File size: 9,739 Bytes
e84aada fa222b5 e84aada d5b7431 e84aada d5b7431 e84aada d5b7431 e84aada d5b7431 2d47e1a e84aada 2d47e1a e84aada fa222b5 e84aada fa222b5 e84aada 35ef4cf e84aada d5b7431 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
---
library_name: transformers
license: mit
base_model: dbmdz/bert-base-turkish-cased
tags:
- generated_from_trainer
- turkish
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: turkish-zeroshot
results: []
datasets:
- facebook/xnli
language:
- tr
pipeline_tag: zero-shot-classification
---
<!-- 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. -->
# turkish-zeroshot
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) onfacebook/xnli tr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5637
- Accuracy: 0.7731
- F1: 0.7740
- Precision: 0.7804
- Recall: 0.7731
## Usage
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("zero-shot-classification", model="kaixkhazaki/turkish-zeroshot")
#Enter your text and possible candidates of classification
sequence = "Bu laptopun pil ömrü ne kadar dayanıyor?"
candidate_labels = ["ürün özellikleri", "soru", "bilgi talebi", "laptop", "teknik destek"]
pipe(
sequence,
candidate_labels,
)
>>
{'sequence': 'Bu laptopun pil ömrü ne kadar dayanıyor?',
'labels': ['ürün özellikleri',
'soru',
'bilgi talebi',
'laptop',
'teknik destek'],
'scores': [0.296932578086853,
0.2693993151187897,
0.20735479891300201,
0.12200483679771423,
0.10430848598480225]}
```
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.09 | 0.0326 | 200 | 1.0950 | 0.3759 | 0.3534 | 0.3966 | 0.3759 |
| 0.9377 | 0.0652 | 400 | 0.8817 | 0.6092 | 0.6059 | 0.6499 | 0.6092 |
| 0.8277 | 0.0978 | 600 | 0.7518 | 0.6799 | 0.6801 | 0.6904 | 0.6799 |
| 0.7771 | 0.1304 | 800 | 0.7274 | 0.6984 | 0.6991 | 0.7138 | 0.6984 |
| 0.7698 | 0.1630 | 1000 | 0.6928 | 0.7 | 0.7015 | 0.7111 | 0.7 |
| 0.7619 | 0.1956 | 1200 | 0.6820 | 0.7161 | 0.7166 | 0.7313 | 0.7161 |
| 0.7453 | 0.2282 | 1400 | 0.6614 | 0.7205 | 0.7217 | 0.7307 | 0.7205 |
| 0.7287 | 0.2608 | 1600 | 0.6589 | 0.7209 | 0.7204 | 0.7346 | 0.7209 |
| 0.7168 | 0.2934 | 1800 | 0.6694 | 0.7157 | 0.7157 | 0.7311 | 0.7157 |
| 0.6923 | 0.3259 | 2000 | 0.6655 | 0.7165 | 0.7167 | 0.7312 | 0.7165 |
| 0.7348 | 0.3585 | 2200 | 0.6594 | 0.7221 | 0.7207 | 0.7366 | 0.7221 |
| 0.7022 | 0.3911 | 2400 | 0.6757 | 0.7317 | 0.7309 | 0.7536 | 0.7317 |
| 0.6968 | 0.4237 | 2600 | 0.6448 | 0.7297 | 0.7305 | 0.7484 | 0.7297 |
