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--- |
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language: |
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- tr |
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tags: |
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- zero-shot-classification |
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- nli |
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- pytorch |
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pipeline_tag: zero-shot-classification |
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license: apache-2.0 |
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datasets: |
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- nli_tr |
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metrics: |
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- accuracy |
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widget: |
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- text: "Dolar yükselmeye devam ediyor." |
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candidate_labels: "ekonomi, siyaset, spor" |
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- text: "Senaryo çok saçmaydı, beğendim diyemem." |
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candidate_labels: "olumlu, olumsuz" |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-base-turkish-cased_allnli_tr |
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This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6481 |
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- Accuracy: 0.7381 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.94 | 0.03 | 1000 | 0.9074 | 0.5813 | |
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| 0.8102 | 0.07 | 2000 | 0.8802 | 0.5949 | |
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| 0.7737 | 0.1 | 3000 | 0.8491 | 0.6155 | |
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| 0.7576 | 0.14 | 4000 | 0.8283 | 0.6261 | |
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| 0.7286 | 0.17 | 5000 | 0.8150 | 0.6362 | |
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| 0.7162 | 0.2 | 6000 | 0.7998 | 0.6400 | |
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| 0.7092 | 0.24 | 7000 | 0.7830 | 0.6565 | |
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| 0.6962 | 0.27 | 8000 | 0.7653 | 0.6629 | |
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| 0.6876 | 0.31 | 9000 | 0.7630 | 0.6687 | |
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| 0.6778 | 0.34 | 10000 | 0.7475 | 0.6739 | |
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| 0.6737 | 0.37 | 11000 | 0.7495 | 0.6781 | |
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| 0.6712 | 0.41 | 12000 | 0.7350 | 0.6826 | |
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| 0.6559 | 0.44 | 13000 | 0.7274 | 0.6897 | |
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| 0.6493 | 0.48 | 14000 | 0.7248 | 0.6902 | |
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| 0.6483 | 0.51 | 15000 | 0.7263 | 0.6858 | |
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| 0.6445 | 0.54 | 16000 | 0.7070 | 0.6978 | |
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| 0.6467 | 0.58 | 17000 | 0.7083 | 0.6981 | |
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| 0.6332 | 0.61 | 18000 | 0.6996 | 0.7004 | |
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| 0.6288 | 0.65 | 19000 | 0.6979 | 0.6978 | |
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| 0.6308 | 0.68 | 20000 | 0.6912 | 0.7040 | |
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| 0.622 | 0.71 | 21000 | 0.6904 | 0.7092 | |
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| 0.615 | 0.75 | 22000 | 0.6872 | 0.7094 | |
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| 0.6186 | 0.78 | 23000 | 0.6877 | 0.7075 | |
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| 0.6183 | 0.82 | 24000 | 0.6818 | 0.7111 | |
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| 0.6115 | 0.85 | 25000 | 0.6856 | 0.7122 | |
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| 0.608 | 0.88 | 26000 | 0.6697 | 0.7179 | |
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| 0.6071 | 0.92 | 27000 | 0.6727 | 0.7181 | |
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| 0.601 | 0.95 | 28000 | 0.6798 | 0.7118 | |
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| 0.6018 | 0.99 | 29000 | 0.6854 | 0.7071 | |
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| 0.5762 | 1.02 | 30000 | 0.6697 | 0.7214 | |
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| 0.5507 | 1.05 | 31000 | 0.6710 | 0.7185 | |
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| 0.5575 | 1.09 | 32000 | 0.6709 | 0.7226 | |
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| 0.5493 | 1.12 | 33000 | 0.6659 | 0.7191 | |
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| 0.5464 | 1.15 | 34000 | 0.6709 | 0.7232 | |
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| 0.5595 | 1.19 | 35000 | 0.6642 | 0.7220 | |
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| 0.5446 | 1.22 | 36000 | 0.6709 | 0.7202 | |
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| 0.5524 | 1.26 | 37000 | 0.6751 | 0.7148 | |
