BoolQ_Llama-3.2-1B-5r42yp3k

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: 1.5466
• Model Preparation Time: 0.0056
• Mdl: 7296.3329
• Accumulated Loss: 5057.4326
• Correct Preds: 2619.0
• Total Preds: 3270.0
• Accuracy: 0.8009
• Correct Gen Preds: 2594.0
• Gen Accuracy: 0.7933
• Correct Gen Preds 9642: 1748.0
• Correct Preds 9642: 1776.0
• Total Labels 9642: 2026.0
• Accuracy 9642: 0.8766
• Gen Accuracy 9642: 0.8628
• Correct Gen Preds 2822: 838.0
• Correct Preds 2822: 843.0
• Total Labels 2822: 1231.0
• Accuracy 2822: 0.6848
• Gen Accuracy 2822: 0.6807

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: 32
• eval_batch_size: 120
• 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 9642	Correct Preds 9642	Total Labels 9642	Accuracy 9642	Gen Accuracy 9642	Correct Gen Preds 2822	Correct Preds 2822	Total Labels 2822	Accuracy 2822	Gen Accuracy 2822
No log	0	0	0.7080	0.0056	3339.8933	2315.0376	2032.0	3270.0	0.6214	2040.0	0.6239	2007.0	2008.0	2026.0	0.9911	0.9906	24.0	24.0	1231.0	0.0195	0.0195
0.4335	1.0	43	0.5330	0.0056	2514.6645	1743.0326	2457.0	3270.0	0.7514	2447.0	0.7483	1619.0	1630.0	2026.0	0.8045	0.7991	819.0	827.0	1231.0	0.6718	0.6653
0.2605	2.0	86	0.6563	0.0056	3096.0653	2146.0289	2450.0	3270.0	0.7492	1969.0	0.6021	1023.0	1427.0	2026.0	0.7043	0.5049	939.0	1023.0	1231.0	0.8310	0.7628
0.0158	3.0	129	1.0674	0.0056	5035.6484	3490.4455	2536.0	3270.0	0.7755	2378.0	0.7272	1717.0	1872.0	2026.0	0.9240	0.8475	654.0	664.0	1231.0	0.5394	0.5313
0.1505	4.0	172	1.4954	0.0056	7054.8825	4890.0719	2587.0	3270.0	0.7911	2572.0	0.7865	1811.0	1831.0	2026.0	0.9038	0.8939	752.0	756.0	1231.0	0.6141	0.6109
0.0	5.0	215	1.4715	0.0056	6942.0371	4811.8535	2611.0	3270.0	0.7985	2575.0	0.7875	1690.0	1727.0	2026.0	0.8524	0.8342	877.0	884.0	1231.0	0.7181	0.7124
0.0004	6.0	258	1.5466	0.0056	7296.3329	5057.4326	2619.0	3270.0	0.8009	2594.0	0.7933	1748.0	1776.0	2026.0	0.8766	0.8628	838.0	843.0	1231.0	0.6848	0.6807
0.0	7.0	301	1.5498	0.0056	7311.3028	5067.8089	2617.0	3270.0	0.8003	2587.0	0.7911	1708.0	1739.0	2026.0	0.8583	0.8430	871.0	878.0	1231.0	0.7132	0.7076
0.0	8.0	344	1.5583	0.0056	7351.5687	5095.7191	2617.0	3270.0	0.8003	2591.0	0.7924	1708.0	1737.0	2026.0	0.8574	0.8430	875.0	880.0	1231.0	0.7149	0.7108
0.0	9.0	387	1.5645	0.0056	7380.4891	5115.7652	2615.0	3270.0	0.7997	2589.0	0.7917	1710.0	1738.0	2026.0	0.8578	0.8440	871.0	877.0	1231.0	0.7124	0.7076
0.0	10.0	430	1.5689	0.0056	7401.5336	5130.3521	2615.0	3270.0	0.7997	2593.0	0.7930	1712.0	1738.0	2026.0	0.8578	0.8450	873.0	877.0	1231.0	0.7124	0.7092
0.0	11.0	473	1.5753	0.0056	7431.6332	5151.2156	2618.0	3270.0	0.8006	2595.0	0.7936	1713.0	1738.0	2026.0	0.8578	0.8455	873.0	880.0	1231.0	0.7149	0.7092
0.0	12.0	516	1.5764	0.0056	7436.8304	5154.8180	2617.0	3270.0	0.8003	2594.0	0.7933	1714.0	1739.0	2026.0	0.8583	0.8460	872.0	878.0	1231.0	0.7132	0.7084
0.0	13.0	559	1.5821	0.0056	7463.8777	5173.5658	2616.0	3270.0	0.8	2592.0	0.7927	1712.0	1738.0	2026.0	0.8578	0.8450	872.0	878.0	1231.0	0.7132	0.7084
0.0	14.0	602	1.5848	0.0056	7476.3623	5182.2194	2615.0	3270.0	0.7997	2592.0	0.7927	1711.0	1737.0	2026.0	0.8574	0.8445	873.0	878.0	1231.0	0.7132	0.7092
0.0	15.0	645	1.5866	0.0056	7484.9367	5188.1628	2617.0	3270.0	0.8003	2595.0	0.7936	1712.0	1738.0	2026.0	0.8578	0.8450	874.0	879.0	1231.0	0.7141	0.7100