| 0.7011 | 0.4563 | 2800 | 0.6169 | 0.7398 | 0.7403 | 0.7458 | 0.7398 |
| 0.6949 | 0.4889 | 3000 | 0.6200 | 0.7482 | 0.7483 | 0.7530 | 0.7482 |
| 0.7042 | 0.5215 | 3200 | 0.6267 | 0.7402 | 0.7406 | 0.7592 | 0.7402 |
| 0.6884 | 0.5541 | 3400 | 0.6222 | 0.7494 | 0.7487 | 0.7584 | 0.7494 |
| 0.655 | 0.5867 | 3600 | 0.6460 | 0.7337 | 0.7333 | 0.7485 | 0.7337 |
| 0.6745 | 0.6193 | 3800 | 0.6133 | 0.7538 | 0.7537 | 0.7574 | 0.7538 |
| 0.6809 | 0.6519 | 4000 | 0.6338 | 0.7442 | 0.7436 | 0.7544 | 0.7442 |
| 0.6674 | 0.6845 | 4200 | 0.6118 | 0.7494 | 0.7506 | 0.7588 | 0.7494 |
| 0.6815 | 0.7171 | 4400 | 0.6173 | 0.7462 | 0.7477 | 0.7587 | 0.7462 |
| 0.652 | 0.7497 | 4600 | 0.5969 | 0.7659 | 0.7656 | 0.7691 | 0.7659 |
| 0.6517 | 0.7823 | 4800 | 0.6170 | 0.7506 | 0.7515 | 0.7615 | 0.7506 |
| 0.6335 | 0.8149 | 5000 | 0.5767 | 0.7731 | 0.7736 | 0.7763 | 0.7731 |
| 0.6362 | 0.8475 | 5200 | 0.6273 | 0.7542 | 0.7550 | 0.7676 | 0.7542 |
| 0.6638 | 0.8801 | 5400 | 0.5773 | 0.7743 | 0.7753 | 0.7795 | 0.7743 |
| 0.6369 | 0.9126 | 5600 | 0.5980 | 0.7534 | 0.7552 | 0.7673 | 0.7534 |
| 0.6551 | 0.9452 | 5800 | 0.5927 | 0.7526 | 0.7546 | 0.7732 | 0.7526 |
| 0.6549 | 0.9778 | 6000 | 0.5673 | 0.7618 | 0.7634 | 0.7709 | 0.7618 |
| 0.5314 | 1.0104 | 6200 | 0.6203 | 0.7590 | 0.7589 | 0.7670 | 0.7590 |
| 0.5127 | 1.0430 | 6400 | 0.5939 | 0.7663 | 0.7665 | 0.7697 | 0.7663 |
| 0.5405 | 1.0756 | 6600 | 0.6012 | 0.7594 | 0.7605 | 0.7714 | 0.7594 |
| 0.5618 | 1.1082 | 6800 | 0.6069 | 0.7614 | 0.7621 | 0.7682 | 0.7614 |
| 0.5509 | 1.1408 | 7000 | 0.6226 | 0.7538 | 0.7552 | 0.7754 | 0.7538 |
| 0.5501 | 1.1734 | 7200 | 0.5793 | 0.7703 | 0.7715 | 0.7765 | 0.7703 |
| 0.5476 | 1.2060 | 7400 | 0.5969 | 0.7627 | 0.7617 | 0.7703 | 0.7627 |
| 0.5434 | 1.2386 | 7600 | 0.5980 | 0.7578 | 0.7590 | 0.7753 | 0.7578 |
| 0.5606 | 1.2712 | 7800 | 0.6319 | 0.7518 | 0.7502 | 0.7659 | 0.7518 |
| 0.5449 | 1.3038 | 8000 | 0.5945 | 0.7574 | 0.7578 | 0.7652 | 0.7574 |
| 0.5099 | 1.3364 | 8200 | 0.6824 | 0.7426 | 0.7427 | 0.7685 | 0.7426 |
| 0.5406 | 1.3690 | 8400 | 0.5831 | 0.7695 | 0.7702 | 0.7737 | 0.7695 |
| 0.5577 | 1.4016 | 8600 | 0.6264 | 0.7490 | 0.7483 | 0.7687 | 0.7490 |
| 0.5502 | 1.4342 | 8800 | 0.5838 | 0.7647 | 0.7644 | 0.7689 | 0.7647 |
| 0.527 | 1.4668 | 9000 | 0.5837 | 0.7675 | 0.7679 | 0.7705 | 0.7675 |
| 0.5066 | 1.4993 | 9200 | 0.5884 | 0.7651 | 0.7660 | 0.7728 | 0.7651 |
