htas5pp8_20250703_035755

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5051
• Model Preparation Time: 0.0118
• Move Accuracy: 0.3398
• Token Accuracy: 0.8074
• Accuracy: 0.3398

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: 0.0003
• train_batch_size: 128
• eval_batch_size: 256
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant_with_warmup
• lr_scheduler_warmup_ratio: 0.001
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Move Accuracy	Token Accuracy	Accuracy
No log	0	0	11.9474	0.0118	0.0	0.0000	0.0
3.3801	0.0098	100	3.3574	0.0118	0.0	0.2318	0.0
1.6495	0.0196	200	1.6567	0.0118	0.0021	0.3705	0.0021
1.5244	0.0295	300	1.5462	0.0118	0.0068	0.4059	0.0068
1.4183	0.0393	400	1.4742	0.0118	0.0025	0.4291	0.0025
1.409	0.0491	500	1.4532	0.0118	0.0030	0.4372	0.0030
1.3546	0.0589	600	1.4006	0.0118	0.0085	0.4597	0.0085
1.387	0.0687	700	1.3901	0.0118	0.0080	0.4677	0.0080
1.4124	0.0785	800	1.3525	0.0118	0.0168	0.4827	0.0168
1.3189	0.0884	900	1.3249	0.0118	0.0273	0.4956	0.0273
1.3474	0.0982	1000	1.3240	0.0118	0.0224	0.4955	0.0224
1.2771	0.1080	1100	1.2989	0.0118	0.0242	0.4976	0.0242
1.2078	0.1178	1200	1.2705	0.0118	0.0321	0.5140	0.0321
1.242	0.1276	1300	1.2530	0.0118	0.0349	0.5219	0.0349
1.2516	0.1374	1400	1.2183	0.0118	0.0453	0.5352	0.0453
1.1566	0.1473	1500	1.2033	0.0118	0.0476	0.5436	0.0476
1.1482	0.1571	1600	1.1564	0.0118	0.0518	0.5554	0.0518
1.1464	0.1669	1700	1.1460	0.0118	0.0539	0.5641	0.0539
1.1489	0.1767	1800	1.1304	0.0118	0.0563	0.5653	0.0563
1.0549	0.1865	1900	1.1059	0.0118	0.0619	0.5813	0.0619
1.0532	0.1963	2000	1.0807	0.0118	0.0676	0.5902	0.0676
1.0457	0.2062	2100	1.0564	0.0118	0.0681	0.5925	0.0681
1.0556	0.2160	2200	1.0300	0.0118	0.0791	0.6062	0.0791
1.056	0.2258	2300	1.0156	0.0118	0.0782	0.6125	0.0782
0.8758	0.2356	2400	0.9785	0.0118	0.0859	0.6256	0.0859
0.925	0.2454	2500	0.9579	0.0118	0.0942	0.6351	0.0942
0.966	0.2553	2600	0.9379	0.0118	0.1034	0.6413	0.1034
0.9378	0.2651	2700	0.9129	0.0118	0.1112	0.6537	0.1112
0.8783	0.2749	2800	0.9005	0.0118	0.1078	0.6557	0.1078
0.8372	0.2847	2900	0.8897	0.0118	0.1120	0.6588	0.1120
0.8517	0.2945	3000	0.8776	0.0118	0.1217	0.6668	0.1217
0.8685	0.3043	3100	0.8611	0.0118	0.1281	0.6737	0.1281
0.8054	0.3142	3200	0.8556	0.0118	0.1238	0.6752	0.1238
0.8663	0.3240	3300	0.8268	0.0118	0.1354	0.6840	0.1354
0.806	0.3338	3400	0.8313	0.0118	0.1319	0.6816	0.1319
