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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: detect-femicide-news-bert-nl-None |
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results: [] |
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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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# detect-femicide-news-bert-nl-None |
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This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8162 |
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- Accuracy: 0.75 |
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- Precision Neg: 0.8235 |
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- Precision Pos: 0.6364 |
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- Recall Neg: 0.7778 |
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- Recall Pos: 0.7 |
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- F1 Score Neg: 0.8000 |
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- F1 Score Pos: 0.6667 |
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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: 1e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 8 |
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- seed: 1996 |
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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: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Neg | Precision Pos | Recall Neg | Recall Pos | F1 Score Neg | F1 Score Pos | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:| |
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| 0.6636 | 1.0 | 23 | 0.6474 | 0.6429 | 0.8333 | 0.5 | 0.5556 | 0.8 | 0.6667 | 0.6154 | |
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| 0.572 | 2.0 | 46 | 0.5653 | 0.6071 | 0.6842 | 0.4444 | 0.7222 | 0.4 | 0.7027 | 0.4211 | |
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| 0.502 | 3.0 | 69 | 0.5601 | 0.6786 | 0.8462 | 0.5333 | 0.6111 | 0.8 | 0.7097 | 0.64 | |
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| 0.4576 | 4.0 | 92 | 0.5199 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.3803 | 5.0 | 115 | 0.5219 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.3466 | 6.0 | 138 | 0.5125 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.3325 | 7.0 | 161 | 0.4930 | 0.75 | 0.7895 | 0.6667 | 0.8333 | 0.6 | 0.8108 | 0.6316 | |
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| 0.3022 | 8.0 | 184 | 0.5144 | 0.75 | 0.7895 | 0.6667 | 0.8333 | 0.6 | 0.8108 | 0.6316 | |
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| 0.2854 | 9.0 | 207 | 0.5588 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2797 | 10.0 | 230 | 0.5700 | 0.6786 | 0.7647 | 0.5455 | 0.7222 | 0.6 | 0.7429 | 0.5714 | |
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| 0.2645 | 11.0 | 253 | 0.5806 | 0.6786 | 0.7647 | 0.5455 | 0.7222 | 0.6 | 0.7429 | 0.5714 | |
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| 0.2411 | 12.0 | 276 | 0.5642 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2554 | 13.0 | 299 | 0.6364 | 0.6786 | 0.8 | 0.5385 | 0.6667 | 0.7 | 0.7273 | 0.6087 | |
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| 0.2682 | 14.0 | 322 | 0.5656 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2429 | 15.0 | 345 | 0.6249 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2368 | 16.0 | 368 | 0.5914 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2398 | 17.0 | 391 | 0.7456 | 0.6786 | 0.8462 | 0.5333 | 0.6111 | 0.8 | 0.7097 | 0.64 | |
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| 0.251 | 18.0 | 414 | 0.5602 | 0.75 | 0.7895 | 0.6667 | 0.8333 | 0.6 | 0.8108 | 0.6316 | |
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| 0.2403 | 19.0 | 437 | 0.5803 | 0.75 | 0.7895 | 0.6667 | 0.8333 | 0.6 | 0.8108 | 0.6316 | |
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| 0.2237 | 20.0 | 460 | 0.8165 | 0.6786 | 0.9091 | 0.5294 | 0.5556 | 0.9 | 0.6897 | 0.6667 | |
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| 0.2481 | 21.0 | 483 | 0.6195 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2357 | 22.0 | 506 | 0.7081 | 0.6429 | 0.75 | 0.5 | 0.6667 | 0.6 | 0.7059 | 0.5455 | |
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| 0.2227 | 23.0 | 529 | 0.6786 | 0.6786 | 0.8 | 0.5385 | 0.6667 | 0.7 | 0.7273 | 0.6087 | |
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| 0.2137 | 24.0 | 552 | 0.6567 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.2216 | 25.0 | 575 | 0.7286 | 0.7143 | 0.8571 | 0.5714 | 0.6667 | 0.8 | 0.75 | 0.6667 | |
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| 0.2289 | 26.0 | 598 | 0.6146 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2268 | 27.0 | 621 | 0.6721 | 0.6786 | 0.8 | 0.5385 | 0.6667 | 0.7 | 0.7273 | 0.6087 | |
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| 0.2208 | 28.0 | 644 | 0.6894 | 0.6786 | 0.8 | 0.5385 | 0.6667 | 0.7 | 0.7273 | 0.6087 | |
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| 0.2252 | 29.0 | 667 | 0.5986 | 0.7857 | 0.8 | 0.75 | 0.8889 | 0.6 | 0.8421 | 0.6667 | |
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| 0.2127 | 30.0 | 690 | 0.6868 | 0.6429 | 0.75 | 0.5 | 0.6667 | 0.6 | 0.7059 | 0.5455 | |
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| 0.2259 | 31.0 | 713 | 0.6682 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2253 | 32.0 | 736 | 0.8906 | 0.6786 | 0.9091 | 0.5294 | 0.5556 | 0.9 | 0.6897 | 0.6667 | |
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| 0.2421 | 33.0 | 759 | 0.6461 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2181 | 34.0 | 782 | 0.7014 | 0.6786 | 0.7647 | 0.5455 | 0.7222 | 0.6 | 0.7429 | 0.5714 | |
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| 0.2199 | 35.0 | 805 | 0.7655 | 0.6786 | 0.8 | 0.5385 | 0.6667 | 0.7 | 0.7273 | 0.6087 | |
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| 0.201 | 36.0 | 828 | 0.7356 | 0.6429 | 0.75 | 0.5 | 0.6667 | 0.6 | 0.7059 | 0.5455 | |
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| 0.2192 | 37.0 | 851 | 0.6958 | 0.6786 | 0.7647 | 0.5455 | 0.7222 | 0.6 | 0.7429 | 0.5714 | |
