contradiction entailment neutral
---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: mit
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"
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-base-turkish-cased_allnli_tr

This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5771
- Accuracy: 0.7978

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8559        | 0.03  | 1000  | 0.7577          | 0.6798   |
| 0.6612        | 0.07  | 2000  | 0.7263          | 0.6958   |
| 0.6115        | 0.1   | 3000  | 0.6431          | 0.7364   |
| 0.5916        | 0.14  | 4000  | 0.6347          | 0.7407   |
| 0.5719        | 0.17  | 5000  | 0.6317          | 0.7483   |
| 0.5575        | 0.2   | 6000  | 0.6034          | 0.7544   |
| 0.5521        | 0.24  | 7000  | 0.6148          | 0.7568   |
| 0.5393        | 0.27  | 8000  | 0.5931          | 0.7610   |
| 0.5382        | 0.31  | 9000  | 0.5866          | 0.7665   |
| 0.5306        | 0.34  | 10000 | 0.5881          | 0.7594   |
| 0.5295        | 0.37  | 11000 | 0.6120          | 0.7632   |
| 0.5225        | 0.41  | 12000 | 0.5620          | 0.7759   |
| 0.5112        | 0.44  | 13000 | 0.5641          | 0.7769   |
| 0.5133        | 0.48  | 14000 | 0.5571          | 0.7798   |
| 0.5023        | 0.51  | 15000 | 0.5719          | 0.7722   |
| 0.5017        | 0.54  | 16000 | 0.5482          | 0.7844   |
| 0.5111        | 0.58  | 17000 | 0.5503          | 0.7800   |
| 0.4929        | 0.61  | 18000 | 0.5502          | 0.7836   |
| 0.4923        | 0.65  | 19000 | 0.5424          | 0.7843   |
| 0.4894        | 0.68  | 20000 | 0.5417          | 0.7851   |
| 0.4877        | 0.71  | 21000 | 0.5514          | 0.7841   |
| 0.4818        | 0.75  | 22000 | 0.5494          | 0.7848   |
| 0.4898        | 0.78  | 23000 | 0.5450          | 0.7859   |
| 0.4823        | 0.82  | 24000 | 0.5417          | 0.7878   |
| 0.4806        | 0.85  | 25000 | 0.5354          | 0.7875   |
| 0.4779        | 0.88  | 26000 | 0.5338          | 0.7848   |
| 0.4744        | 0.92  | 27000 | 0.5277          | 0.7934   |
| 0.4678        | 0.95  | 28000 | 0.5507          | 0.7871   |
| 0.4727        | 0.99  | 29000 | 0.5603          | 0.7789   |
| 0.4243        | 1.02  | 30000 | 0.5626          | 0.7894   |
| 0.3955        | 1.05  | 31000 | 0.5324          | 0.7939   |
| 0.4022        | 1.09  | 32000 | 0.5322          | 0.7925   |
| 0.3976        | 1.12  | 33000 | 0.5450          | 0.7920   |
| 0.3913        | 1.15  | 34000 | 0.5464          | 0.7948   |
| 0.406         | 1.19  | 35000 | 0.5406          | 0.7958   |
| 0.3875        | 1.22  | 36000 | 0.5489          | 0.7878   |
| 0.4024        | 1.26  | 37000 | 0.5427          | 0.7925   |
| 0.3988        | 1.29  | 38000 | 0.5335          | 0.7904   |
| 0.393         | 1.32  | 39000 | 0.5415          | 0.7923   |
| 0.3988        | 1.36  | 40000 | 0.5385          | 0.7962   |
| 0.3912        | 1.39  | 41000 | 0.5383          | 0.7950   |
| 0.3949        | 1.43  | 42000 | 0.5415          | 0.7931   |
| 0.3902        | 1.46  | 43000 | 0.5438          | 0.7893   |
| 0.3948        | 1.49  | 44000 | 0.5348          | 0.7906   |
| 0.3921        | 1.53  | 45000 | 0.5361          | 0.7890   |
| 0.3944        | 1.56  | 46000 | 0.5419          | 0.7953   |
| 0.3959        | 1.6   | 47000 | 0.5402          | 0.7967   |
| 0.3926        | 1.63  | 48000 | 0.5429          | 0.7925   |
| 0.3854        | 1.66  | 49000 | 0.5346          | 0.7959   |
| 0.3864        | 1.7   | 50000 | 0.5241          | 0.7979   |
| 0.385         | 1.73  | 51000 | 0.5149          | 0.8002   |
| 0.3871        | 1.77  | 52000 | 0.5325          | 0.8002   |
| 0.3819        | 1.8   | 53000 | 0.5332          | 0.8022   |
| 0.384         | 1.83  | 54000 | 0.5419          | 0.7873   |
| 0.3899        | 1.87  | 55000 | 0.5225          | 0.7974   |
| 0.3894        | 1.9   | 56000 | 0.5358          | 0.7977   |
| 0.3838        | 1.94  | 57000 | 0.5264          | 0.7988   |
| 0.3881        | 1.97  | 58000 | 0.5280          | 0.7956   |
| 0.3756        | 2.0   | 59000 | 0.5601          | 0.7969   |
| 0.3156        | 2.04  | 60000 | 0.5936          | 0.7925   |
| 0.3125        | 2.07  | 61000 | 0.5898          | 0.7938   |
| 0.3179        | 2.11  | 62000 | 0.5591          | 0.7981   |
| 0.315         | 2.14  | 63000 | 0.5853          | 0.7970   |
| 0.3122        | 2.17  | 64000 | 0.5802          | 0.7979   |
| 0.3105        | 2.21  | 65000 | 0.5758          | 0.7979   |
| 0.3076        | 2.24  | 66000 | 0.5685          | 0.7980   |
| 0.3117        | 2.28  | 67000 | 0.5799          | 0.7944   |
| 0.3108        | 2.31  | 68000 | 0.5742          | 0.7988   |
| 0.3047        | 2.34  | 69000 | 0.5907          | 0.7921   |
| 0.3114        | 2.38  | 70000 | 0.5723          | 0.7937   |
| 0.3035        | 2.41  | 71000 | 0.5944          | 0.7955   |
| 0.3129        | 2.45  | 72000 | 0.5838          | 0.7928   |
| 0.3071        | 2.48  | 73000 | 0.5929          | 0.7949   |
| 0.3061        | 2.51  | 74000 | 0.5794          | 0.7967   |
| 0.3068        | 2.55  | 75000 | 0.5892          | 0.7954   |
| 0.3053        | 2.58  | 76000 | 0.5796          | 0.7962   |
| 0.3117        | 2.62  | 77000 | 0.5763          | 0.7981   |
| 0.3062        | 2.65  | 78000 | 0.5852          | 0.7964   |
| 0.3004        | 2.68  | 79000 | 0.5793          | 0.7966   |
| 0.3146        | 2.72  | 80000 | 0.5693          | 0.7985   |
| 0.3146        | 2.75  | 81000 | 0.5788          | 0.7982   |
| 0.3079        | 2.79  | 82000 | 0.5726          | 0.7978   |
| 0.3058        | 2.82  | 83000 | 0.5677          | 0.7988   |
| 0.3055        | 2.85  | 84000 | 0.5701          | 0.7982   |
| 0.3049        | 2.89  | 85000 | 0.5809          | 0.7970   |
| 0.3044        | 2.92  | 86000 | 0.5741          | 0.7986   |
| 0.3057        | 2.96  | 87000 | 0.5743          | 0.7980   |
| 0.3081        | 2.99  | 88000 | 0.5771          | 0.7978   |


### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