0.7983	0.3436	3500	0.8150	0.0118	0.1412	0.6889	0.1412
0.8185	0.3534	3600	0.8114	0.0118	0.1479	0.6923	0.1479
0.7706	0.3632	3700	0.8163	0.0118	0.1488	0.6895	0.1488
0.7885	0.3731	3800	0.7931	0.0118	0.1479	0.6977	0.1479
0.7337	0.3829	3900	0.7924	0.0118	0.1556	0.6985	0.1556
0.8259	0.3927	4000	0.7868	0.0118	0.1548	0.6996	0.1548
0.7386	0.4025	4100	0.7777	0.0118	0.1534	0.7040	0.1534
0.7201	0.4123	4200	0.7778	0.0118	0.1546	0.7028	0.1546
0.7268	0.4221	4300	0.7559	0.0118	0.1691	0.7128	0.1691
0.7721	0.4320	4400	0.7480	0.0118	0.1678	0.7134	0.1678
0.7769	0.4418	4500	0.7525	0.0118	0.1731	0.7127	0.1731
0.6926	0.4516	4600	0.7371	0.0118	0.1789	0.7190	0.1789
0.704	0.4614	4700	0.7372	0.0118	0.1766	0.7204	0.1766
0.7149	0.4712	4800	0.7284	0.0118	0.1797	0.7214	0.1797
0.7103	0.4811	4900	0.7320	0.0118	0.1813	0.7230	0.1813
0.7337	0.4909	5000	0.7156	0.0118	0.1913	0.7274	0.1913
0.7155	0.5007	5100	0.7126	0.0118	0.1897	0.7289	0.1897
0.6604	0.5105	5200	0.7172	0.0118	0.1886	0.7269	0.1886
0.7236	0.5203	5300	0.7004	0.0118	0.1963	0.7330	0.1963
0.6923	0.5301	5400	0.7134	0.0118	0.1944	0.7299	0.1944
0.6581	0.5400	5500	0.6981	0.0118	0.1972	0.7337	0.1972
0.7241	0.5498	5600	0.7029	0.0118	0.1979	0.7320	0.1979
0.6625	0.5596	5700	0.6890	0.0118	0.2024	0.7361	0.2024
0.7195	0.5694	5800	0.6885	0.0118	0.2001	0.7361	0.2001
0.7245	0.5792	5900	0.6845	0.0118	0.2029	0.7379	0.2029
0.6701	0.5890	6000	0.6802	0.0118	0.2023	0.7386	0.2023
0.6658	0.5989	6100	0.6698	0.0118	0.2103	0.7412	0.2103
0.7011	0.6087	6200	0.6688	0.0118	0.2082	0.7425	0.2082
0.6311	0.6185	6300	0.6617	0.0118	0.2132	0.7459	0.2132
0.5948	0.6283	6400	0.6537	0.0118	0.2172	0.7475	0.2172
0.6518	0.6381	6500	0.6542	0.0118	0.2182	0.7485	0.2182
0.7194	0.6479	6600	0.6465	0.0118	0.2235	0.7515	0.2235
0.5416	0.6578	6700	0.6419	0.0118	0.2219	0.7523	0.2219
0.6842	0.6676	6800	0.6469	0.0118	0.2246	0.7513	0.2246
0.5816	0.6774	6900	0.6386	0.0118	0.2247	0.7532	0.2247
0.6298	0.6872	7000	0.6275	0.0118	0.2241	0.7578	0.2241
0.6543	0.6970	7100	0.6304	0.0118	0.2353	0.7562	0.2353
0.5648	0.7069	7200	0.6215	0.0118	0.2383	0.7600	0.2383
0.5445	0.7167	7300	0.6191	0.0118	0.2371	0.7615	0.2371
0.6196	0.7265	7400	0.6190	0.0118	0.2373	0.7613	0.2373
0.5591	0.7363	7500	0.6088	0.0118	0.2474	0.7650	0.2474
0.6466	0.7461	7600	0.6184	0.0118	0.2355	0.7614	0.2355
0.705	0.7559	7700	0.6219	0.0118	0.2361	0.7589	0.2361