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| 0.2164 | 38.0 | 874 | 0.7475 | 0.6429 | 0.75 | 0.5 | 0.6667 | 0.6 | 0.7059 | 0.5455 | |
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| 0.22 | 39.0 | 897 | 0.6847 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2177 | 40.0 | 920 | 0.6463 | 0.7857 | 0.8 | 0.75 | 0.8889 | 0.6 | 0.8421 | 0.6667 | |
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| 0.2126 | 41.0 | 943 | 0.6793 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2069 | 42.0 | 966 | 0.7303 | 0.7143 | 0.8125 | 0.5833 | 0.7222 | 0.7 | 0.7647 | 0.6364 | |
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| 0.2099 | 43.0 | 989 | 0.6598 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2104 | 44.0 | 1012 | 0.7276 | 0.6786 | 0.7647 | 0.5455 | 0.7222 | 0.6 | 0.7429 | 0.5714 | |
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| 0.213 | 45.0 | 1035 | 0.7099 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2083 | 46.0 | 1058 | 0.7545 | 0.6786 | 0.8 | 0.5385 | 0.6667 | 0.7 | 0.7273 | 0.6087 | |
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| 0.1958 | 47.0 | 1081 | 0.6533 | 0.75 | 0.7895 | 0.6667 | 0.8333 | 0.6 | 0.8108 | 0.6316 | |
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| 0.2096 | 48.0 | 1104 | 0.7141 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2134 | 49.0 | 1127 | 0.7008 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.203 | 50.0 | 1150 | 0.6557 | 0.75 | 0.7895 | 0.6667 | 0.8333 | 0.6 | 0.8108 | 0.6316 | |
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| 0.2024 | 51.0 | 1173 | 0.7348 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.2095 | 52.0 | 1196 | 0.7708 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1997 | 53.0 | 1219 | 0.7106 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2048 | 54.0 | 1242 | 0.7530 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1963 | 55.0 | 1265 | 0.7520 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2039 | 56.0 | 1288 | 0.7230 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2023 | 57.0 | 1311 | 0.7644 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.2022 | 58.0 | 1334 | 0.7666 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1898 | 59.0 | 1357 | 0.7961 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.2155 | 60.0 | 1380 | 0.7763 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1948 | 61.0 | 1403 | 0.7545 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.2124 | 62.0 | 1426 | 0.7344 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1979 | 63.0 | 1449 | 0.7676 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1958 | 64.0 | 1472 | 0.7567 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1946 | 65.0 | 1495 | 0.7349 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1888 | 66.0 | 1518 | 0.7472 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1889 | 67.0 | 1541 | 0.7202 | 0.7857 | 0.8 | 0.75 | 0.8889 | 0.6 | 0.8421 | 0.6667 | |
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| 0.2077 | 68.0 | 1564 | 0.7193 | 0.7857 | 0.8 | 0.75 | 0.8889 | 0.6 | 0.8421 | 0.6667 | |
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| 0.1882 | 69.0 | 1587 | 0.7541 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1903 | 70.0 | 1610 | 0.8058 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.2017 | 71.0 | 1633 | 0.7862 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1929 | 72.0 | 1656 | 0.8000 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.192 | 73.0 | 1679 | 0.8199 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1903 | 74.0 | 1702 | 0.8044 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1953 | 75.0 | 1725 | 0.7943 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1908 | 76.0 | 1748 | 0.7805 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1975 | 77.0 | 1771 | 0.7595 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1943 | 78.0 | 1794 | 0.7908 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.192 | 79.0 | 1817 | 0.8389 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1879 | 80.0 | 1840 | 0.7925 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1933 | 81.0 | 1863 | 0.8149 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1867 | 82.0 | 1886 | 0.7925 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1906 | 83.0 | 1909 | 0.8118 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1895 | 84.0 | 1932 | 0.8108 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1925 | 85.0 | 1955 | 0.7962 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1851 | 86.0 | 1978 | 0.7942 | 0.7143 | 0.7778 | 0.6 | 0.7778 | 0.6 | 0.7778 | 0.6 | |
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| 0.1952 | 87.0 | 2001 | 0.8104 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1821 | 88.0 | 2024 | 0.8187 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1946 | 89.0 | 2047 | 0.8378 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1904 | 90.0 | 2070 | 0.8407 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1931 | 91.0 | 2093 | 0.8351 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1883 | 92.0 | 2116 | 0.8269 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1845 | 93.0 | 2139 | 0.8110 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1883 | 94.0 | 2162 | 0.8209 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1991 | 95.0 | 2185 | 0.8194 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.187 | 96.0 | 2208 | 0.8182 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1842 | 97.0 | 2231 | 0.8168 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1837 | 98.0 | 2254 | 0.8164 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.1849 | 99.0 | 2277 | 0.8173 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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| 0.189 | 100.0 | 2300 | 0.8162 | 0.75 | 0.8235 | 0.6364 | 0.7778 | 0.7 | 0.8000 | 0.6667 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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