bert_multi-uncased-finetuned-pos-tr

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

• Loss: 0.3933
• Precision: 0.9225
• Recall: 0.9275
• F1: 0.9250
• Accuracy: 0.9564

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4051	1.0	836	0.1752	0.9110	0.9098	0.9104	0.9493
0.1506	2.0	1672	0.1492	0.9112	0.9207	0.9159	0.9529
0.1182	3.0	2508	0.1632	0.9183	0.9229	0.9206	0.9552
0.0973	4.0	3344	0.1544	0.9197	0.9216	0.9206	0.9550
0.0796	5.0	4180	0.1676	0.9227	0.9221	0.9224	0.9569
0.0673	6.0	5016	0.1829	0.9196	0.9206	0.9201	0.9548
0.052	7.0	5852	0.1850	0.9220	0.9237	0.9228	0.9562
0.0448	8.0	6688	0.1980	0.9187	0.9305	0.9246	0.9571
0.0394	9.0	7524	0.2298	0.9147	0.9204	0.9176	0.9531
0.0298	10.0	8360	0.2288	0.9217	0.9277	0.9247	0.9571
0.028	11.0	9196	0.2434	0.9173	0.9231	0.9202	0.9545
0.0233	12.0	10032	0.2609	0.9174	0.9275	0.9225	0.9558
0.018	13.0	10868	0.2701	0.9211	0.9249	0.9230	0.9559
0.0168	14.0	11704	0.2694	0.9206	0.9261	0.9234	0.9564
0.0157	15.0	12540	0.2774	0.9213	0.929	0.9251	0.9568
0.0148	16.0	13376	0.2923	0.9229	0.9255	0.9242	0.9565
0.013	17.0	14212	0.2853	0.9229	0.9263	0.9246	0.9574
0.0104	18.0	15048	0.2899	0.9224	0.9265	0.9245	0.9569
0.0096	19.0	15884	0.2769	0.9239	0.9253	0.9246	0.9571
0.009	20.0	16720	0.3074	0.9204	0.9267	0.9235	0.9570
0.0098	21.0	17556	0.2985	0.9221	0.9287	0.9254	0.9573
0.0071	22.0	18392	0.3293	0.9216	0.9277	0.9246	0.9561
0.0069	23.0	19228	0.3135	0.9242	0.9285	0.9263	0.9572
0.007	24.0	20064	0.3098	0.9226	0.9291	0.9258	0.9576
0.0072	25.0	20900	0.3352	0.9241	0.9288	0.9265	0.9580
0.0048	26.0	21736	0.3384	0.9228	0.9269	0.9249	0.9567
0.006	27.0	22572	0.3316	0.9232	0.9276	0.9254	0.9575
0.0057	28.0	23408	0.3381	0.9238	0.9272	0.9255	0.9578
0.0051	29.0	24244	0.3494	0.9211	0.9282	0.9246	0.9568
0.0046	30.0	25080	0.3379	0.9237	0.9254	0.9246	0.9570
0.0044	31.0	25916	0.3512	0.9232	0.9251	0.9242	0.9565
0.0037	32.0	26752	0.3625	0.9227	0.9252	0.9240	0.9563
0.0029	33.0	27588	0.3476	0.9220	0.9264	0.9242	0.9574
0.0039	34.0	28424	0.3635	0.9238	0.9275	0.9257	0.9575
0.0034	35.0	29260	0.3685	0.9205	0.9247	0.9226	0.9554
0.0033	36.0	30096	0.3693	0.9219	0.9245	0.9232	0.9555
0.003	37.0	30932	0.3698	0.9239	0.9257	0.9248	0.9573
0.0024	38.0	31768	0.3772	0.9242	0.926	0.9251	0.9570
0.0029	39.0	32604	0.3798	0.9246	0.9281	0.9263	0.9563
0.0024	40.0	33440	0.3804	0.9215	0.9264	0.9239	0.9562
0.0017	41.0	34276	0.3804	0.9238	0.9274	0.9256	0.9570
0.0025	42.0	35112	0.3808	0.9252	0.9273	0.9263	0.9570
0.0025	43.0	35948	0.3794	0.9237	0.9282	0.9259	0.9568
0.0029	44.0	36784	0.3784	0.9249	0.9282	0.9265	0.9576
0.0019	45.0	37620	0.3895	0.9238	0.9281	0.9259	0.9569
0.0019	46.0	38456	0.3859	0.9238	0.9284	0.9261	0.9572
0.0017	47.0	39292	0.3906	0.9222	0.9277	0.9249	0.9567
0.0016	48.0	40128	0.3933	0.9221	0.9273	0.9247	0.9565
0.0016	49.0	40964	0.3924	0.9224	0.9273	0.9248	0.9565
0.0017	50.0	41800	0.3933	0.9225	0.9275	0.9250	0.9564

Framework versions

• Transformers 4.34.1
• Pytorch 2.2.1+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1