0.6321	0.7658	7800	0.6051	0.0118	0.2455	0.7666	0.2455
0.5462	0.7756	7900	0.6009	0.0118	0.2483	0.7669	0.2483
0.5795	0.7854	8000	0.6038	0.0118	0.2508	0.7664	0.2508
0.5289	0.7952	8100	0.6067	0.0118	0.2495	0.7669	0.2495
0.5854	0.8050	8200	0.5976	0.0118	0.2464	0.7671	0.2464
0.5256	0.8148	8300	0.5932	0.0118	0.2500	0.7715	0.2500
0.5925	0.8247	8400	0.5980	0.0118	0.2437	0.7663	0.2437
0.5503	0.8345	8500	0.5887	0.0118	0.2562	0.7714	0.2562
0.6096	0.8443	8600	0.5966	0.0118	0.2505	0.7681	0.2505
0.5807	0.8541	8700	0.5848	0.0118	0.2613	0.7739	0.2613
0.6099	0.8639	8800	0.5860	0.0118	0.2543	0.7724	0.2543
0.5949	0.8737	8900	0.5753	0.0118	0.2689	0.7768	0.2689
0.5951	0.8836	9000	0.5828	0.0118	0.2596	0.7748	0.2596
0.4958	0.8934	9100	0.5791	0.0118	0.2674	0.7768	0.2674
0.55	0.9032	9200	0.5808	0.0118	0.2567	0.7748	0.2567
0.583	0.9130	9300	0.5796	0.0118	0.2592	0.7751	0.2592
0.5077	0.9228	9400	0.5689	0.0118	0.2709	0.7797	0.2709
0.5856	0.9327	9500	0.5811	0.0118	0.2681	0.7763	0.2681
0.5741	0.9425	9600	0.5759	0.0118	0.2595	0.7765	0.2595
0.5992	0.9523	9700	0.5660	0.0118	0.2716	0.7805	0.2716
0.564	0.9621	9800	0.5731	0.0118	0.2715	0.7776	0.2715
0.5931	0.9719	9900	0.5667	0.0118	0.2683	0.7801	0.2683
0.5814	0.9817	10000	0.5724	0.0118	0.2692	0.7777	0.2692
0.5418	0.9916	10100	0.5611	0.0118	0.2756	0.7827	0.2756
0.5392	1.0014	10200	0.5623	0.0118	0.2729	0.7824	0.2729
0.5566	1.0112	10300	0.5587	0.0118	0.2729	0.7820	0.2729
0.5505	1.0210	10400	0.5605	0.0118	0.2787	0.7835	0.2787
0.5183	1.0308	10500	0.5620	0.0118	0.2758	0.7828	0.2758
0.5255	1.0406	10600	0.5561	0.0118	0.2819	0.7845	0.2819
0.5343	1.0505	10700	0.5561	0.0118	0.2864	0.7853	0.2864
0.5619	1.0603	10800	0.5559	0.0118	0.2840	0.7850	0.2840
0.5234	1.0701	10900	0.5500	0.0118	0.2891	0.7859	0.2891
0.581	1.0799	11000	0.5571	0.0118	0.2773	0.7823	0.2773
0.5466	1.0897	11100	0.5597	0.0118	0.2731	0.7815	0.2731
0.5796	1.0995	11200	0.5555	0.0118	0.2772	0.7834	0.2772
0.5414	1.1094	11300	0.5506	0.0118	0.2838	0.7850	0.2838
0.535	1.1192	11400	0.5494	0.0118	0.2906	0.7865	0.2906
0.5177	1.1290	11500	0.5510	0.0118	0.2813	0.7854	0.2813
0.5417	1.1388	11600	0.5502	0.0118	0.2921	0.7857	0.2921
0.609	1.1486	11700	0.5497	0.0118	0.2858	0.7852	0.2858
0.5418	1.1585	11800	0.5474	0.0118	0.2863	0.7864	0.2863
0.5559	1.1683	11900	0.5432	0.0118	0.2939	0.7892	0.2939
0.4735	1.1781	12000	0.5474	0.0118	0.2926	0.7879	0.2926
