t5-base-finetuned-stocknews_1900_100

This model is a fine-tuned version of google-t5/t5-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.2997
• Rouge1: 16.6203
• Rouge2: 8.7831
• Rougel: 13.9116
• Rougelsum: 15.4831
• Gen Len: 19.0

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 40
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	102	1.5488	14.6381	6.8963	12.1802	13.6527	19.0
No log	2.0	204	1.4139	15.0451	6.9216	12.6068	14.1445	19.0
No log	3.0	306	1.3627	15.3864	7.115	12.6537	14.267	19.0
No log	4.0	408	1.3288	15.6891	7.5106	13.0451	14.6203	19.0
1.8685	5.0	510	1.3087	15.8071	7.6382	13.103	14.7587	19.0
1.8685	6.0	612	1.2938	15.6775	7.6448	13.0823	14.6034	19.0
1.8685	7.0	714	1.2870	15.7672	7.89	13.3325	14.7821	19.0
1.8685	8.0	816	1.2779	16.1616	8.1642	13.4471	15.0305	19.0
1.8685	9.0	918	1.2731	16.3679	8.4804	13.7618	15.3468	19.0
1.1991	10.0	1020	1.2695	16.2821	8.456	13.7692	15.2461	19.0
1.1991	11.0	1122	1.2647	16.4056	8.5019	13.7217	15.3711	19.0
1.1991	12.0	1224	1.2667	16.4259	8.6692	13.8396	15.4122	19.0
1.1991	13.0	1326	1.2654	16.6988	8.9574	14.0239	15.6864	19.0
1.1991	14.0	1428	1.2648	16.7394	9.0588	14.0529	15.6644	19.0
1.0382	15.0	1530	1.2642	16.6864	9.106	13.9046	15.5687	19.0
1.0382	16.0	1632	1.2662	16.6786	8.8288	13.9603	15.5724	19.0
1.0382	17.0	1734	1.2651	16.7446	8.9211	13.9999	15.6617	19.0
1.0382	18.0	1836	1.2702	16.6361	8.8503	14.0324	15.546	19.0
1.0382	19.0	1938	1.2676	16.7046	9.0089	14.073	15.6342	19.0
0.9273	20.0	2040	1.2732	16.4339	8.6714	13.8422	15.44	19.0
0.9273	21.0	2142	1.2743	16.5655	8.7747	13.839	15.4958	19.0
0.9273	22.0	2244	1.2781	16.7749	8.9154	14.1216	15.6395	19.0
0.9273	23.0	2346	1.2814	16.535	8.7436	13.971	15.5056	19.0
0.9273	24.0	2448	1.2795	16.6612	8.7045	14.0096	15.5692	19.0
0.8539	25.0	2550	1.2844	16.6083	8.6106	13.9202	15.5641	19.0
0.8539	26.0	2652	1.2817	16.6449	8.8127	14.0562	15.5792	19.0
0.8539	27.0	2754	1.2856	16.6185	8.7475	14.0134	15.5439	19.0
0.8539	28.0	2856	1.2868	16.4913	8.7293	13.9367	15.4702	19.0
0.8539	29.0	2958	1.2905	16.4887	8.6461	13.8893	15.4342	19.0
0.8006	30.0	3060	1.2893	16.5861	8.695	13.9081	15.4307	19.0
0.8006	31.0	3162	1.2919	16.5972	8.8314	13.9069	15.4967	19.0
0.8006	32.0	3264	1.2940	16.5957	8.789	13.9202	15.4839	19.0
0.8006	33.0	3366	1.2946	16.6313	8.8011	13.9684	15.5256	19.0
0.8006	34.0	3468	1.2945	16.6711	8.8915	14.0228	15.5394	19.0
0.7598	35.0	3570	1.2970	16.67	8.891	13.9749	15.5174	19.0
0.7598	36.0	3672	1.2975	16.6223	8.7522	13.9528	15.4761	19.0
0.7598	37.0	3774	1.2987	16.6444	8.8594	13.9567	15.5117	19.0
0.7598	38.0	3876	1.2993	16.6444	8.8594	13.9567	15.5117	19.0
0.7598	39.0	3978	1.2996	16.6196	8.8108	13.9213	15.4806	19.0
0.7463	40.0	4080	1.2997	16.6203	8.7831	13.9116	15.4831	19.0

Framework versions

• Transformers 4.38.2
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2