA-MeD/marimari-r2r-mlsum

marimari-r2r-mlsum is a machine learning model from CLARA-MeD. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Model type: encoder-decoder
Access: Public
Created: Jan 13, 2023
Updated: Jan 27, 2023
SHA: 64f7da4c

Model card

From the Hugging Face model README

marimari-r2r-mlsum-clara-med

This model is a fine-tuned version of IIC/marimari-r2r-mlsum on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.9618
Rouge1: 42.6764
Rouge2: 24.4569
Rougel: 37.0033
Rougelsum: 37.1595

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	2.3970	40.7426	23.212	35.7093	35.8437
No log	2.0	380	2.3165	42.5676	24.6494	37.1225	37.2619
1.9699	3.0	570	2.4711	42.0346	23.7633	36.3472	36.4433
1.9699	4.0	760	2.7339	41.1717	22.8419	35.3263	35.4823
0.6485	5.0	950	2.9593	40.714	22.6931	34.8859	35.0647
0.6485	6.0	1140	3.1316	41.3218	23.2054	35.3103	35.5063
0.6485	7.0	1330	3.2542	41.2786	23.4853	35.8236	35.972
0.1529	8.0	1520	3.3470	41.2991	22.8385	35.0524	35.2153
0.1529	9.0	1710	3.4324	41.3838	23.1045	35.3472	35.5779
0.0719	10.0	1900	3.5187	42.0833	23.8538	36.3282	36.5294
0.0719	11.0	2090	3.5527	41.2993	23.0323	35.3116	35.4687
0.0719	12.0	2280	3.6624	41.6524	23.8925	35.9281	36.1012
0.0393	13.0	2470	3.6536	41.188	23.2066	35.371	35.5616
0.0393	14.0	2660	3.6656	40.8222	22.5651	35.0515	35.1399
0.0266	15.0	2850	3.7349	41.844	23.7839	36.102	36.3169
0.0266	16.0	3040	3.7254	41.5535	23.3996	35.9619	36.0981
0.0266	17.0	3230	3.7919	41.5683	23.2824	36.0855	36.2475
0.0151	18.0	3420	3.8152	42.1272	24.0548	36.5784	36.785
0.0151	19.0	3610	3.8213	41.9185	23.5975	36.1182	36.3194
0.0087	20.0	3800	3.8501	41.3409	23.0081	35.7662	35.9451
0.0087	21.0	3990	3.8690	41.9496	23.7032	36.0116	36.1843
0.0087	22.0	4180	3.8809	42.5366	24.6413	37.2644	37.459
0.0044	23.0	4370	3.8865	42.4346	24.2278	36.7284	36.8846
0.0044	24.0	4560	3.9044	42.9781	24.8423	37.3582	37.4807
0.0024	25.0	4750	3.9138	42.6738	24.4737	36.8959	37.0031
0.0024	26.0	4940	3.9361	42.5267	24.4155	36.8414	36.9915
0.0024	27.0	5130	3.9477	42.4844	24.5483	36.8857	37.0219
0.0013	28.0	5320	3.9561	42.7199	24.5977	37.1206	37.2374
0.0013	29.0	5510	3.9599	42.7088	24.4474	37.0513	37.1971
0.001	30.0	5700	3.9618	42.6764	24.4569	37.0033	37.1595

Framework versions

Transformers 4.25.1
Pytorch 1.13.0
Datasets 2.8.0
Tokenizers 0.12.1

Library: transformers
Type: encoder-decoder
Created: Jan 13, 2023
Updated: Jan 27, 2023