0.9802	16.0	688	1.5898	0.0056	7499.9718	5198.5843	2617.0	3270.0	0.8003	2597.0	0.7942	1714.0	1738.0	2026.0	0.8578	0.8460	875.0	879.0	1231.0	0.7141	0.7108
0.0	17.0	731	1.5963	0.0056	7530.6554	5219.8526	2616.0	3270.0	0.8	2597.0	0.7942	1715.0	1739.0	2026.0	0.8583	0.8465	874.0	877.0	1231.0	0.7124	0.7100
0.0	18.0	774	1.6015	0.0056	7555.0401	5236.7547	2613.0	3270.0	0.7991	2592.0	0.7927	1712.0	1737.0	2026.0	0.8574	0.8450	872.0	876.0	1231.0	0.7116	0.7084
0.0	19.0	817	1.5991	0.0056	7543.8108	5228.9712	2618.0	3270.0	0.8006	2597.0	0.7942	1713.0	1738.0	2026.0	0.8578	0.8455	876.0	880.0	1231.0	0.7149	0.7116
0.0	20.0	860	1.6021	0.0056	7558.1173	5238.8877	2616.0	3270.0	0.8	2596.0	0.7939	1715.0	1739.0	2026.0	0.8583	0.8465	873.0	877.0	1231.0	0.7124	0.7092
0.0	21.0	903	1.6036	0.0056	7565.0561	5243.6973	2614.0	3270.0	0.7994	2594.0	0.7933	1713.0	1737.0	2026.0	0.8574	0.8455	873.0	877.0	1231.0	0.7124	0.7092
0.0	22.0	946	1.6052	0.0056	7572.8549	5249.1031	2615.0	3270.0	0.7997	2596.0	0.7939	1713.0	1737.0	2026.0	0.8574	0.8455	874.0	878.0	1231.0	0.7132	0.7100
0.0	23.0	989	1.6049	0.0056	7571.4610	5248.1369	2614.0	3270.0	0.7994	2595.0	0.7936	1712.0	1736.0	2026.0	0.8569	0.8450	875.0	878.0	1231.0	0.7132	0.7108
0.0	24.0	1032	1.6037	0.0056	7565.6381	5244.1007	2616.0	3270.0	0.8	2597.0	0.7942	1716.0	1739.0	2026.0	0.8583	0.8470	873.0	877.0	1231.0	0.7124	0.7092
0.0	25.0	1075	1.6096	0.0056	7593.4658	5263.3894	2615.0	3270.0	0.7997	2595.0	0.7936	1714.0	1738.0	2026.0	0.8578	0.8460	873.0	877.0	1231.0	0.7124	0.7092
0.0	26.0	1118	1.6081	0.0056	7586.3418	5258.4514	2618.0	3270.0	0.8006	2600.0	0.7951	1717.0	1739.0	2026.0	0.8583	0.8475	875.0	879.0	1231.0	0.7141	0.7108
0.0	27.0	1161	1.6060	0.0056	7576.7036	5251.7707	2615.0	3270.0	0.7997	2594.0	0.7933	1712.0	1737.0	2026.0	0.8574	0.8450	874.0	878.0	1231.0	0.7132	0.7100
0.0	28.0	1204	1.6088	0.0056	7589.7099	5260.7860	2617.0	3270.0	0.8003	2598.0	0.7945	1717.0	1739.0	2026.0	0.8583	0.8475	873.0	878.0	1231.0	0.7132	0.7092
0.0	29.0	1247	1.6068	0.0056	7580.2581	5254.2345	2613.0	3270.0	0.7991	2595.0	0.7936	1717.0	1740.0	2026.0	0.8588	0.8475	869.0	873.0	1231.0	0.7092	0.7059
0.0	30.0	1290	1.6088	0.0056	7589.7604	5260.8210	2616.0	3270.0	0.8	2599.0	0.7948	1716.0	1738.0	2026.0	0.8578	0.8470	875.0	878.0	1231.0	0.7132	0.7108
0.0	31.0	1333	1.6060	0.0056	7576.4338	5251.5837	2611.0	3270.0	0.7985	2592.0	0.7927	1713.0	1736.0	2026.0	0.8569	0.8455	871.0	875.0	1231.0	0.7108	0.7076
0.0	32.0	1376	1.6103	0.0056	7596.7626	5265.6746	2618.0	3270.0	0.8006	2599.0	0.7948	1716.0	1740.0	2026.0	0.8588	0.8470	875.0	878.0	1231.0	0.7132	0.7108
0.0	33.0	1419	1.6099	0.0056	7594.6633	5264.2194	2612.0	3270.0	0.7988	2594.0	0.7933	1715.0	1737.0	2026.0	0.8574	0.8465	871.0	875.0	1231.0	0.7108	0.7076
0.0	34.0	1462	1.6107	0.0056	7598.6742	5266.9996	2616.0	3270.0	0.8	2597.0	0.7942	1716.0	1738.0	2026.0	0.8578	0.8470	873.0	878.0	1231.0	0.7132	0.7092
0.0	35.0	1505	1.6082	0.0056	7586.7298	5258.7204	2617.0	3270.0	0.8003	2601.0	0.7954	1718.0	1738.0	2026.0	0.8578	0.8480	874.0	879.0	1231.0	0.7141	0.7100
0.0	36.0	1548	1.6120	0.0056	7604.7402	5271.2042	2617.0	3270.0	0.8003	2601.0	0.7954	1718.0	1738.0	2026.0	0.8578	0.8480	875.0	879.0	1231.0	0.7141	0.7108

Framework versions

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