BoolQ_Llama-3.2-1B-eszatdiq

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: 2.5571
• Model Preparation Time: 0.0056
• Mdl: 12063.3522
• Accumulated Loss: 8361.6786
• Correct Preds: 2256.0
• Total Preds: 3270.0
• Accuracy: 0.6899
• Correct Gen Preds: 2181.0
• Gen Accuracy: 0.6670
• Correct Gen Preds 9642: 1467.0
• Correct Preds 9642: 1519.0
• Total Labels 9642: 2026.0
• Accuracy 9642: 0.7498
• Gen Accuracy 9642: 0.7241
• Correct Gen Preds 2822: 706.0
• Correct Preds 2822: 737.0
• Total Labels 2822: 1231.0
• Accuracy 2822: 0.5987
• Gen Accuracy 2822: 0.5735

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
0.8982	1.0	3	0.8052	0.0056	3798.5080	2632.9251	1559.0	3270.0	0.4768	1467.0	0.4486	337.0	372.0	2026.0	0.1836	0.1663	1121.0	1187.0	1231.0	0.9643	0.9106
0.3133	2.0	6	0.6938	0.0056	3273.0467	2268.7031	2128.0	3270.0	0.6508	1815.0	0.5550	1609.0	1865.0	2026.0	0.9205	0.7942	197.0	263.0	1231.0	0.2136	0.1600
0.0233	3.0	9	0.7795	0.0056	3677.5836	2549.1067	2216.0	3270.0	0.6777	2161.0	0.6609	1362.0	1401.0	2026.0	0.6915	0.6723	790.0	815.0	1231.0	0.6621	0.6418
0.0001	4.0	12	2.6272	0.0056	12394.1502	8590.9703	2192.0	3270.0	0.6703	2195.0	0.6713	1973.0	1977.0	2026.0	0.9758	0.9738	214.0	215.0	1231.0	0.1747	0.1738
0.0025	5.0	15	2.5778	0.0056	12161.0922	8429.4268	2237.0	3270.0	0.6841	2227.0	0.6810	1771.0	1782.0	2026.0	0.8796	0.8741	448.0	455.0	1231.0	0.3696	0.3639
0.0	6.0	18	2.5571	0.0056	12063.3522	8361.6786	2256.0	3270.0	0.6899	2181.0	0.6670	1467.0	1519.0	2026.0	0.7498	0.7241	706.0	737.0	1231.0	0.5987	0.5735
0.0	7.0	21	2.6065	0.0056	12296.3865	8523.2057	2192.0	3270.0	0.6703	1996.0	0.6104	1273.0	1402.0	2026.0	0.6920	0.6283	715.0	790.0	1231.0	0.6418	0.5808
0.0001	8.0	24	2.6148	0.0056	12335.8294	8550.5454	2175.0	3270.0	0.6651	1910.0	0.5841	1210.0	1395.0	2026.0	0.6885	0.5972	692.0	780.0	1231.0	0.6336	0.5621
0.0	9.0	27	2.6483	0.0056	12493.8025	8660.0440	2170.0	3270.0	0.6636	1920.0	0.5872	1220.0	1396.0	2026.0	0.6890	0.6022	691.0	774.0	1231.0	0.6288	0.5613
0.0001	10.0	30	2.6828	0.0056	12656.5201	8772.8312	2177.0	3270.0	0.6657	1963.0	0.6003	1255.0	1400.0	2026.0	0.6910	0.6194	700.0	777.0	1231.0	0.6312	0.5686
0.0001	11.0	33	2.7214	0.0056	12838.5669	8899.0164	2171.0	3270.0	0.6639	2013.0	0.6156	1279.0	1393.0	2026.0	0.6876	0.6313	725.0	778.0	1231.0	0.6320	0.5890
0.0	12.0	36	2.7415	0.0056	12933.2785	8964.6655	2169.0	3270.0	0.6633	2035.0	0.6223	1301.0	1393.0	2026.0	0.6876	0.6422	726.0	776.0	1231.0	0.6304	0.5898
0.0	13.0	39	2.7593	0.0056	13017.3006	9022.9052	2172.0	3270.0	0.6642	2056.0	0.6287	1313.0	1395.0	2026.0	0.6885	0.6481	734.0	777.0	1231.0	0.6312	0.5963
0.0	14.0	42	2.7708	0.0056	13071.4073	9060.4091	2167.0	3270.0	0.6627	2066.0	0.6318	1322.0	1393.0	2026.0	0.6876	0.6525	736.0	774.0	1231.0	0.6288	0.5979
0.0	15.0	45	2.7767	0.0056	13099.2616	9079.7162	2168.0	3270.0	0.6630	2068.0	0.6324	1320.0	1392.0	2026.0	0.6871	0.6515	740.0	776.0	1231.0	0.6304	0.6011
0.0	16.0	48	2.7824	0.0056	13126.3414	9098.4865	2169.0	3270.0	0.6633	2077.0	0.6352	1325.0	1391.0	2026.0	0.6866	0.6540	743.0	778.0	1231.0	0.6320	0.6036