0.5169	1.1879	12100	0.5431	0.0118	0.2905	0.7887	0.2905
0.5026	1.1977	12200	0.5356	0.0118	0.2986	0.7912	0.2986
0.5098	1.2075	12300	0.5405	0.0118	0.2992	0.7906	0.2992
0.5173	1.2174	12400	0.5434	0.0118	0.2887	0.7879	0.2887
0.555	1.2272	12500	0.5464	0.0118	0.2854	0.7878	0.2854
0.5105	1.2370	12600	0.5366	0.0118	0.2990	0.7915	0.2990
0.5395	1.2468	12700	0.5376	0.0118	0.2976	0.7917	0.2976
0.5162	1.2566	12800	0.5334	0.0118	0.2955	0.7919	0.2955
0.589	1.2664	12900	0.5382	0.0118	0.2948	0.7906	0.2948
0.5555	1.2763	13000	0.5362	0.0118	0.2990	0.7925	0.2990
0.4993	1.2861	13100	0.5352	0.0118	0.2976	0.7922	0.2976
0.5081	1.2959	13200	0.5345	0.0118	0.3075	0.7945	0.3075
0.5197	1.3057	13300	0.5318	0.0118	0.3033	0.7938	0.3033
0.5371	1.3155	13400	0.5326	0.0118	0.3043	0.7938	0.3043
0.5171	1.3253	13500	0.5343	0.0118	0.2983	0.7917	0.2983
0.4954	1.3352	13600	0.5283	0.0118	0.3060	0.7948	0.3060
0.5308	1.3450	13700	0.5353	0.0118	0.3031	0.7933	0.3031
0.5699	1.3548	13800	0.5308	0.0118	0.3058	0.7937	0.3058
0.4632	1.3646	13900	0.5283	0.0118	0.3028	0.7949	0.3028
0.5144	1.3744	14000	0.5287	0.0118	0.3080	0.7943	0.3080
0.5107	1.3843	14100	0.5279	0.0118	0.3074	0.7955	0.3074
0.4974	1.3941	14200	0.5252	0.0118	0.3091	0.7961	0.3091
0.5128	1.4039	14300	0.5257	0.0118	0.3097	0.7962	0.3097
0.4881	1.4137	14400	0.5328	0.0118	0.3017	0.7936	0.3017
0.5137	1.4235	14500	0.5282	0.0118	0.3082	0.7963	0.3082
0.5249	1.4333	14600	0.5335	0.0118	0.2977	0.7925	0.2977
0.5049	1.4432	14700	0.5290	0.0118	0.3022	0.7945	0.3022
0.4812	1.4530	14800	0.5227	0.0118	0.3099	0.7975	0.3099
0.5707	1.4628	14900	0.5285	0.0118	0.3037	0.7950	0.3037
0.4681	1.4726	15000	0.5250	0.0118	0.3086	0.7964	0.3086
0.5326	1.4824	15100	0.5224	0.0118	0.3140	0.7985	0.3140
0.4954	1.4922	15200	0.5221	0.0118	0.3064	0.7962	0.3064
0.5011	1.5021	15300	0.5140	0.0118	0.3180	0.7998	0.3180
0.4921	1.5119	15400	0.5206	0.0118	0.3078	0.7971	0.3078
0.5393	1.5217	15500	0.5244	0.0118	0.3138	0.7957	0.3138
0.4661	1.5315	15600	0.5204	0.0118	0.3179	0.7987	0.3179
0.5692	1.5413	15700	0.5184	0.0118	0.3139	0.7983	0.3139
0.5032	1.5511	15800	0.5151	0.0118	0.3203	0.8003	0.3203
0.4783	1.5610	15900	0.5223	0.0118	0.3146	0.7981	0.3146
0.5215	1.5708	16000	0.5208	0.0118	0.3178	0.7993	0.3178
0.5261	1.5806	16100	0.5151	0.0118	0.3151	0.8004	0.3151
0.5801	1.5904	16200	0.5108	0.0118	0.3178	0.8010	0.3178
0.5418	1.6002	16300	0.5184	0.0118	0.3175	0.8009	0.3175
