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--- |
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license: other |
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base_model: google/gemma-2b |
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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: gemma_2b_ledgar |
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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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# gemma_2b_ledgar |
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4836 |
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- Accuracy: 0.8682 |
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- F1 Macro: 0.7942 |
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- F1 Micro: 0.8682 |
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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-06 |
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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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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 64 |
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- total_eval_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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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:| |
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| 1.3984 | 0.11 | 100 | 1.1195 | 0.7373 | 0.5766 | 0.7373 | |
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| 0.8017 | 0.21 | 200 | 0.7836 | 0.7952 | 0.6837 | 0.7952 | |
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| 0.7073 | 0.32 | 300 | 0.7108 | 0.8103 | 0.7035 | 0.8103 | |
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| 0.6628 | 0.43 | 400 | 0.6609 | 0.8197 | 0.7111 | 0.8197 | |
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| 0.6884 | 0.53 | 500 | 0.6126 | 0.8291 | 0.7293 | 0.8291 | |
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| 0.6117 | 0.64 | 600 | 0.5862 | 0.8397 | 0.7521 | 0.8397 | |
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| 0.5478 | 0.75 | 700 | 0.5648 | 0.8437 | 0.7615 | 0.8437 | |
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| 0.576 | 0.85 | 800 | 0.5639 | 0.8409 | 0.7532 | 0.8409 | |
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| 0.5161 | 0.96 | 900 | 0.5263 | 0.8508 | 0.7735 | 0.8508 | |
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| 0.2538 | 1.07 | 1000 | 0.5196 | 0.8594 | 0.7784 | 0.8594 | |
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| 0.2496 | 1.17 | 1100 | 0.5230 | 0.8568 | 0.7772 | 0.8568 | |
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| 0.2782 | 1.28 | 1200 | 0.5193 | 0.8633 | 0.7831 | 0.8633 | |
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| 0.2084 | 1.39 | 1300 | 0.5217 | 0.8593 | 0.7802 | 0.8593 | |
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| 0.2054 | 1.49 | 1400 | 0.5111 | 0.8612 | 0.7823 | 0.8612 | |
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| 0.2505 | 1.6 | 1500 | 0.5194 | 0.8602 | 0.7760 | 0.8602 | |
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| 0.2695 | 1.71 | 1600 | 0.5041 | 0.8616 | 0.7874 | 0.8616 | |
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| 0.2427 | 1.81 | 1700 | 0.4895 | 0.8664 | 0.7884 | 0.8664 | |
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| 0.2271 | 1.92 | 1800 | 0.4836 | 0.8682 | 0.7942 | 0.8682 | |
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| 0.0644 | 2.03 | 1900 | 0.4861 | 0.8686 | 0.7921 | 0.8686 | |
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| 0.0451 | 2.13 | 2000 | 0.4934 | 0.8723 | 0.7964 | 0.8723 | |
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| 0.0539 | 2.24 | 2100 | 0.4957 | 0.8703 | 0.7968 | 0.8703 | |
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| 0.0419 | 2.35 | 2200 | 0.4946 | 0.8717 | 0.7984 | 0.8717 | |
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| 0.0639 | 2.45 | 2300 | 0.4900 | 0.873 | 0.7999 | 0.873 | |
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| 0.0422 | 2.56 | 2400 | 0.4956 | 0.8725 | 0.7985 | 0.8725 | |
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| 0.0485 | 2.67 | 2500 | 0.4953 | 0.8732 | 0.7994 | 0.8732 | |
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| 0.0508 | 2.77 | 2600 | 0.4951 | 0.8738 | 0.8022 | 0.8738 | |
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| 0.047 | 2.88 | 2700 | 0.4934 | 0.8747 | 0.8027 | 0.8747 | |
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| 0.043 | 2.99 | 2800 | 0.4926 | 0.8749 | 0.8025 | 0.8749 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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