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| 0.5473 | 1.29 | 38000 | 0.6642 | 0.7209 | |
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| 0.5477 | 1.32 | 39000 | 0.6662 | 0.7223 | |
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| 0.5522 | 1.36 | 40000 | 0.6586 | 0.7227 | |
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| 0.5406 | 1.39 | 41000 | 0.6602 | 0.7258 | |
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| 0.54 | 1.43 | 42000 | 0.6564 | 0.7273 | |
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| 0.5458 | 1.46 | 43000 | 0.6780 | 0.7213 | |
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| 0.5448 | 1.49 | 44000 | 0.6561 | 0.7235 | |
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| 0.5418 | 1.53 | 45000 | 0.6600 | 0.7253 | |
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| 0.5408 | 1.56 | 46000 | 0.6616 | 0.7274 | |
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| 0.5451 | 1.6 | 47000 | 0.6557 | 0.7283 | |
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| 0.5385 | 1.63 | 48000 | 0.6583 | 0.7295 | |
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| 0.5261 | 1.66 | 49000 | 0.6468 | 0.7325 | |
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| 0.5364 | 1.7 | 50000 | 0.6447 | 0.7329 | |
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| 0.5294 | 1.73 | 51000 | 0.6429 | 0.7320 | |
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| 0.5332 | 1.77 | 52000 | 0.6508 | 0.7272 | |
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| 0.5274 | 1.8 | 53000 | 0.6492 | 0.7326 | |
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| 0.5286 | 1.83 | 54000 | 0.6470 | 0.7318 | |
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| 0.5359 | 1.87 | 55000 | 0.6393 | 0.7354 | |
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| 0.5366 | 1.9 | 56000 | 0.6445 | 0.7367 | |
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| 0.5296 | 1.94 | 57000 | 0.6413 | 0.7313 | |
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| 0.5346 | 1.97 | 58000 | 0.6393 | 0.7315 | |
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| 0.5264 | 2.0 | 59000 | 0.6448 | 0.7357 | |
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| 0.4857 | 2.04 | 60000 | 0.6640 | 0.7335 | |
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| 0.4888 | 2.07 | 61000 | 0.6612 | 0.7318 | |
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| 0.4964 | 2.11 | 62000 | 0.6516 | 0.7337 | |
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| 0.493 | 2.14 | 63000 | 0.6503 | 0.7356 | |
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| 0.4961 | 2.17 | 64000 | 0.6519 | 0.7348 | |
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| 0.4847 | 2.21 | 65000 | 0.6517 | 0.7327 | |
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| 0.483 | 2.24 | 66000 | 0.6555 | 0.7310 | |
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| 0.4857 | 2.28 | 67000 | 0.6525 | 0.7312 | |
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| 0.484 | 2.31 | 68000 | 0.6444 | 0.7342 | |
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| 0.4792 | 2.34 | 69000 | 0.6508 | 0.7330 | |
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| 0.488 | 2.38 | 70000 | 0.6513 | 0.7344 | |
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| 0.472 | 2.41 | 71000 | 0.6547 | 0.7346 | |
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| 0.4872 | 2.45 | 72000 | 0.6500 | 0.7342 | |
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| 0.4782 | 2.48 | 73000 | 0.6585 | 0.7358 | |
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| 0.481 | 2.51 | 74000 | 0.6477 | 0.7356 | |
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| 0.4822 | 2.55 | 75000 | 0.6587 | 0.7346 | |
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| 0.4728 | 2.58 | 76000 | 0.6572 | 0.7340 | |
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| 0.4841 | 2.62 | 77000 | 0.6443 | 0.7374 | |
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| 0.4885 | 2.65 | 78000 | 0.6494 | 0.7362 | |
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| 0.4752 | 2.68 | 79000 | 0.6509 | 0.7382 | |
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| 0.4883 | 2.72 | 80000 | 0.6457 | 0.7371 | |
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| 0.4888 | 2.75 | 81000 | 0.6497 | 0.7364 | |
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| 0.4844 | 2.79 | 82000 | 0.6481 | 0.7376 | |
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| 0.4833 | 2.82 | 83000 | 0.6451 | 0.7389 | |
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| 0.48 | 2.85 | 84000 | 0.6423 | 0.7373 | |
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| 0.4832 | 2.89 | 85000 | 0.6477 | 0.7357 | |
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| 0.4805 | 2.92 | 86000 | 0.6464 | 0.7379 | |
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| 0.4775 | 2.96 | 87000 | 0.6477 | 0.7380 | |
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| 0.4843 | 2.99 | 88000 | 0.6481 | 0.7381 | |
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### Framework versions |
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- Transformers 4.12.3 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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