0.572	1.6101	16400	0.5157	0.0118	0.3161	0.8011	0.3161
0.5078	1.6199	16500	0.5163	0.0118	0.3165	0.8002	0.3165
0.4871	1.6297	16600	0.5113	0.0118	0.3200	0.8013	0.3200
0.5272	1.6395	16700	0.5131	0.0118	0.3178	0.8006	0.3178
0.507	1.6493	16800	0.5150	0.0118	0.3171	0.8000	0.3171
0.4897	1.6591	16900	0.5126	0.0118	0.3188	0.8012	0.3188
0.4852	1.6690	17000	0.5162	0.0118	0.3144	0.7980	0.3144
0.4846	1.6788	17100	0.5107	0.0118	0.3220	0.8014	0.3220
0.4821	1.6886	17200	0.5136	0.0118	0.3153	0.8007	0.3153
0.4788	1.6984	17300	0.5151	0.0118	0.3191	0.7995	0.3191
0.4535	1.7082	17400	0.5186	0.0118	0.3222	0.7992	0.3222
0.4837	1.7180	17500	0.5176	0.0118	0.3165	0.8003	0.3165
0.4782	1.7279	17600	0.5159	0.0118	0.3187	0.7983	0.3187
0.5019	1.7377	17700	0.5076	0.0118	0.3265	0.8031	0.3265
0.5075	1.7475	17800	0.5133	0.0118	0.3175	0.7996	0.3175
0.51	1.7573	17900	0.5112	0.0118	0.3195	0.8012	0.3195
0.5045	1.7671	18000	0.5114	0.0118	0.3238	0.8028	0.3238
0.5147	1.7769	18100	0.5087	0.0118	0.3212	0.8030	0.3212
0.5508	1.7868	18200	0.5058	0.0118	0.3309	0.8047	0.3309
0.5431	1.7966	18300	0.5113	0.0118	0.3182	0.8006	0.3182
0.5002	1.8064	18400	0.5100	0.0118	0.3226	0.8020	0.3226
0.4756	1.8162	18500	0.5107	0.0118	0.3219	0.8024	0.3219
0.5019	1.8260	18600	0.5083	0.0118	0.3170	0.8031	0.3170
0.5028	1.8359	18700	0.5118	0.0118	0.3203	0.8014	0.3203
0.4589	1.8457	18800	0.5126	0.0118	0.3191	0.8020	0.3191
0.4696	1.8555	18900	0.5140	0.0118	0.3173	0.8003	0.3173
0.5024	1.8653	19000	0.5055	0.0118	0.3247	0.8034	0.3247
0.4898	1.8751	19100	0.5146	0.0118	0.3160	0.8000	0.3160
0.4838	1.8849	19200	0.5173	0.0118	0.3151	0.7997	0.3151
0.5609	1.8948	19300	0.5115	0.0118	0.3148	0.8013	0.3148
0.4862	1.9046	19400	0.5168	0.0118	0.3208	0.8011	0.3208
0.4755	1.9144	19500	0.5094	0.0118	0.3201	0.8022	0.3201
0.5071	1.9242	19600	0.5139	0.0118	0.3239	0.8009	0.3239
0.5007	1.9340	19700	0.5123	0.0118	0.3219	0.8018	0.3219
0.5044	1.9438	19800	0.5091	0.0118	0.3236	0.8019	0.3236
0.4418	1.9537	19900	0.5102	0.0118	0.3278	0.8018	0.3278
0.4993	1.9635	20000	0.5062	0.0118	0.3231	0.8035	0.3231
0.5295	1.9733	20100	0.5110	0.0118	0.3271	0.8026	0.3271
0.5748	1.9831	20200	0.5087	0.0118	0.3205	0.8011	0.3205
0.4929	1.9929	20300	0.5061	0.0118	0.3251	0.8043	0.3251
0.4868	2.0027	20400	0.5107	0.0118	0.3175	0.8029	0.3175
0.4336	2.0126	20500	0.5030	0.0118	0.3309	0.8058	0.3309
0.4698	2.0224	20600	0.4990	0.0118	0.3367	0.8063	0.3367
