Training Loss	Epoch	Step	Validation Loss	Hint Hit Num	Hint Precision	Num	Gen Len
2.119	0.4	200	1.7746	1.966	0.3387	5.814	18.99
1.9135	0.8	400	1.7157	1.76	0.3129	5.617	18.987
1.85	1.2	600	1.7140	1.913	0.3327	5.732	18.995
1.7963	1.6	800	1.7022	1.887	0.3291	5.702	18.994
1.7784	2.0	1000	1.6911	1.875	0.3268	5.711	18.989
1.711	2.4	1200	1.6935	1.932	0.3354	5.749	18.994
1.7186	2.8	1400	1.6721	1.979	0.3427	5.791	18.997
1.6704	3.2	1600	1.7007	1.945	0.334	5.792	18.994
1.6484	3.6	1800	1.6900	1.896	0.3282	5.751	18.994
1.6334	4.0	2000	1.6732	1.879	0.3283	5.698	18.994
1.5761	4.4	2200	1.6869	1.97	0.3357	5.861	18.992
1.5882	4.8	2400	1.6784	1.952	0.3354	5.792	18.992
1.558	5.2	2600	1.7012	1.984	0.3394	5.83	19.0
1.5339	5.6	2800	1.7013	1.898	0.3245	5.82	18.991
1.5419	6.0	3000	1.6850	1.952	0.3377	5.766	18.992
1.4884	6.4	3200	1.7009	1.967	0.3375	5.812	18.991
1.4857	6.8	3400	1.7038	1.913	0.3289	5.805	18.992
1.4655	7.2	3600	1.7103	1.956	0.3347	5.82	18.992
1.4578	7.6	3800	1.7235	1.946	0.3318	5.837	18.999
1.443	8.0	4000	1.7176	1.963	0.3347	5.828	18.991
1.42	8.4	4200	1.7305	1.977	0.3404	5.809	18.996
1.4155	8.8	4400	1.7267	1.988	0.3408	5.816	18.997
1.3753	9.2	4600	1.7418	1.992	0.3427	5.804	19.0
1.3853	9.6	4800	1.7360	2.013	0.3461	5.818	18.992
1.3768	10.0	5000	1.7280	1.994	0.3397	5.874	18.992
1.3465	10.4	5200	1.7530	2.01	0.3424	5.855	18.992
1.3445	10.8	5400	1.7416	1.996	0.3438	5.814	18.992
1.3321	11.2	5600	1.7653	2.014	0.3434	5.861	18.992
1.3092	11.6	5800	1.7705	2.007	0.3423	5.861	18.983
1.3263	12.0	6000	1.7617	1.988	0.3412	5.815	18.986
1.2847	12.4	6200	1.7816	1.988	0.3407	5.815	18.992
1.2942	12.8	6400	1.7905	1.987	0.3395	5.83	18.991
1.2784	13.2	6600	1.7795	2.028	0.3436	5.899	18.992
1.2562	13.6	6800	1.7861	1.97	0.3371	5.825	18.989
1.2776	14.0	7000	1.7899	2.02	0.3431	5.871	18.992
1.2524	14.4	7200	1.8054	2.038	0.3435	5.916	18.992
1.2402	14.8	7400	1.8072	2.034	0.3459	5.872	18.995
1.2352	15.2	7600	1.8123	2.014	0.3431	5.861	18.987
1.2195	15.6	7800	1.8196	2.034	0.3444	5.869	18.987
1.23	16.0	8000	1.8115	1.979	0.338	5.85	18.989
1.2047	16.4	8200	1.8129	2.02	0.3428	5.888	18.99
1.2155	16.8	8400	1.8178	1.978	0.335	5.883	18.991
1.2028	17.2	8600	1.8293	2.017	0.3418	5.88	18.992
1.189	17.6	8800	1.8303	1.983	0.3374	5.858	18.992
1.195	18.0	9000	1.8367	2.021	0.3423	5.883	18.992
1.1837	18.4	9200	1.8388	2.015	0.3403	5.893	18.999
1.1668	18.8	9400	1.8388	2.023	0.342	5.903	18.991
1.1568	19.2	9600	1.8514	2.036	0.3458	5.876	18.99
1.1783	19.6	9800	1.8419	2.042	0.3458	5.902	18.985
1.1674	20.0	10000	1.8433	1.992	0.3394	5.868	18.991
1.1515	20.4	10200	1.8601	2.004	0.3404	5.881	18.985
1.1478	20.8	10400	1.8520	2.032	0.3437	5.897	18.991
1.1634	21.2	10600	1.8582	2.013	0.3398	5.926	18.985
1.138	21.6	10800	1.8571	2.006	0.3399	5.902	18.985
1.1609	22.0	11000	1.8557	2.006	0.3402	5.899	18.991
1.1306	22.4	11200	1.8622	2.02	0.3431	5.894	18.99
1.1485	22.8	11400	1.8619	2.003	0.3402	5.872	18.992
1.1239	23.2	11600	1.8648	2.004	0.3405	5.879	18.992
1.1427	23.6	11800	1.8651	2.003	0.3397	5.897	18.991
1.1451	24.0	12000	1.8631	2.008	0.3404	5.89	18.991
1.1342	24.4	12200	1.8654	2.004	0.3397	5.884	18.99
1.1289	24.8	12400	1.8672	2.005	0.3399	5.888	18.991

Framework versions

Transformers 4.18.0
Pytorch 1.10.0+cu111
Datasets 2.5.1
Tokenizers 0.12.1

Zekunli/flan-t5-large-extraction-all-cnn_8000-ep25-nonstop

flan-t5-large-extraction-all-cnn_8000-ep25-nonstop

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Zekunli/
flan-t5-large-extraction-all-cnn_8000-ep25-nonstop