ARC-Challenge_Llama-3.2-1B-r0yf05qb

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: 4.9607
• Model Preparation Time: 0.0059
• Mdl: 2139.8614
• Accumulated Loss: 1483.2389
• Correct Preds: 77.0
• Total Preds: 299.0
• Accuracy: 0.2575
• Correct Gen Preds: 15.0
• Gen Accuracy: 0.0502
• Correct Gen Preds 32: 0.0
• Correct Preds 32: 2.0
• Total Labels 32: 64.0
• Accuracy 32: 0.0312
• Gen Accuracy 32: 0.0
• Correct Gen Preds 33: 11.0
• Correct Preds 33: 60.0
• Total Labels 33: 73.0
• Accuracy 33: 0.8219
• Gen Accuracy 33: 0.1507
• Correct Gen Preds 34: 2.0
• Correct Preds 34: 10.0
• Total Labels 34: 78.0
• Accuracy 34: 0.1282
• Gen Accuracy 34: 0.0256
• Correct Gen Preds 35: 2.0
• Correct Preds 35: 5.0
• Total Labels 35: 83.0
• Accuracy 35: 0.0602
• Gen Accuracy 35: 0.0241
• Correct Gen Preds 36: 0.0
• Correct Preds 36: 0.0
• Total Labels 36: 1.0
• Accuracy 36: 0.0
• Gen Accuracy 36: 0.0

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: 64
• eval_batch_size: 112
• 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: cosine
• lr_scheduler_warmup_ratio: 0.01
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Mdl	Accumulated Loss	Correct Preds	Total Preds	Accuracy	Correct Gen Preds	Gen Accuracy	Correct Gen Preds 32	Correct Preds 32	Total Labels 32	Accuracy 32	Gen Accuracy 32	Correct Gen Preds 33	Correct Preds 33	Total Labels 33	Accuracy 33	Gen Accuracy 33	Correct Gen Preds 34	Correct Preds 34	Total Labels 34	Accuracy 34	Gen Accuracy 34	Correct Gen Preds 35	Correct Preds 35	Total Labels 35	Accuracy 35	Gen Accuracy 35	Correct Gen Preds 36	Correct Preds 36	Total Labels 36	Accuracy 36	Gen Accuracy 36
No log	0	0	1.6389	0.0059	706.9523	490.0220	66.0	299.0	0.2207	66.0	0.2207	62.0	62.0	64.0	0.9688	0.9688	0.0	0.0	73.0	0.0	0.0	4.0	4.0	78.0	0.0513	0.0513	0.0	0.0	83.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
1.7061	1.0	1	1.6389	0.0059	706.9523	490.0220	66.0	299.0	0.2207	66.0	0.2207	62.0	62.0	64.0	0.9688	0.9688	0.0	0.0	73.0	0.0	0.0	4.0	4.0	78.0	0.0513	0.0513	0.0	0.0	83.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
1.7128	2.0	2	2.9134	0.0059	1256.7296	871.0986	73.0	299.0	0.2441	73.0	0.2441	0.0	0.0	64.0	0.0	0.0	73.0	73.0	73.0	1.0	1.0	0.0	0.0	78.0	0.0	0.0	0.0	0.0	83.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
1.1696	3.0	3	2.2709	0.0059	979.5827	678.9950	76.0	299.0	0.2542	4.0	0.0134	1.0	22.0	64.0	0.3438	0.0156	2.0	50.0	73.0	0.6849	0.0274	1.0	1.0	78.0	0.0128	0.0128	0.0	3.0	83.0	0.0361	0.0	0.0	0.0	1.0	0.0	0.0
0.8314	4.0	4	1.8837	0.0059	812.5640	563.2265	75.0	299.0	0.2508	70.0	0.2341	0.0	0.0	64.0	0.0	0.0	68.0	73.0	73.0	1.0	0.9315	0.0	0.0	78.0	0.0	0.0	2.0	2.0	83.0	0.0241	0.0241	0.0	0.0	1.0	0.0	0.0
0.4234	5.0	5	2.7987	0.0059	1207.2848	836.8261	73.0	299.0	0.2441	71.0	0.2375	0.0	0.0	64.0	0.0	0.0	71.0	73.0	73.0	1.0	0.9726	0.0	0.0	78.0	0.0	0.0	0.0	0.0	83.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
0.1618	6.0	6	3.2839	0.0059	1416.5756	981.8954	75.0	299.0	0.2508	48.0	0.1605	0.0	0.0	64.0	0.0	0.0	47.0	73.0	73.0	1.0	0.6438	1.0	2.0	78.0	0.0256	0.0128	0.0	0.0	83.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
0.0192	7.0	7	3.7217	0.0059	1605.4263	1112.7967	74.0	299.0	0.2475	23.0	0.0769	0.0	0.0	64.0	0.0	0.0	20.0	69.0	73.0	0.9452	0.2740	1.0	3.0	78.0	0.0385	0.0128	2.0	2.0	83.0	0.0241	0.0241	0.0	0.0	1.0	0.0	0.0
0.0008	8.0	8	3.9983	0.0059	1724.7310	1195.4925	74.0	299.0	0.2475	17.0	0.0569	0.0	1.0	64.0	0.0156	0.0	14.0	66.0	73.0	0.9041	0.1918	1.0	5.0	78.0	0.0641	0.0128	2.0	2.0	83.0	0.0241	0.0241	0.0	0.0	1.0	0.0	0.0
0.0002	9.0	9	4.2129	0.0059	1817.3188	1259.6694	72.0	299.0	0.2408	16.0	0.0535	0.0	1.0	64.0	0.0156	0.0	13.0	64.0	73.0	0.8767	0.1781	1.0	5.0	78.0	0.0641	0.0128	2.0	2.0	83.0	0.0241	0.0241	0.0	0.0	1.0	0.0	0.0
0.0001	10.0	10	4.3845	0.0059	1891.3416	1310.9781	72.0	299.0	0.2408	16.0	0.0535	0.0	1.0	64.0	0.0156	0.0	13.0	63.0	73.0	0.8630	0.1781	1.0	5.0	78.0	0.0641	0.0128	2.0	3.0	83.0	0.0361	0.0241	0.0	0.0	1.0	0.0	0.0
