openllama-3b-finance

This model is a fine-tuned version of openlm-research/open_llama_3b_v2 on the financial_phrasebank dataset. It achieves the following results on the evaluation set:

• Loss: 4.0296
• Accuracy: 0.4143

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: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
21.9655	0.01	20	8.1663	0.0816
2.231	0.01	40	6.3007	0.4143
2.7452	0.02	60	4.0892	0.4143
2.4561	0.02	80	5.0314	0.4143
2.337	0.03	100	5.6176	0.4143
3.2226	0.03	120	4.4963	0.4143
2.5633	0.04	140	6.1800	0.4143
2.4764	0.04	160	4.7059	0.4143
2.45	0.05	180	5.0602	0.4143
1.4232	0.05	200	5.3418	0.4143
2.7684	0.06	220	5.1805	0.4143
1.7065	0.06	240	4.7568	0.4143
2.3417	0.07	260	6.1062	0.4143
1.907	0.07	280	12.0988	0.5041
14.6043	0.08	300	3.0283	0.0816
1.337	0.08	320	12.7786	0.4143
4.182	0.09	340	7.5619	0.4143
3.7365	0.09	360	7.8581	0.4143
3.209	0.1	380	3.2547	0.4143
3.4836	0.1	400	89.8525	0.0816
4.5805	0.11	420	103.0762	0.4143
4.6351	0.11	440	91.4501	0.4143
11.0873	0.12	460	88.0469	0.4143
1.1274	0.12	480	86.7130	0.4143
2.0398	0.13	500	86.4186	0.4143
18.6924	0.13	520	80.1491	0.4143
1.2216	0.14	540	76.8429	0.4143
1.1179	0.14	560	78.0159	0.4143
10.0981	0.15	580	71.1114	0.4143
9.0123	0.15	600	66.2945	0.4143
1.9539	0.16	620	65.6854	0.4143
8.4729	0.17	640	62.1595	0.4143
7.816	0.17	660	52.0763	0.4143
6.0443	0.18	680	41.1500	0.4143
3.1804	0.18	700	42.8007	0.4143
1.6122	0.19	720	44.0976	0.4143
9.8927	0.19	740	31.6381	0.4143
6.828	0.2	760	12.7483	0.4143
3.1457	0.2	780	13.2981	0.4143
1.9991	0.21	800	12.4846	0.4143
2.5539	0.21	820	13.7669	0.4143
1.3898	0.22	840	12.8919	0.0816
2.9251	0.22	860	15.9149	0.0816
4.0874	0.23	880	10.5282	0.4143
2.4763	0.23	900	3.0281	0.4143
2.2865	0.24	920	12.2460	0.4143
4.2438	0.24	940	10.1961	0.4143
2.547	0.25	960	1.4099	0.4143
0.8659	0.25	980	8.3217	0.4143
3.5331	0.26	1000	6.3990	0.4143
2.4704	0.26	1020	2.2337	0.0816
2.1381	0.27	1040	10.6263	0.4143
1.5927	0.27	1060	11.1989	0.4143
2.485	0.28	1080	8.8174	0.4143
2.8074	0.28	1100	5.5971	0.4143
0.8622	0.29	1120	5.5089	0.4143
2.8085	0.29	1140	5.4300	0.4143
1.2405	0.3	1160	7.5657	0.4143
3.9374	0.3	1180	2.7180	0.4143
1.7494	0.31	1200	4.9639	0.0816
2.6094	0.32	1220	2.1980	0.4143
2.2072	0.32	1240	7.3392	0.4143
0.9978	0.33	1260	7.9127	0.4143
2.3872	0.33	1280	7.0613	0.4143
3.3129	0.34	1300	4.4202	0.4143
1.776	0.34	1320	6.1467	0.4143
3.1179	0.35	1340	6.0607	0.4143
1.272	0.35	1360	5.0484	0.4143
3.0694	0.36	1380	3.1665	0.4143
1.9452	0.36	1400	4.8692	0.4143
2.3689	0.37	1420	4.9375	0.4143
2.7082	0.37	1440	3.2108	0.4143
0.8244	0.38	1460	7.0151	0.4143
2.6032	0.38	1480	5.5645	0.4143
2.8745	0.39	1500	4.2408	0.4143
2.625	0.39	1520	6.8800	0.4143
2.5335	0.4	1540	6.3109	0.4143
2.5495	0.4	1560	4.4017	0.4143
1.7234	0.41	1580	5.1739	0.4143
2.1066	0.41	1600	6.0769	0.4143
2.5541	0.42	1620	3.7539	0.4143
2.4598	0.42	1640	4.2075	0.4143
1.7211	0.43	1660	5.3975	0.4143
2.3993	0.43	1680	4.1427	0.4143
1.6161	0.44	1700	5.0871	0.4143
2.2361	0.44	1720	4.3375	0.4143
2.0841	0.45	1740	4.7357	0.4143
2.137	0.45	1760	5.2737	0.4143
2.3819	0.46	1780	3.1688	0.4143
2.6391	0.46	1800	5.6169	0.4143
1.276	0.47	1820	6.1945	0.4143
2.0694	0.48	1840	6.3761	0.4143
2.3715	0.48	1860	6.1666	0.4143
