distilbert-base-uncased-finetunedINv-ner

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1553
• Precision: 0.5303
• Recall: 0.3601
• F1: 0.4289
• Accuracy: 0.9052

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: 16
• eval_batch_size: 16
• 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: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	126	0.8103	0.2722	0.0443	0.0762	0.8885
No log	2.0	252	0.8341	0.4557	0.1863	0.2645	0.8943
No log	3.0	378	0.8622	0.4857	0.2406	0.3218	0.8974
0.2038	4.0	504	0.8880	0.5089	0.3017	0.3788	0.9012
0.2038	5.0	630	0.9086	0.5022	0.3112	0.3843	0.9019
0.2038	6.0	756	0.9324	0.5256	0.3380	0.4114	0.9037
0.2038	7.0	882	0.9504	0.5227	0.3408	0.4125	0.9042
0.0591	8.0	1008	0.9430	0.5105	0.3539	0.4180	0.9047
0.0591	9.0	1134	0.9995	0.5336	0.3417	0.4166	0.9044
0.0591	10.0	1260	0.9983	0.5252	0.3574	0.4253	0.9050
0.0591	11.0	1386	1.0278	0.5287	0.3665	0.4329	0.9051
0.0317	12.0	1512	1.0470	0.5352	0.3539	0.4261	0.9048
0.0317	13.0	1638	1.0525	0.5319	0.3551	0.4259	0.9050
0.0317	14.0	1764	1.0734	0.5496	0.3431	0.4225	0.9045
0.0317	15.0	1890	1.0629	0.5218	0.3620	0.4275	0.9050
0.0211	16.0	2016	1.0774	0.5198	0.3634	0.4277	0.9052
0.0211	17.0	2142	1.0817	0.5177	0.3658	0.4287	0.9052
0.0211	18.0	2268	1.0893	0.5227	0.3664	0.4308	0.9051
0.0211	19.0	2394	1.1293	0.5380	0.3535	0.4267	0.9050
0.0157	20.0	2520	1.1259	0.5323	0.3543	0.4254	0.9050
0.0157	21.0	2646	1.1250	0.5359	0.3521	0.4250	0.9050
0.0157	22.0	2772	1.1369	0.5307	0.3588	0.4281	0.9052
0.0157	23.0	2898	1.1483	0.5368	0.3569	0.4287	0.9051
0.0127	24.0	3024	1.1338	0.5295	0.3642	0.4316	0.9052
0.0127	25.0	3150	1.1476	0.5326	0.3545	0.4257	0.9050
0.0127	26.0	3276	1.1605	0.5418	0.3530	0.4275	0.9050
0.0127	27.0	3402	1.1553	0.5370	0.3577	0.4294	0.9052
0.0107	28.0	3528	1.1570	0.5314	0.3603	0.4295	0.9052
0.0107	29.0	3654	1.1611	0.5346	0.3583	0.4291	0.9051
0.0107	30.0	3780	1.1553	0.5303	0.3601	0.4289	0.9052

Framework versions

• Transformers 4.49.0
• Pytorch 2.7.1+cu118
• Datasets 4.7.0
• Tokenizers 0.21.4