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fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-base-uncased-without-ITTL-without-freeze-LR-1e-05

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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-base-uncased-without-ITTL-without-freeze-LR-1e-05
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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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+ # fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-base-uncased-without-ITTL-without-freeze-LR-1e-05
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+
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+ This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1493
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+ - Exact Match: 60.5585
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+ - F1: 75.1071
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+ - Precision: 76.3329
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+ - Recall: 81.4497
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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: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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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+ - lr_scheduler_warmup_ratio: 0.06
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+ - num_epochs: 16
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
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+ | 6.1192 | 0.5 | 38 | 4.8873 | 4.0140 | 16.4529 | 16.4981 | 38.0734 |
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+ | 5.4384 | 0.99 | 76 | 2.8628 | 16.7539 | 29.8825 | 29.0280 | 50.2974 |
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+ | 3.1591 | 1.5 | 114 | 2.4374 | 24.2583 | 36.1059 | 35.6027 | 53.7380 |
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+ | 2.4014 | 1.99 | 152 | 2.2367 | 30.0175 | 41.9697 | 41.9505 | 53.7706 |
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+ | 2.4014 | 2.5 | 190 | 2.0861 | 33.5079 | 45.2875 | 45.6044 | 55.6393 |
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+ | 2.1121 | 2.99 | 228 | 1.8134 | 41.1867 | 52.1539 | 53.0988 | 60.0665 |
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+ | 1.8437 | 3.5 | 266 | 1.5977 | 46.0733 | 59.5453 | 60.0688 | 69.5715 |
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+ | 1.5105 | 3.99 | 304 | 1.3928 | 51.4834 | 65.0228 | 65.8592 | 72.3641 |
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+ | 1.5105 | 4.5 | 342 | 1.3275 | 54.9738 | 68.7090 | 69.9803 | 75.8245 |
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+ | 1.2337 | 4.99 | 380 | 1.2185 | 56.8935 | 70.5705 | 72.3556 | 75.7959 |
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+ | 1.1333 | 5.5 | 418 | 1.2537 | 57.2426 | 70.9476 | 72.6953 | 75.6818 |
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+ | 0.9915 | 5.99 | 456 | 1.1484 | 58.4642 | 73.3124 | 75.0975 | 78.1646 |
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+ | 0.9915 | 6.5 | 494 | 1.1665 | 59.3368 | 74.0503 | 75.6279 | 79.6335 |
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+ | 0.8931 | 6.99 | 532 | 1.1316 | 59.6859 | 74.4803 | 75.9433 | 79.8837 |
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+ | 0.8498 | 7.5 | 570 | 1.1414 | 60.9075 | 75.3350 | 76.5606 | 81.1204 |
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+ | 0.7783 | 7.99 | 608 | 1.1332 | 60.3839 | 75.2719 | 76.8970 | 81.1038 |
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+ | 0.7783 | 8.5 | 646 | 1.1133 | 61.2565 | 75.3214 | 76.9111 | 81.1566 |
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+ | 0.7209 | 8.99 | 684 | 1.1493 | 60.5585 | 75.1071 | 76.3329 | 81.4497 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu117
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+ - Datasets 2.2.0
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+ - Tokenizers 0.13.2