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ABL_trad_2a

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  3. runs/Jun07_08-08-45_PcJavierOmen/events.out.tfevents.1717753619.PcJavierOmen.15760.1 +3 -0
README.md ADDED
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+ ---
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+ base_model: dccuchile/bert-base-spanish-wwm-cased
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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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+ - f1
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+ model-index:
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+ - name: ABL_trad_2a
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+ results: []
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+ ---
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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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+
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+ # ABL_trad_2a
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+
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+ This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.5485
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+ - Accuracy: 0.6917
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+ - F1: 0.6904
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 6
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+ - eval_batch_size: 6
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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: 78
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
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+ | 0.9204 | 1.0 | 2000 | 0.8942 | 0.5825 | 0.5814 |
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+ | 0.8116 | 2.0 | 4000 | 0.8397 | 0.615 | 0.6112 |
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+ | 0.7443 | 3.0 | 6000 | 0.8179 | 0.635 | 0.6340 |
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+ | 0.7074 | 4.0 | 8000 | 0.8130 | 0.65 | 0.6475 |
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+ | 0.6663 | 5.0 | 10000 | 0.8134 | 0.6625 | 0.6590 |
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+ | 0.6385 | 6.0 | 12000 | 0.8092 | 0.6708 | 0.6694 |
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+ | 0.5702 | 7.0 | 14000 | 0.8355 | 0.6708 | 0.6699 |
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+ | 0.5436 | 8.0 | 16000 | 0.8662 | 0.6758 | 0.6728 |
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+ | 0.5002 | 9.0 | 18000 | 0.9040 | 0.6825 | 0.6804 |
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+ | 0.4602 | 10.0 | 20000 | 0.9345 | 0.6967 | 0.6948 |
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+ | 0.4324 | 11.0 | 22000 | 1.0021 | 0.6858 | 0.6842 |
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+ | 0.3862 | 12.0 | 24000 | 1.0713 | 0.6917 | 0.6899 |
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+ | 0.3586 | 13.0 | 26000 | 1.1688 | 0.7017 | 0.6989 |
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+ | 0.3536 | 14.0 | 28000 | 1.2699 | 0.695 | 0.6923 |
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+ | 0.3041 | 15.0 | 30000 | 1.4034 | 0.6917 | 0.6911 |
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+ | 0.2851 | 16.0 | 32000 | 1.5512 | 0.6917 | 0.6897 |
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+ | 0.3071 | 17.0 | 34000 | 1.6367 | 0.69 | 0.6885 |
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+ | 0.2586 | 18.0 | 36000 | 1.7689 | 0.6892 | 0.6880 |
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+ | 0.2192 | 19.0 | 38000 | 1.9568 | 0.6842 | 0.6828 |
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+ | 0.2418 | 20.0 | 40000 | 2.0536 | 0.6808 | 0.6770 |
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+ | 0.2066 | 21.0 | 42000 | 2.1762 | 0.6917 | 0.6899 |
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+ | 0.1449 | 22.0 | 44000 | 2.3044 | 0.69 | 0.6879 |
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+ | 0.1669 | 23.0 | 46000 | 2.3854 | 0.6908 | 0.6891 |
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+ | 0.1376 | 24.0 | 48000 | 2.5111 | 0.6883 | 0.6849 |
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+ | 0.1434 | 25.0 | 50000 | 2.5862 | 0.685 | 0.6829 |
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+ | 0.1123 | 26.0 | 52000 | 2.6845 | 0.685 | 0.6825 |
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+ | 0.0949 | 27.0 | 54000 | 2.7896 | 0.6825 | 0.6804 |
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+ | 0.1264 | 28.0 | 56000 | 2.8471 | 0.6908 | 0.6887 |
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+ | 0.0774 | 29.0 | 58000 | 2.8967 | 0.6883 | 0.6860 |
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+ | 0.1046 | 30.0 | 60000 | 2.9571 | 0.6867 | 0.6837 |
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+ | 0.0967 | 31.0 | 62000 | 2.9687 | 0.6892 | 0.6858 |
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+ | 0.0689 | 32.0 | 64000 | 3.0554 | 0.6917 | 0.6887 |