0.0001	11.0	11	4.4976	0.0059	1940.0996	1344.7746	74.0	299.0	0.2475	15.0	0.0502	0.0	1.0	64.0	0.0156	0.0	12.0	63.0	73.0	0.8630	0.1644	1.0	6.0	78.0	0.0769	0.0128	2.0	4.0	83.0	0.0482	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	12.0	12	4.5953	0.0059	1982.2350	1373.9806	73.0	299.0	0.2441	15.0	0.0502	0.0	1.0	64.0	0.0156	0.0	12.0	61.0	73.0	0.8356	0.1644	2.0	7.0	78.0	0.0897	0.0256	1.0	4.0	83.0	0.0482	0.0120	0.0	0.0	1.0	0.0	0.0
0.0	13.0	13	4.6757	0.0059	2016.9523	1398.0448	74.0	299.0	0.2475	15.0	0.0502	0.0	1.0	64.0	0.0156	0.0	11.0	61.0	73.0	0.8356	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	4.0	83.0	0.0482	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	14.0	14	4.7315	0.0059	2041.0281	1414.7328	74.0	299.0	0.2475	14.0	0.0468	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	1.0	4.0	83.0	0.0482	0.0120	0.0	0.0	1.0	0.0	0.0
0.0	15.0	15	4.7760	0.0059	2060.2072	1428.0268	74.0	299.0	0.2475	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	4.0	83.0	0.0482	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	16.0	16	4.8067	0.0059	2073.4479	1437.2046	74.0	299.0	0.2475	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	4.0	83.0	0.0482	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	17.0	17	4.8319	0.0059	2084.3267	1444.7451	73.0	299.0	0.2441	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	59.0	73.0	0.8082	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	4.0	83.0	0.0482	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	18.0	18	4.8644	0.0059	2098.3559	1454.4695	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	19.0	19	4.8834	0.0059	2106.5338	1460.1380	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	20.0	20	4.8959	0.0059	2111.9169	1463.8692	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	21.0	21	4.9055	0.0059	2116.0587	1466.7401	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	22.0	22	4.9121	0.0059	2118.9142	1468.7194	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	23.0	23	4.9255	0.0059	2124.6990	1472.7291	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	24.0	24	4.9346	0.0059	2128.6175	1475.4452	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	25.0	25	4.9462	0.0059	2133.6370	1478.9245	74.0	299.0	0.2475	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	59.0	73.0	0.8082	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	26.0	26	4.9465	0.0059	2133.7463	1479.0002	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	27.0	27	4.9520	0.0059	2136.1279	1480.6510	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	59.0	73.0	0.8082	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	28.0	28	4.9488	0.0059	2134.7464	1479.6934	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	29.0	29	4.9554	0.0059	2137.6001	1481.6715	74.0	299.0	0.2475	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	59.0	73.0	0.8082	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	30.0	30	4.9554	0.0059	2137.6041	1481.6743	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	31.0	31	4.9607	0.0059	2139.8614	1483.2389	77.0	299.0	0.2575	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	10.0	78.0	0.1282	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	32.0	32	4.9608	0.0059	2139.9391	1483.2928	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	33.0	33	4.9612	0.0059	2140.0986	1483.4033	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	34.0	34	4.9602	0.0059	2139.6793	1483.1127	77.0	299.0	0.2575	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	10.0	78.0	0.1282	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	35.0	35	4.9670	0.0059	2142.5922	1485.1317	75.0	299.0	0.2508	16.0	0.0535	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	3.0	8.0	78.0	0.1026	0.0385	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	36.0	36	4.9635	0.0059	2141.0976	1484.0958	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	37.0	37	4.9663	0.0059	