0.4323	2.0322	20700	0.5102	0.0118	0.3176	0.8019	0.3176
0.4583	2.0420	20800	0.5007	0.0118	0.3311	0.8058	0.3311
0.5309	2.0518	20900	0.5075	0.0118	0.3222	0.8052	0.3222
0.4522	2.0617	21000	0.5006	0.0118	0.3276	0.8058	0.3276
0.528	2.0715	21100	0.5067	0.0118	0.3259	0.8031	0.3259
0.5174	2.0813	21200	0.5045	0.0118	0.3240	0.8036	0.3240
0.5047	2.0911	21300	0.4968	0.0118	0.3376	0.8076	0.3376
0.5155	2.1009	21400	0.5001	0.0118	0.3270	0.8051	0.3270
0.5113	2.1107	21500	0.4986	0.0118	0.3320	0.8055	0.3320
0.4601	2.1206	21600	0.5035	0.0118	0.3286	0.8053	0.3286
0.4792	2.1304	21700	0.5004	0.0118	0.3317	0.8077	0.3317
0.4788	2.1402	21800	0.5031	0.0118	0.3306	0.8039	0.3306
0.4857	2.1500	21900	0.5009	0.0118	0.3301	0.8050	0.3301
0.5015	2.1598	22000	0.5023	0.0118	0.3313	0.8054	0.3313
0.4762	2.1696	22100	0.5029	0.0118	0.3258	0.8032	0.3258
0.4672	2.1795	22200	0.5110	0.0118	0.3228	0.8019	0.3228
0.5216	2.1893	22300	0.5122	0.0118	0.3239	0.8024	0.3239
0.5162	2.1991	22400	0.5028	0.0118	0.3288	0.8053	0.3288
0.4522	2.2089	22500	0.5020	0.0118	0.3294	0.8039	0.3294
0.4738	2.2187	22600	0.5067	0.0118	0.3311	0.8047	0.3311
0.4918	2.2285	22700	0.5112	0.0118	0.3182	0.8014	0.3182
0.5306	2.2384	22800	0.5042	0.0118	0.3281	0.8051	0.3281
0.4333	2.2482	22900	0.5093	0.0118	0.3250	0.8030	0.3250
0.4708	2.2580	23000	0.5078	0.0118	0.3270	0.8044	0.3270
0.5793	2.2678	23100	0.5051	0.0118	0.3398	0.8074	0.3398
0.4442	2.2776	23200	0.5028	0.0118	0.3294	0.8054	0.3294
0.5244	2.2875	23300	0.5150	0.0118	0.3225	0.8026	0.3225
0.5019	2.2973	23400	0.5030	0.0118	0.3270	0.8043	0.3270
0.4933	2.3071	23500	0.5028	0.0118	0.3247	0.8041	0.3247
0.4888	2.3169	23600	0.5057	0.0118	0.3200	0.8034	0.3200
0.4575	2.3267	23700	0.5098	0.0118	0.3233	0.8018	0.3233
0.5153	2.3365	23800	0.4991	0.0118	0.3349	0.8082	0.3349
0.4876	2.3464	23900	0.4999	0.0118	0.3275	0.8047	0.3275
0.5158	2.3562	24000	0.5046	0.0118	0.3238	0.8024	0.3238
0.472	2.3660	24100	0.5035	0.0118	0.3260	0.8049	0.3260
0.5062	2.3758	24200	0.5014	0.0118	0.3298	0.8058	0.3298
0.4677	2.3856	24300	0.5002	0.0118	0.3270	0.8047	0.3270
0.5315	2.3954	24400	0.4966	0.0118	0.3285	0.8066	0.3285
0.5049	2.4053	24500	0.5066	0.0118	0.3215	0.8029	0.3215
0.4281	2.4151	24600	0.5048	0.0118	0.3232	0.8046	0.3232
0.4793	2.4249	24700	0.4953	0.0118	0.3389	0.8086	0.3389
0.4718	2.4347	24800	0.5055	0.0118	0.3229	0.8031	0.3229
