distilbert-base-turkish-cased_allnli_tr

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

Loss: 0.6481
Accuracy: 0.7381

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: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.94	0.03	1000	0.9074	0.5813
0.8102	0.07	2000	0.8802	0.5949
0.7737	0.1	3000	0.8491	0.6155
0.7576	0.14	4000	0.8283	0.6261
0.7286	0.17	5000	0.8150	0.6362
0.7162	0.2	6000	0.7998	0.6400
0.7092	0.24	7000	0.7830	0.6565
0.6962	0.27	8000	0.7653	0.6629
0.6876	0.31	9000	0.7630	0.6687
0.6778	0.34	10000	0.7475	0.6739
0.6737	0.37	11000	0.7495	0.6781
0.6712	0.41	12000	0.7350	0.6826
0.6559	0.44	13000	0.7274	0.6897
0.6493	0.48	14000	0.7248	0.6902
0.6483	0.51	15000	0.7263	0.6858
0.6445	0.54	16000	0.7070	0.6978
0.6467	0.58	17000	0.7083	0.6981
0.6332	0.61	18000	0.6996	0.7004
0.6288	0.65	19000	0.6979	0.6978
0.6308	0.68	20000	0.6912	0.7040
0.622	0.71	21000	0.6904	0.7092
0.615	0.75	22000	0.6872	0.7094
0.6186	0.78	23000	0.6877	0.7075
0.6183	0.82	24000	0.6818	0.7111
0.6115	0.85	25000	0.6856	0.7122
0.608	0.88	26000	0.6697	0.7179
0.6071	0.92	27000	0.6727	0.7181
0.601	0.95	28000	0.6798	0.7118
0.6018	0.99	29000	0.6854	0.7071
0.5762	1.02	30000	0.6697	0.7214
0.5507	1.05	31000	0.6710	0.7185
0.5575	1.09	32000	0.6709	0.7226
0.5493	1.12	33000	0.6659	0.7191
0.5464	1.15	34000	0.6709	0.7232
0.5595	1.19	35000	0.6642	0.7220
0.5446	1.22	36000	0.6709	0.7202
0.5524	1.26	37000	0.6751	0.7148
0.5473	1.29	38000	0.6642	0.7209
0.5477	1.32	39000	0.6662	0.7223
0.5522	1.36	40000	0.6586	0.7227
0.5406	1.39	41000	0.6602	0.7258
0.54	1.43	42000	0.6564	0.7273
0.5458	1.46	43000	0.6780	0.7213
0.5448	1.49	44000	0.6561	0.7235
0.5418	1.53	45000	0.6600	0.7253
0.5408	1.56	46000	0.6616	0.7274
0.5451	1.6	47000	0.6557	0.7283
0.5385	1.63	48000	0.6583	0.7295
0.5261	1.66	49000	0.6468	0.7325
0.5364	1.7	50000	0.6447	0.7329
0.5294	1.73	51000	0.6429	0.7320
0.5332	1.77	52000	0.6508	0.7272
0.5274	1.8	53000	0.6492	0.7326
0.5286	1.83	54000	0.6470	0.7318
0.5359	1.87	55000	0.6393	0.7354
0.5366	1.9	56000	0.6445	0.7367
0.5296	1.94	57000	0.6413	0.7313
0.5346	1.97	58000	0.6393	0.7315
0.5264	2.0	59000	0.6448	0.7357
0.4857	2.04	60000	0.6640	0.7335
0.4888	2.07	61000	0.6612	0.7318
0.4964	2.11	62000	0.6516	0.7337
0.493	2.14	63000	0.6503	0.7356
0.4961	2.17	64000	0.6519	0.7348
0.4847	2.21	65000	0.6517	0.7327
0.483	2.24	66000	0.6555	0.7310
0.4857	2.28	67000	0.6525	0.7312
0.484	2.31	68000	0.6444	0.7342
0.4792	2.34	69000	0.6508	0.7330
0.488	2.38	70000	0.6513	0.7344
0.472	2.41	71000	0.6547	0.7346
0.4872	2.45	72000	0.6500	0.7342
0.4782	2.48	73000	0.6585	0.7358
0.481	2.51	74000	0.6477	0.7356
0.4822	2.55	75000	0.6587	0.7346
0.4728	2.58	76000	0.6572	0.7340
0.4841	2.62	77000	0.6443	0.7374
0.4885	2.65	78000	0.6494	0.7362
0.4752	2.68	79000	0.6509	0.7382
0.4883	2.72	80000	0.6457	0.7371
0.4888	2.75	81000	0.6497	0.7364
0.4844	2.79	82000	0.6481	0.7376
0.4833	2.82	83000	0.6451	0.7389
0.48	2.85	84000	0.6423	0.7373
0.4832	2.89	85000	0.6477	0.7357
0.4805	2.92	86000	0.6464	0.7379
0.4775	2.96	87000	0.6477	0.7380
0.4843	2.99	88000	0.6481	0.7381

Framework versions

Transformers 4.12.3
Pytorch 1.10.0+cu102
Datasets 1.15.1
Tokenizers 0.10.3