0.0	17.0	51	2.7841	0.0056	13134.4015	9104.0734	2165.0	3270.0	0.6621	2078.0	0.6355	1328.0	1392.0	2026.0	0.6871	0.6555	742.0	773.0	1231.0	0.6279	0.6028
0.0	18.0	54	2.7872	0.0056	13148.8380	9114.0800	2171.0	3270.0	0.6639	2082.0	0.6367	1331.0	1397.0	2026.0	0.6895	0.6570	742.0	774.0	1231.0	0.6288	0.6028
0.0	19.0	57	2.7901	0.0056	13162.4860	9123.5401	2171.0	3270.0	0.6639	2081.0	0.6364	1327.0	1393.0	2026.0	0.6876	0.6550	745.0	778.0	1231.0	0.6320	0.6052
0.0	20.0	60	2.7935	0.0056	13178.7743	9134.8302	2172.0	3270.0	0.6642	2083.0	0.6370	1330.0	1398.0	2026.0	0.6900	0.6565	745.0	774.0	1231.0	0.6288	0.6052
0.0	21.0	63	2.7929	0.0056	13175.9740	9132.8892	2167.0	3270.0	0.6627	2080.0	0.6361	1328.0	1393.0	2026.0	0.6876	0.6555	743.0	774.0	1231.0	0.6288	0.6036
0.0	22.0	66	2.7951	0.0056	13186.0428	9139.8684	2175.0	3270.0	0.6651	2087.0	0.6382	1331.0	1397.0	2026.0	0.6895	0.6570	748.0	778.0	1231.0	0.6320	0.6076
0.0	23.0	69	2.7974	0.0056	13196.9785	9147.4485	2171.0	3270.0	0.6639	2089.0	0.6388	1330.0	1394.0	2026.0	0.6881	0.6565	751.0	777.0	1231.0	0.6312	0.6101
0.0	24.0	72	2.7988	0.0056	13203.5576	9152.0087	2172.0	3270.0	0.6642	2089.0	0.6388	1333.0	1395.0	2026.0	0.6885	0.6579	748.0	777.0	1231.0	0.6312	0.6076
0.0	25.0	75	2.8010	0.0056	13214.0329	9159.2696	2172.0	3270.0	0.6642	2093.0	0.6401	1335.0	1396.0	2026.0	0.6890	0.6589	749.0	776.0	1231.0	0.6304	0.6084
0.0	26.0	78	2.8012	0.0056	13214.8892	9159.8632	2174.0	3270.0	0.6648	2088.0	0.6385	1332.0	1397.0	2026.0	0.6895	0.6575	748.0	777.0	1231.0	0.6312	0.6076
0.0	27.0	81	2.8035	0.0056	13225.9128	9167.5042	2172.0	3270.0	0.6642	2092.0	0.6398	1333.0	1394.0	2026.0	0.6881	0.6579	751.0	778.0	1231.0	0.6320	0.6101
0.0	28.0	84	2.8045	0.0056	13230.6764	9170.8061	2172.0	3270.0	0.6642	2095.0	0.6407	1337.0	1395.0	2026.0	0.6885	0.6599	750.0	777.0	1231.0	0.6312	0.6093
0.0	29.0	87	2.8054	0.0056	13234.8323	9173.6867	2171.0	3270.0	0.6639	2090.0	0.6391	1333.0	1396.0	2026.0	0.6890	0.6579	749.0	775.0	1231.0	0.6296	0.6084
0.0	30.0	90	2.8060	0.0056	13237.7898	9175.7367	2175.0	3270.0	0.6651	2094.0	0.6404	1335.0	1396.0	2026.0	0.6890	0.6589	751.0	779.0	1231.0	0.6328	0.6101
0.0	31.0	93	2.8078	0.0056	13246.1557	9181.5355	2168.0	3270.0	0.6630	2091.0	0.6394	1335.0	1393.0	2026.0	0.6876	0.6589	747.0	775.0	1231.0	0.6296	0.6068
0.0	32.0	96	2.8082	0.0056	13247.9959	9182.8110	2169.0	3270.0	0.6633	2095.0	0.6407	1337.0	1393.0	2026.0	0.6876	0.6599	749.0	776.0	1231.0	0.6304	0.6084
0.0	33.0	99	2.8077	0.0056	13245.4286	9181.0315	2173.0	3270.0	0.6645	2100.0	0.6422	1338.0	1396.0	2026.0	0.6890	0.6604	753.0	777.0	1231.0	0.6312	0.6117
0.0	34.0	102	2.8115	0.0056	13263.6309	9193.6484	2169.0	3270.0	0.6633	2091.0	0.6394	1333.0	1394.0	2026.0	0.6881	0.6579	749.0	775.0	1231.0	0.6296	0.6084
0.0	35.0	105	2.8099	0.0056	13255.9181	9188.3022	2174.0	3270.0	0.6648	2095.0	0.6407	1339.0	1397.0	2026.0	0.6895	0.6609	748.0	777.0	1231.0	0.6312	0.6076
0.0	36.0	108	2.8103	0.0056	13258.0305	9189.7664	2173.0	3270.0	0.6645	2098.0	0.6416	1339.0	1397.0	2026.0	0.6895	0.6609	750.0	776.0	1231.0	0.6304	0.6093

Framework versions

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