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+ | 0.069 | 33.0 | 66000 | 3.0982 | 0.6917 | 0.6888 |
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+ | 0.044 | 34.0 | 68000 | 3.1798 | 0.6917 | 0.6898 |
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+ | 0.0654 | 35.0 | 70000 | 3.2407 | 0.685 | 0.6821 |
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+ | 0.0438 | 36.0 | 72000 | 3.1972 | 0.6908 | 0.6881 |
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+ | 0.0553 | 37.0 | 74000 | 3.2033 | 0.6967 | 0.6947 |
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+ | 0.0534 | 38.0 | 76000 | 3.2928 | 0.6883 | 0.6856 |
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+ | 0.0568 | 39.0 | 78000 | 3.3607 | 0.6933 | 0.6893 |
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+ | 0.03 | 40.0 | 80000 | 3.2983 | 0.6867 | 0.6836 |
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+ | 0.0539 | 41.0 | 82000 | 3.2896 | 0.6908 | 0.6870 |
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+ | 0.0459 | 42.0 | 84000 | 3.3401 | 0.6883 | 0.6855 |
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+ | 0.0322 | 43.0 | 86000 | 3.3879 | 0.6883 | 0.6847 |
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+ | 0.0251 | 44.0 | 88000 | 3.3517 | 0.6875 | 0.6855 |
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+ | 0.063 | 45.0 | 90000 | 3.3609 | 0.6875 | 0.6856 |
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+ | 0.0343 | 46.0 | 92000 | 3.4785 | 0.6825 | 0.6801 |
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+ | 0.052 | 47.0 | 94000 | 3.4663 | 0.6817 | 0.6794 |
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+ | 0.038 | 48.0 | 96000 | 3.4157 | 0.6908 | 0.6889 |
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+ | 0.0312 | 49.0 | 98000 | 3.3996 | 0.6958 | 0.6933 |
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+ | 0.0469 | 50.0 | 100000 | 3.3978 | 0.6942 | 0.6918 |
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+ | 0.0405 | 51.0 | 102000 | 3.4383 | 0.6833 | 0.6801 |
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+ | 0.0438 | 52.0 | 104000 | 3.3833 | 0.7025 | 0.7004 |
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+ | 0.0366 | 53.0 | 106000 | 3.5241 | 0.6917 | 0.6879 |
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+ | 0.0415 | 54.0 | 108000 | 3.4236 | 0.6992 | 0.6972 |
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+ | 0.0315 | 55.0 | 110000 | 3.3053 | 0.7033 | 0.7009 |
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+ | 0.041 | 56.0 | 112000 | 3.4287 | 0.6975 | 0.6961 |
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+ | 0.024 | 57.0 | 114000 | 3.4783 | 0.695 | 0.6919 |
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+ | 0.0263 | 58.0 | 116000 | 3.5307 | 0.6942 | 0.6917 |
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+ | 0.0231 | 59.0 | 118000 | 3.4495 | 0.6908 | 0.6883 |
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+ | 0.0269 | 60.0 | 120000 | 3.4664 | 0.6925 | 0.6907 |
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+ | 0.0152 | 61.0 | 122000 | 3.4655 | 0.6917 | 0.6893 |
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+ | 0.025 | 62.0 | 124000 | 3.4954 | 0.6967 | 0.6957 |
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+ | 0.0313 | 63.0 | 126000 | 3.4727 | 0.6967 | 0.6942 |
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+ | 0.0175 | 64.0 | 128000 | 3.5688 | 0.6908 | 0.6892 |
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+ | 0.0319 | 65.0 | 130000 | 3.4812 | 0.6975 | 0.6963 |
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+ | 0.0382 | 66.0 | 132000 | 3.5716 | 0.6975 | 0.6942 |
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+ | 0.0168 | 67.0 | 134000 | 3.5241 | 0.7008 | 0.6978 |
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+ | 0.0351 | 68.0 | 136000 | 3.5020 | 0.6917 | 0.6889 |
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+ | 0.0185 | 69.0 | 138000 | 3.4793 | 0.6908 | 0.6894 |
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+ | 0.0264 | 70.0 | 140000 | 3.5716 | 0.6883 | 0.6847 |
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+ | 0.0236 | 71.0 | 142000 | 3.5403 | 0.69 | 0.6861 |
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+ | 0.0238 | 72.0 | 144000 | 3.5622 | 0.6917 | 0.6886 |
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+ | 0.0163 | 73.0 | 146000 | 3.4998 | 0.7008 | 0.6984 |
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+ | 0.0229 | 74.0 | 148000 | 3.5659 | 0.6925 | 0.6898 |
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+ | 0.0168 | 75.0 | 150000 | 3.5080 | 0.6983 | 0.6958 |
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+ | 0.0149 | 76.0 | 152000 | 3.4678 | 0.7008 | 0.6985 |
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+ | 0.0175 | 77.0 | 154000 | 3.5733 | 0.6967 | 0.6939 |
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+ | 0.0118 | 78.0 | 156000 | 3.5485 | 0.6917 | 0.6904 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.1
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