2142.2723	1484.9100	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	38.0	38	4.9711	0.0059	2144.3738	1486.3666	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	39.0	39	4.9587	0.0059	2139.0114	1482.6497	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	40.0	40	4.9709	0.0059	2144.2620	1486.2892	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	41.0	41	4.9670	0.0059	2142.5850	1485.1268	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	42.0	42	4.9677	0.0059	2142.8901	1485.3383	75.0	299.0	0.2508	16.0	0.0535	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	3.0	8.0	78.0	0.1026	0.0385	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	43.0	43	4.9700	0.0059	2143.8805	1486.0247	77.0	299.0	0.2575	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	10.0	78.0	0.1282	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	44.0	44	4.9743	0.0059	2145.7331	1487.3088	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	45.0	45	4.9644	0.0059	2141.4820	1484.3622	76.0	299.0	0.2542	16.0	0.0535	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	3.0	9.0	78.0	0.1154	0.0385	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	46.0	46	4.9724	0.0059	2144.9162	1486.7426	77.0	299.0	0.2575	16.0	0.0535	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	3.0	10.0	78.0	0.1282	0.0385	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	47.0	47	4.9662	0.0059	2142.2475	1484.8928	77.0	299.0	0.2575	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	10.0	78.0	0.1282	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	48.0	48	4.9728	0.0059	2145.0799	1486.8561	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	49.0	49	4.9655	0.0059	2141.9301	1484.6728	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	50.0	50	4.9758	0.0059	2146.4115	1487.7791	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	51.0	51	4.9633	0.0059	2141.0096	1484.0348	77.0	299.0	0.2575	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	10.0	78.0	0.1282	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	52.0	52	4.9658	0.0059	2142.0944	1484.7867	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	53.0	53	4.9699	0.0059	2143.8417	1485.9978	75.0	299.0	0.2508	16.0	0.0535	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	3.0	8.0	78.0	0.1026	0.0385	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	54.0	54	4.9681	0.0059	2143.0609	1485.4566	77.0	299.0	0.2575	16.0	0.0535	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	3.0	10.0	78.0	0.1282	0.0385	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	55.0	55	4.9687	0.0059	2143.3099	1485.6292	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	59.0	73.0	0.8082	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	56.0	56	4.9649	0.0059	2141.6995	1484.5129	76.0	299.0	0.2542	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	57.0	57	4.9705	0.0059	2144.0945	1486.1730	75.0	299.0	0.2508	16.0	0.0535	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	3.0	8.0	78.0	0.1026	0.0385	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	58.0	58	4.9699	0.0059	2143.8280	1485.9883	77.0	299.0	0.2575	16.0	0.0535	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	3.0	10.0	78.0	0.1282	0.0385	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	59.0	59	4.9683	0.0059	2143.1700	1485.5323	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	59.0	73.0	0.8082	0.1507	2.0	9.0	78.0	0.1154	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	60.0	60	4.9680	0.0059	2143.0255	1485.4321	77.0	299.0	0.2575	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	10.0	78.0	0.1282	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0
0.0	61.0	61	4.9672	0.0059	2142.6706	1485.1861	75.0	299.0	0.2508	15.0	0.0502	0.0	2.0	64.0	0.0312	0.0	11.0	60.0	73.0	0.8219	0.1507	2.0	8.0	78.0	0.1026	0.0256	2.0	5.0	83.0	0.0602	0.0241	0.0	0.0	1.0	0.0	0.0

Framework versions

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