meeting-sensai-2

raquelclemente/meeting-sensai-2

meeting-sensai-2 is a machine learning model from raquelclemente. 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: mt5
Access: Public
Created: May 8, 2023
Updated: May 8, 2023
SHA: a7a50819

Model card

From the Hugging Face model README

meeting-sensai-2

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

Loss: 2.7555
Rouge1: 0.3039
Rouge2: 0.1453
Rougel: 0.2534
Rougelsum: 0.2529

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: 0.0005
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
No log	0.09	30	3.1865	0.1936	0.0509	0.1606	0.1611
No log	0.18	60	2.9928	0.2636	0.0961	0.1951	0.1969
No log	0.28	90	2.9472	0.2667	0.0921	0.2097	0.2115
No log	0.37	120	2.8506	0.2755	0.1151	0.2243	0.2243
No log	0.46	150	2.8883	0.2505	0.1134	0.2057	0.2056
No log	0.55	180	2.8464	0.2946	0.1229	0.2449	0.2446
No log	0.65	210	2.8274	0.2668	0.1005	0.2112	0.2120
No log	0.74	240	2.8153	0.2960	0.1268	0.2392	0.2384
No log	0.83	270	2.7803	0.2940	0.1203	0.2323	0.2312
No log	0.92	300	2.8129	0.2966	0.1083	0.2447	0.2444
No log	1.02	330	2.7478	0.2977	0.1152	0.2388	0.2382
No log	1.11	360	2.7482	0.2905	0.1135	0.2379	0.2369
No log	1.2	390	2.7646	0.3215	0.1260	0.2590	0.2594
No log	1.29	420	2.7763	0.3164	0.1273	0.2536	0.2524
No log	1.38	450	2.8300	0.2867	0.1198	0.2182	0.2186
No log	1.48	480	2.7683	0.3313	0.1567	0.2647	0.2643
2.7437	1.57	510	2.7669	0.3004	0.1313	0.2545	0.2541
2.7437	1.66	540	2.7242	0.2960	0.1394	0.2404	0.2409
2.7437	1.75	570	2.7565	0.3042	0.1172	0.2475	0.2463
2.7437	1.85	600	2.7866	0.2994	0.1300	0.2375	0.2373
2.7437	1.94	630	2.7293	0.3122	0.1306	0.2611	0.2581
2.7437	2.03	660	2.7398	0.3194	0.1314	0.2504	0.2506
2.7437	2.12	690	2.7183	0.3109	0.1374	0.2591	0.2588
2.7437	2.22	720	2.7929	0.3184	0.1454	0.2562	0.2562
2.7437	2.31	750	2.8156	0.3360	0.1566	0.2613	0.2611
2.7437	2.4	780	2.7750	0.3125	0.1364	0.2496	0.2494
2.7437	2.49	810	2.8071	0.2928	0.1434	0.2501	0.2491
2.7437	2.58	840	2.7322	0.3043	0.1403	0.2488	0.2484
2.7437	2.68	870	2.7449	0.3006	0.1437	0.2521	0.2516
2.7437	2.77	900	2.7425	0.3029	0.1479	0.2545	0.2543
2.7437	2.86	930	2.7242	0.3028	0.1355	0.2318	0.2307
2.7437	2.95	960	2.7232	0.3100	0.1474	0.2449	0.2443
2.7437	3.05	990	2.7787	0.3036	0.1457	0.2465	0.2467
2.1872	3.14	1020	2.7759	0.2957	0.1371	0.2394	0.2375
2.1872	3.23	1050	2.7896	0.3105	0.1391	0.2403	0.2375
2.1872	3.32	1080	2.7724	0.3121	0.1498	0.2452	0.2453
2.1872	3.42	1110	2.7639	0.3204	0.1534	0.2556	0.2563
2.1872	3.51	1140	2.7673	0.3103	0.1541	0.2529	0.2521
2.1872	3.6	1170	2.7644	0.3059	0.1399	0.2450	0.2448
2.1872	3.69	1200	2.7443	0.3186	0.1484	0.2554	0.2562
2.1872	3.78	1230	2.7517	0.3045	0.1444	0.2522	0.2519
2.1872	3.88	1260	2.7501	0.3039	0.1453	0.2534	0.2529
2.1872	3.97	1290	2.7555	0.3039	0.1453	0.2534	0.2529

Framework versions

Transformers 4.26.1
Pytorch 1.13.0
Datasets 2.1.0
Tokenizers 0.13.2

Library: transformers
Type: mt5
Created: May 8, 2023
Updated: May 8, 2023