0.4519	2.4445	24900	0.5049	0.0118	0.3279	0.8045	0.3279
0.4584	2.4543	25000	0.5019	0.0118	0.3297	0.8063	0.3297
0.5015	2.4642	25100	0.5096	0.0118	0.3293	0.8031	0.3293
0.5433	2.4740	25200	0.5085	0.0118	0.3164	0.8013	0.3164
0.4755	2.4838	25300	0.5025	0.0118	0.3209	0.8042	0.3209
0.4756	2.4936	25400	0.5027	0.0118	0.3245	0.8047	0.3245
0.4881	2.5034	25500	0.4947	0.0118	0.3343	0.8080	0.3343
0.4677	2.5133	25600	0.5034	0.0118	0.3252	0.8049	0.3252
0.483	2.5231	25700	0.5019	0.0118	0.3291	0.8065	0.3291
0.5215	2.5329	25800	0.5135	0.0118	0.3233	0.8021	0.3233
0.5102	2.5427	25900	0.5061	0.0118	0.3204	0.8024	0.3204
0.4884	2.5525	26000	0.5034	0.0118	0.3302	0.8041	0.3302
0.4775	2.5623	26100	0.5050	0.0118	0.3360	0.8057	0.3360
0.4877	2.5722	26200	0.5008	0.0118	0.3283	0.8057	0.3283
0.5116	2.5820	26300	0.5015	0.0118	0.3295	0.8065	0.3295
0.5392	2.5918	26400	0.5046	0.0118	0.3267	0.8038	0.3267
0.5084	2.6016	26500	0.5066	0.0118	0.3253	0.8047	0.3253
0.489	2.6114	26600	0.5013	0.0118	0.3265	0.8060	0.3265
0.5067	2.6212	26700	0.5090	0.0118	0.3229	0.8033	0.3229
0.4967	2.6311	26800	0.5036	0.0118	0.3276	0.8050	0.3276
0.4657	2.6409	26900	0.5020	0.0118	0.3312	0.8059	0.3312
0.5267	2.6507	27000	0.4994	0.0118	0.3327	0.8067	0.3327
0.5084	2.6605	27100	0.5002	0.0118	0.3311	0.8059	0.3311
0.4886	2.6703	27200	0.5043	0.0118	0.3306	0.8051	0.3306
0.5041	2.6801	27300	0.4973	0.0118	0.3364	0.8079	0.3364
0.4823	2.6900	27400	0.5021	0.0118	0.3364	0.8067	0.3364
0.5017	2.6998	27500	0.4943	0.0118	0.3314	0.8085	0.3314
0.4973	2.7096	27600	0.5042	0.0118	0.3255	0.8041	0.3255
0.4963	2.7194	27700	0.4960	0.0118	0.3353	0.8085	0.3353
0.4893	2.7292	27800	0.5020	0.0118	0.3307	0.8052	0.3307
0.486	2.7391	27900	0.5044	0.0118	0.3271	0.8056	0.3271
0.514	2.7489	28000	0.5043	0.0118	0.3258	0.8047	0.3258
0.4647	2.7587	28100	0.5010	0.0118	0.3299	0.8066	0.3299
0.4714	2.7685	28200	0.5027	0.0118	0.3216	0.8044	0.3216
0.4857	2.7783	28300	0.5050	0.0118	0.3301	0.8048	0.3301
0.4692	2.7881	28400	0.5084	0.0118	0.3302	0.8040	0.3302
0.4517	2.7980	28500	0.5089	0.0118	0.3243	0.8034	0.3243
0.523	2.8078	28600	0.5117	0.0118	0.3195	0.8022	0.3195
0.4767	2.8176	28700	0.5121	0.0118	0.3189	0.8018	0.3189
0.5137	2.8274	28800	0.5077	0.0118	0.3216	0.8038	0.3216
0.516	2.8372	28900	0.5041	0.0118	0.3229	0.8034	0.3229
0.491	2.8470	29000	0.5025	0.0118	0.3257	0.8044	0.3257
0.4739	2.8569	29100	0.5025	0.0118	0.3214	0.8043	0.3214
