contradiction entailment neutral
---
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"
---

<!-- 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. -->

# convbert-base-turkish-mc4-cased_allnli_tr

This model is a fine-tuned version of [dbmdz/convbert-base-turkish-mc4-cased](https://huggingface.co/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