2.1428	0.49	1880	6.4718	0.4143
2.0409	0.49	1900	6.3259	0.4143
2.1924	0.5	1920	6.0853	0.4143
2.3511	0.5	1940	4.7199	0.4143
2.7335	0.51	1960	4.3591	0.4143
1.6784	0.51	1980	3.7488	0.1612
1.5525	0.52	2000	6.0497	0.4143
2.7457	0.52	2020	3.5952	0.4143
2.3929	0.53	2040	4.7684	0.4143
1.9522	0.53	2060	5.6394	0.4143
2.2257	0.54	2080	4.5801	0.4143
1.6753	0.54	2100	5.0521	0.4143
1.6154	0.55	2120	5.4730	0.4143
1.7723	0.55	2140	5.5251	0.4143
2.6963	0.56	2160	3.5098	0.4143
1.7274	0.56	2180	5.4262	0.4143
2.4059	0.57	2200	4.5019	0.4143
1.6505	0.57	2220	5.1107	0.4143
1.2469	0.58	2240	5.3456	0.4143
1.6702	0.58	2260	5.4103	0.4143
1.615	0.59	2280	5.8024	0.4143
1.5622	0.59	2300	5.6035	0.4143
2.3536	0.6	2320	5.3779	0.4143
2.0512	0.6	2340	5.2498	0.4143
2.1405	0.61	2360	5.2279	0.4143
2.1926	0.61	2380	4.3260	0.4143
2.3995	0.62	2400	4.4445	0.4143
1.4944	0.62	2420	4.9616	0.4143
2.6623	0.63	2440	4.9736	0.4143
1.4095	0.64	2460	4.6506	0.4143
2.4803	0.64	2480	4.0971	0.4143
1.2721	0.65	2500	4.3192	0.4143
1.8372	0.65	2520	4.4907	0.4143
1.8942	0.66	2540	4.7323	0.4143
2.1407	0.66	2560	4.9554	0.4143
2.5039	0.67	2580	5.1599	0.4143
1.7321	0.67	2600	5.6089	0.4143
2.0621	0.68	2620	4.8359	0.4143
2.1664	0.68	2640	4.5581	0.4143
1.8835	0.69	2660	5.1029	0.4143
3.0314	0.69	2680	3.9587	0.4143
1.1781	0.7	2700	4.4584	0.4143
3.3222	0.7	2720	4.7628	0.4143
2.1184	0.71	2740	4.4039	0.4143
1.9293	0.71	2760	3.8755	0.4143
2.2448	0.72	2780	4.4327	0.4143
2.4697	0.72	2800	3.3026	0.4143
1.8569	0.73	2820	3.7722	0.4143
0.8544	0.73	2840	4.9176	0.4143
2.2445	0.74	2860	4.3889	0.4143
1.3723	0.74	2880	4.3280	0.4143
2.2167	0.75	2900	4.4016	0.4143
1.98	0.75	2920	3.8661	0.4143
1.7344	0.76	2940	3.7919	0.4143
1.924	0.76	2960	4.1408	0.4143
1.3811	0.77	2980	4.3730	0.4143
1.8289	0.77	3000	4.2872	0.4143
1.9573	0.78	3020	4.6165	0.4143
2.4877	0.78	3040	4.5988	0.4143
1.1749	0.79	3060	4.7887	0.4143
2.1835	0.8	3080	4.9018	0.4143
2.3752	0.8	3100	4.6911	0.4143
1.9741	0.81	3120	4.5126	0.4143
1.7513	0.81	3140	4.6251	0.4143
3.0666	0.82	3160	4.0260	0.4143
0.5569	0.82	3180	4.0965	0.4143
2.1805	0.83	3200	4.5240	0.4143
2.4319	0.83	3220	4.3080	0.4143
2.126	0.84	3240	3.7823	0.4143
1.6993	0.84	3260	3.8093	0.4143
0.6861	0.85	3280	4.1618	0.4143
0.748	0.85	3300	4.5653	0.4143
2.5721	0.86	3320	4.6628	0.4143
2.0137	0.86	3340	4.2796	0.4143
2.1864	0.87	3360	4.1173	0.4143
2.4881	0.87	3380	3.9617	0.4143
2.6837	0.88	3400	3.7575	0.4143
1.5951	0.88	3420	3.6086	0.4143
2.504	0.89	3440	3.5919	0.4143
1.4982	0.89	3460	3.7519	0.4143
1.8994	0.9	3480	3.7120	0.4143
1.6126	0.9	3500	3.6854	0.4143
2.002	0.91	3520	3.7888	0.4143
1.0264	0.91	3540	3.7990	0.4143
1.9495	0.92	3560	3.9635	0.4143
2.0742	0.92	3580	3.9651	0.4143
1.7803	0.93	3600	3.9518	0.4143
2.0843	0.93	3620	3.9404	0.4143
1.8431	0.94	3640	3.9334	0.4143
1.4987	0.95	3660	3.9609	0.4143
1.8214	0.95	3680	4.0060	0.4143
1.0964	0.96	3700	4.0422	0.4143
0.9669	0.96	3720	4.0549	0.4143
1.6226	0.97	3740	4.0486	0.4143
1.8061	0.97	3760	4.0405	0.4143
2.8738	0.98	3780	4.0317	0.4143
1.684	0.98	3800	4.0319	0.4143
1.1158	0.99	3820	4.0303	0.4143
1.775	0.99	3840	4.0294	0.4143
2.1639	1.0	3860	4.0296	0.4143

Framework versions

• Transformers 4.32.0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3