0.5214	2.8667	29200	0.5028	0.0118	0.3338	0.8059	0.3338
0.48	2.8765	29300	0.5003	0.0118	0.3289	0.8041	0.3289
0.4348	2.8863	29400	0.5044	0.0118	0.3255	0.8034	0.3255
0.4777	2.8961	29500	0.5009	0.0118	0.3349	0.8078	0.3349
0.4823	2.9059	29600	0.5005	0.0118	0.3309	0.8071	0.3309
0.4706	2.9158	29700	0.5045	0.0118	0.3208	0.8037	0.3208
0.4913	2.9256	29800	0.5025	0.0118	0.3278	0.8055	0.3278
0.493	2.9354	29900	0.5098	0.0118	0.3282	0.8035	0.3282
0.4679	2.9452	30000	0.5049	0.0118	0.3309	0.8060	0.3309
0.502	2.9550	30100	0.5022	0.0118	0.3224	0.8046	0.3224
0.4818	2.9649	30200	0.5061	0.0118	0.3264	0.8041	0.3264
0.4982	2.9747	30300	0.4997	0.0118	0.3268	0.8058	0.3268
0.4968	2.9845	30400	0.5015	0.0118	0.3311	0.8068	0.3311
0.5531	2.9943	30500	0.5028	0.0118	0.3339	0.8059	0.3339
0.5269	3.0041	30600	0.4946	0.0118	0.3397	0.8084	0.3397
0.4612	3.0139	30700	0.5014	0.0118	0.3279	0.8063	0.3279
0.4947	3.0238	30800	0.5043	0.0118	0.3251	0.8056	0.3251
0.5493	3.0336	30900	0.5041	0.0118	0.3304	0.8047	0.3304
0.4876	3.0434	31000	0.4982	0.0118	0.3331	0.8073	0.3331
0.5314	3.0532	31100	0.5067	0.0118	0.3296	0.8044	0.3296
0.4849	3.0630	31200	0.4975	0.0118	0.3300	0.8069	0.3300
0.5454	3.0728	31300	0.5015	0.0118	0.3285	0.8066	0.3285
0.5145	3.0827	31400	0.5081	0.0118	0.3266	0.8048	0.3266
0.5169	3.0925	31500	0.5040	0.0118	0.3294	0.8062	0.3294
0.4855	3.1023	31600	0.5058	0.0118	0.3255	0.8042	0.3255
0.4716	3.1121	31700	0.5039	0.0118	0.3271	0.8047	0.3271
0.5277	3.1219	31800	0.5111	0.0118	0.3255	0.8031	0.3255
0.4967	3.1317	31900	0.4985	0.0118	0.3339	0.8073	0.3339
0.5205	3.1416	32000	0.5045	0.0118	0.3256	0.8047	0.3256
0.5328	3.1514	32100	0.5120	0.0118	0.3185	0.8014	0.3185
0.5225	3.1612	32200	0.5041	0.0118	0.3217	0.8041	0.3217
0.5333	3.1710	32300	0.5132	0.0118	0.3171	0.8022	0.3171
0.5281	3.1808	32400	0.5058	0.0118	0.3224	0.8046	0.3224
0.501	3.1907	32500	0.5053	0.0118	0.3307	0.8041	0.3307
0.5292	3.2005	32600	0.5054	0.0118	0.3224	0.8039	0.3224
0.4701	3.2103	32700	0.5142	0.0118	0.3129	0.8008	0.3129
0.5319	3.2201	32800	0.5115	0.0118	0.3212	0.8017	0.3212
0.5056	3.2299	32900	0.5127	0.0118	0.3225	0.8012	0.3225
0.5017	3.2397	33000	0.5128	0.0118	0.3170	0.8025	0.3170
0.485	3.2496	33100	0.5067	0.0118	0.3277	0.8035	0.3277
0.5326	3.2594	33200	0.5171	0.0118	0.3195	0.7997	0.3195

Framework versions

• PEFT 0.15.2
• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1