| 0.5391 | 1.5319 | 9400 | 0.5754 | 0.7659 | 0.7665 | 0.7697 | 0.7659 |
| 0.5276 | 1.5645 | 9600 | 0.5743 | 0.7795 | 0.7803 | 0.7830 | 0.7795 |
| 0.5329 | 1.5971 | 9800 | 0.5865 | 0.7570 | 0.7585 | 0.7691 | 0.7570 |
| 0.5467 | 1.6297 | 10000 | 0.6229 | 0.7586 | 0.7598 | 0.7695 | 0.7586 |
| 0.5373 | 1.6623 | 10200 | 0.6006 | 0.7602 | 0.7610 | 0.7665 | 0.7602 |
| 0.517 | 1.6949 | 10400 | 0.6037 | 0.7502 | 0.7517 | 0.7668 | 0.7502 |
| 0.5068 | 1.7275 | 10600 | 0.5945 | 0.7655 | 0.7659 | 0.7729 | 0.7655 |
| 0.5491 | 1.7601 | 10800 | 0.6104 | 0.7602 | 0.7615 | 0.7730 | 0.7602 |
| 0.5282 | 1.7927 | 11000 | 0.5829 | 0.7659 | 0.7666 | 0.7781 | 0.7659 |
| 0.5359 | 1.8253 | 11200 | 0.6102 | 0.7622 | 0.7620 | 0.7754 | 0.7622 |
| 0.549 | 1.8579 | 11400 | 0.5678 | 0.7643 | 0.7652 | 0.7724 | 0.7643 |
| 0.525 | 1.8905 | 11600 | 0.6133 | 0.7627 | 0.7635 | 0.7791 | 0.7627 |
| 0.5297 | 1.9231 | 11800 | 0.5893 | 0.7675 | 0.7679 | 0.7745 | 0.7675 |
| 0.5438 | 1.9557 | 12000 | 0.5637 | 0.7731 | 0.7740 | 0.7804 | 0.7731 |
| 0.5426 | 1.9883 | 12200 | 0.5937 | 0.7622 | 0.7624 | 0.7731 | 0.7622 |
| 0.3892 | 2.0209 | 12400 | 0.6167 | 0.7719 | 0.7725 | 0.7766 | 0.7719 |
| 0.3618 | 2.0535 | 12600 | 0.7019 | 0.7687 | 0.7695 | 0.7759 | 0.7687 |
| 0.392 | 2.0860 | 12800 | 0.7179 | 0.7534 | 0.7551 | 0.7795 | 0.7534 |
| 0.3912 | 2.1186 | 13000 | 0.6969 | 0.7518 | 0.7526 | 0.7715 | 0.7518 |
| 0.3798 | 2.1512 | 13200 | 0.6487 | 0.7715 | 0.7725 | 0.7800 | 0.7715 |
| 0.3856 | 2.1838 | 13400 | 0.6196 | 0.7671 | 0.7677 | 0.7709 | 0.7671 |
| 0.358 | 2.2164 | 13600 | 0.7144 | 0.7574 | 0.7574 | 0.7705 | 0.7574 |
| 0.3854 | 2.2490 | 13800 | 0.6709 | 0.7598 | 0.7598 | 0.7753 | 0.7598 |
| 0.3687 | 2.2816 | 14000 | 0.6448 | 0.7631 | 0.7633 | 0.7705 | 0.7631 |
| 0.3746 | 2.3142 | 14200 | 0.6617 | 0.7723 | 0.7728 | 0.7785 | 0.7723 |
| 0.3798 | 2.3468 | 14400 | 0.6468 | 0.7727 | 0.7736 | 0.7814 | 0.7727 |
| 0.3779 | 2.3794 | 14600 | 0.6503 | 0.7691 | 0.7693 | 0.7773 | 0.7691 |
| 0.3871 | 2.4120 | 14800 | 0.6631 | 0.7618 | 0.7614 | 0.7702 | 0.7618 |
| 0.3859 | 2.4446 | 15000 | 0.6825 | 0.7635 | 0.7641 | 0.7772 | 0.7635 |
| 0.4049 | 2.4772 | 15200 | 0.6647 | 0.7655 | 0.7653 | 0.7749 | 0.7655 |
| 0.3812 | 2.5098 | 15400 | 0.7008 | 0.7558 | 0.7563 | 0.7697 | 0.7558 |
| 0.3874 | 2.5424 | 15600 | 0.6808 | 0.7671 | 0.7677 | 0.7764 | 0.7671 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0 |