text_summarization-finetuned-stocknews_1900_100

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

• Loss: 1.6071
• Rouge1: 15.4764
• Rouge2: 7.3425
• Rougel: 13.0298
• Rougelsum: 14.3613
• 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	1.5996	15.7162	7.3225	13.1679	14.5316	19.0
No log	2.0	204	1.5991	15.7364	7.3916	13.2205	14.5865	19.0
No log	3.0	306	1.5948	15.7337	7.4936	13.2031	14.5941	19.0
No log	4.0	408	1.5935	15.7661	7.4892	13.1138	14.5123	19.0
1.4093	5.0	510	1.5972	15.6328	7.2837	13.1138	14.4789	19.0
1.4093	6.0	612	1.6016	15.5382	7.3117	13.0203	14.3907	19.0
1.4093	7.0	714	1.5983	15.5582	7.2532	12.9421	14.3971	19.0
1.4093	8.0	816	1.6039	15.5287	7.3152	13.002	14.3652	19.0
1.4093	9.0	918	1.6016	15.5916	7.3367	13.0811	14.442	19.0
1.3525	10.0	1020	1.6017	15.749	7.6355	13.1754	14.6339	19.0
1.3525	11.0	1122	1.5992	15.6529	7.5216	13.1041	14.5668	19.0
1.3525	12.0	1224	1.5977	15.64	7.3843	13.0609	14.5366	19.0
1.3525	13.0	1326	1.5993	15.6516	7.4595	13.1143	14.5799	19.0
1.3525	14.0	1428	1.6040	15.6532	7.5787	13.0764	14.5464	19.0
1.3156	15.0	1530	1.5998	15.4999	7.349	13.016	14.4233	19.0
1.3156	16.0	1632	1.6039	15.4718	7.2392	12.9167	14.3196	19.0
1.3156	17.0	1734	1.6026	15.5434	7.376	12.9885	14.3673	19.0
1.3156	18.0	1836	1.6008	15.4092	7.2119	12.9495	14.286	19.0
1.3156	19.0	1938	1.6009	15.4604	7.4049	13.0264	14.3634	19.0
1.2849	20.0	2040	1.6028	15.4735	7.3749	12.9979	14.3637	19.0
1.2849	21.0	2142	1.6025	15.617	7.5495	13.0912	14.4945	19.0
1.2849	22.0	2244	1.6061	15.65	7.6043	13.119	14.5419	19.0
1.2849	23.0	2346	1.6039	15.5747	7.5283	13.0601	14.4706	19.0
1.2849	24.0	2448	1.6071	15.4923	7.4246	12.9747	14.3495	19.0
1.2625	25.0	2550	1.6030	15.5403	7.4373	13.1005	14.4791	19.0
1.2625	26.0	2652	1.6044	15.5232	7.4625	13.049	14.4455	19.0
1.2625	27.0	2754	1.6038	15.4961	7.4241	13.0409	14.4496	19.0
1.2625	28.0	2856	1.6048	15.5079	7.551	13.0814	14.4369	19.0
1.2625	29.0	2958	1.6067	15.4629	7.4087	13.0123	14.3897	19.0
1.2418	30.0	3060	1.6052	15.5104	7.518	13.0891	14.4284	19.0
1.2418	31.0	3162	1.6051	15.5104	7.4773	13.0686	14.4114	19.0
1.2418	32.0	3264	1.6044	15.5491	7.5342	13.1145	14.4742	19.0
1.2418	33.0	3366	1.6064	15.5321	7.4773	13.0686	14.4336	19.0
1.2418	34.0	3468	1.6055	15.5193	7.5178	13.0887	14.4521	19.0
1.2313	35.0	3570	1.6057	15.4739	7.4526	13.0326	14.3947	19.0
1.2313	36.0	3672	1.6057	15.4486	7.3244	12.9881	14.3346	19.0
1.2313	37.0	3774	1.6067	15.4764	7.3795	13.0402	14.3886	19.0
1.2313	38.0	3876	1.6072	15.4594	7.3028	12.9813	14.3339	19.0
1.2313	39.0	3978	1.6070	15.4764	7.3795	13.0402	14.3886	19.0
1.2274	40.0	4080	1.6071	15.4764	7.3425	13.0298	14.3613	19.0

Framework versions

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