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--- |
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base_model: xxxxxxxxx |
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tags: |
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- generated_from_trainer |
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datasets: |
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- AmazonScience/massive |
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metrics: |
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- f1 |
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model-index: |
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- name: massive_indo |
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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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# massive_indo |
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This model is a fine-tuned version of [xxxxxxxxx](https://huggingface.co/xxxxxxxxx) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0967 |
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- F1: 0.8702 |
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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: 5e-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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 0.747 | 1.39 | 500 | 1.0303 | 0.5703 | |
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| 0.5618 | 2.78 | 1000 | 0.9201 | 0.6479 | |
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| 0.3695 | 4.17 | 1500 | 0.8216 | 0.6990 | |
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| 0.3392 | 5.56 | 2000 | 0.7637 | 0.7335 | |
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| 0.2638 | 6.94 | 2500 | 0.8244 | 0.7678 | |
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| 0.1907 | 8.33 | 3000 | 0.7912 | 0.7979 | |
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| 0.1661 | 9.72 | 3500 | 0.8266 | 0.7835 | |
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| 0.1073 | 11.11 | 4000 | 0.8120 | 0.8139 | |
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| 0.1265 | 12.5 | 4500 | 0.8336 | 0.8344 | |
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| 0.0481 | 13.89 | 5000 | 0.8240 | 0.8518 | |
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| 0.0646 | 15.28 | 5500 | 0.9290 | 0.8333 | |
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| 0.0846 | 16.67 | 6000 | 0.9176 | 0.8461 | |
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| 0.0228 | 18.06 | 6500 | 0.9600 | 0.8529 | |
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| 0.0696 | 19.44 | 7000 | 0.9769 | 0.8525 | |
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| 0.0614 | 20.83 | 7500 | 0.9944 | 0.8545 | |
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| 0.0173 | 22.22 | 8000 | 1.0110 | 0.8550 | |
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| 0.004 | 23.61 | 8500 | 1.0140 | 0.8417 | |
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| 0.0032 | 25.0 | 9000 | 1.0771 | 0.8314 | |
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| 0.0453 | 26.39 | 9500 | 1.0173 | 0.8424 | |
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| 0.0471 | 27.78 | 10000 | 1.0068 | 0.8652 | |
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| 0.0128 | 29.17 | 10500 | 1.0595 | 0.8658 | |
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| 0.0027 | 30.56 | 11000 | 1.0596 | 0.8506 | |
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| 0.0198 | 31.94 | 11500 | 1.0468 | 0.8593 | |
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| 0.0027 | 33.33 | 12000 | 1.0537 | 0.8693 | |
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| 0.0114 | 34.72 | 12500 | 1.0512 | 0.8620 | |
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| 0.015 | 36.11 | 13000 | 1.0425 | 0.8813 | |
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| 0.005 | 37.5 | 13500 | 1.1092 | 0.8749 | |
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| 0.0038 | 38.89 | 14000 | 1.0829 | 0.8637 | |
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| 0.0096 | 40.28 | 14500 | 1.0902 | 0.8794 | |
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| 0.0007 | 41.67 | 15000 | 1.0994 | 0.8651 | |
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| 0.0109 | 43.06 | 15500 | 1.0957 | 0.8782 | |
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| 0.0026 | 44.44 | 16000 | 1.0997 | 0.8643 | |
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| 0.0061 | 45.83 | 16500 | 1.0853 | 0.8672 | |
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| 0.0005 | 47.22 | 17000 | 1.1082 | 0.8694 | |
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| 0.0005 | 48.61 | 17500 | 1.1016 | 0.8696 | |
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| 0.0028 | 50.0 | 18000 | 1.0967 | 0.8702 | |
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### Framework versions |
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- Transformers 4.34.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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