metadata

language:
  - tr
tags:
  - zero-shot-classification
  - nli
  - pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
  - nli_tr
metrics:
  - accuracy
widget:
  - text: Dolar yükselmeye devam ediyor.
    candidate_labels: ekonomi, siyaset, spor
  - text: Senaryo çok saçmaydı, beğendim diyemem.
    candidate_labels: olumlu, olumsuz

convbert-base-turkish-mc4-cased_allnli_tr

This model is a fine-tuned version of dbmdz/convbert-base-turkish-mc4-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5541
Accuracy: 0.8111

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
0.7338	0.03	1000	0.6722	0.7236
0.603	0.07	2000	0.6465	0.7399
0.5605	0.1	3000	0.5801	0.7728
0.55	0.14	4000	0.5994	0.7626
0.529	0.17	5000	0.5720	0.7697
0.5196	0.2	6000	0.5692	0.7769
0.5117	0.24	7000	0.5725	0.7785
0.5044	0.27	8000	0.5532	0.7787
0.5016	0.31	9000	0.5546	0.7812
0.5031	0.34	10000	0.5461	0.7870
0.4949	0.37	11000	0.5725	0.7826
0.4894	0.41	12000	0.5419	0.7933
0.4796	0.44	13000	0.5278	0.7914
0.4795	0.48	14000	0.5193	0.7953
0.4713	0.51	15000	0.5534	0.7771
0.4738	0.54	16000	0.5098	0.8039
0.481	0.58	17000	0.5244	0.7958
0.4634	0.61	18000	0.5215	0.7972
0.465	0.65	19000	0.5129	0.7985
0.4624	0.68	20000	0.5062	0.8047
0.4597	0.71	21000	0.5114	0.8029
0.4571	0.75	22000	0.5070	0.8073
0.4602	0.78	23000	0.5115	0.7993
0.4552	0.82	24000	0.5085	0.8052
0.4538	0.85	25000	0.5118	0.7974
0.4517	0.88	26000	0.5036	0.8044
0.4517	0.92	27000	0.4930	0.8062
0.4413	0.95	28000	0.5307	0.7964
0.4483	0.99	29000	0.5195	0.7938
0.4036	1.02	30000	0.5238	0.8029
0.3724	1.05	31000	0.5125	0.8082
0.3777	1.09	32000	0.5099	0.8075
0.3753	1.12	33000	0.5172	0.8053
0.367	1.15	34000	0.5188	0.8053
0.3819	1.19	35000	0.5218	0.8046
0.363	1.22	36000	0.5202	0.7993
0.3794	1.26	37000	0.5240	0.8048
0.3749	1.29	38000	0.5026	0.8054
0.367	1.32	39000	0.5198	0.8075
0.3759	1.36	40000	0.5298	0.7993
0.3701	1.39	41000	0.5072	0.8091
0.3742	1.43	42000	0.5071	0.8098
0.3706	1.46	43000	0.5317	0.8037
0.3716	1.49	44000	0.5034	0.8052
0.3717	1.53	45000	0.5258	0.8012
0.3714	1.56	46000	0.5195	0.8050
0.3781	1.6	47000	0.5004	0.8104
0.3725	1.63	48000	0.5124	0.8113
0.3624	1.66	49000	0.5040	0.8094
0.3657	1.7	50000	0.4979	0.8111
0.3669	1.73	51000	0.4968	0.8100
0.3636	1.77	52000	0.5075	0.8079
0.36	1.8	53000	0.4985	0.8110
0.3624	1.83	54000	0.5125	0.8070
0.366	1.87	55000	0.4918	0.8117
0.3655	1.9	56000	0.5051	0.8109
0.3609	1.94	57000	0.5083	0.8105
0.3672	1.97	58000	0.5129	0.8085
0.3545	2.0	59000	0.5467	0.8109
0.2938	2.04	60000	0.5635	0.8049
0.29	2.07	61000	0.5781	0.8041
0.2992	2.11	62000	0.5470	0.8077
0.2957	2.14	63000	0.5765	0.8073
0.292	2.17	64000	0.5472	0.8106
0.2893	2.21	65000	0.5590	0.8085
0.2883	2.24	66000	0.5535	0.8064
0.2923	2.28	67000	0.5508	0.8095
0.2868	2.31	68000	0.5679	0.8098
0.2892	2.34	69000	0.5660	0.8057
0.292	2.38	70000	0.5494	0.8088
0.286	2.41	71000	0.5653	0.8085
0.2939	2.45	72000	0.5673	0.8070
0.286	2.48	73000	0.5600	0.8092
0.2844	2.51	74000	0.5508	0.8095
0.2913	2.55	75000	0.5645	0.8088
0.2859	2.58	76000	0.5677	0.8095
0.2892	2.62	77000	0.5598	0.8113
0.2898	2.65	78000	0.5618	0.8096
0.2814	2.68	79000	0.5664	0.8103
0.2917	2.72	80000	0.5484	0.8122
0.2907	2.75	81000	0.5522	0.8116
0.2896	2.79	82000	0.5540	0.8093
0.2907	2.82	83000	0.5469	0.8104
0.2882	2.85	84000	0.5471	0.8122
0.2878	2.89	85000	0.5532	0.8108
0.2858	2.92	86000	0.5511	0.8115
0.288	2.96	87000	0.5491	0.8111
0.2834	2.99	88000	0.5541	0.8111

Framework versions

Transformers 4.12.3
Pytorch 1.10.0+cu102
Datasets 1.15.1
Tokenizers 0.10.3