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--- |
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license: apache-2.0 |
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base_model: google/matcha-chartqa |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: ViMATCHA |
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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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# ViMATCHA |
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This model is a fine-tuned version of [google/matcha-chartqa](https://huggingface.co/google/matcha-chartqa) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5748 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 3.9077 | 0.1603 | 150 | 3.3649 | |
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| 3.1926 | 0.2137 | 200 | 2.7038 | |
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| 2.5565 | 0.2671 | 250 | 2.1718 | |
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| 2.1552 | 0.3205 | 300 | 1.7380 | |
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| 1.7834 | 0.3739 | 350 | 1.4287 | |
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| 1.428 | 0.4274 | 400 | 1.2302 | |
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| 1.3867 | 0.4808 | 450 | 1.1280 | |
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| 1.1496 | 0.5342 | 500 | 1.0437 | |
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| 1.2414 | 0.5876 | 550 | 0.9901 | |
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| 1.116 | 0.6410 | 600 | 0.9475 | |
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| 1.12 | 0.6944 | 650 | 0.9073 | |
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| 1.0132 | 0.7479 | 700 | 0.8859 | |
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| 0.9954 | 0.8013 | 750 | 0.8638 | |
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| 1.0057 | 0.8547 | 800 | 0.8496 | |
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| 0.9929 | 0.9081 | 850 | 0.8289 | |
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| 0.945 | 0.9615 | 900 | 0.8207 | |
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| 0.866 | 1.0150 | 950 | 0.8059 | |
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| 0.8901 | 1.0684 | 1000 | 0.7944 | |
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| 0.8382 | 1.1218 | 1050 | 0.7828 | |
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| 0.946 | 1.1752 | 1100 | 0.7748 | |
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| 0.9042 | 1.2286 | 1150 | 0.7662 | |
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| 0.8334 | 1.2821 | 1200 | 0.7549 | |
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| 0.8747 | 1.3355 | 1250 | 0.7501 | |
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| 0.8224 | 1.3889 | 1300 | 0.7424 | |
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| 0.7998 | 1.4423 | 1350 | 0.7382 | |
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| 0.9022 | 1.4957 | 1400 | 0.7341 | |
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| 0.8297 | 1.5491 | 1450 | 0.7226 | |
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| 0.8014 | 1.6026 | 1500 | 0.7165 | |
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| 0.8423 | 1.6560 | 1550 | 0.7129 | |
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| 0.7286 | 1.7094 | 1600 | 0.7063 | |
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| 0.7361 | 1.7628 | 1650 | 0.7040 | |
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| 0.8203 | 1.8162 | 1700 | 0.6982 | |
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| 0.8103 | 1.8697 | 1750 | 0.6945 | |
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| 0.7251 | 1.9231 | 1800 | 0.6926 | |
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| 0.7193 | 1.9765 | 1850 | 0.6910 | |
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| 0.8133 | 2.0299 | 1900 | 0.6843 | |
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| 0.7545 | 2.0833 | 1950 | 0.6862 | |
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| 0.8025 | 2.1368 | 2000 | 0.6768 | |
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| 0.7421 | 2.1902 | 2050 | 0.6769 | |
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| 0.6899 | 2.2436 | 2100 | 0.6744 | |
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| 0.7607 | 2.2970 | 2150 | 0.6690 | |
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| 0.739 | 2.3504 | 2200 | 0.6652 | |
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| 0.7095 | 2.4038 | 2250 | 0.6666 | |
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| 0.7392 | 2.4573 | 2300 | 0.6605 | |
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| 0.7307 | 2.5107 | 2350 | 0.6560 | |
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| 0.685 | 2.5641 | 2400 | 0.6579 | |
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| 0.6419 | 2.6175 | 2450 | 0.6499 | |
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| 0.6894 | 2.6709 | 2500 | 0.6532 | |
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| 0.6288 | 2.7244 | 2550 | 0.6482 | |
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| 0.7024 | 2.7778 | 2600 | 0.6471 | |
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| 0.7717 | 2.8312 | 2650 | 0.6475 | |
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| 0.7389 | 2.8846 | 2700 | 0.6434 | |
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| 0.6944 | 2.9380 | 2750 | 0.6406 | |
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| 0.6512 | 2.9915 | 2800 | 0.6405 | |
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| 0.7187 | 3.0449 | 2850 | 0.6410 | |
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| 0.6676 | 3.0983 | 2900 | 0.6383 | |
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| 0.6513 | 3.1517 | 2950 | 0.6359 | |
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| 0.5821 | 3.2051 | 3000 | 0.6345 | |
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| 0.6642 | 3.2585 | 3050 | 0.6338 | |
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| 0.6475 | 3.3120 | 3100 | 0.6311 | |
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| 0.6999 | 3.3654 | 3150 | 0.6281 | |
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| 0.696 | 3.4188 | 3200 | 0.6329 | |
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| 0.6129 | 3.4722 | 3250 | 0.6263 | |
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| 0.5601 | 3.5256 | 3300 | 0.6257 | |
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| 0.6658 | 3.5791 | 3350 | 0.6209 | |
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| 0.7212 | 3.6325 | 3400 | 0.6217 | |
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| 0.6392 | 3.6859 | 3450 | 0.6178 | |
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| 0.6877 | 3.7393 | 3500 | 0.6202 | |
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| 0.632 | 3.7927 | 3550 | 0.6171 | |
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| 0.6762 | 3.8462 | 3600 | 0.6175 | |
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| 0.6776 | 3.8996 | 3650 | 0.6122 | |
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| 0.6964 | 3.9530 | 3700 | 0.6120 | |
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| 0.6448 | 4.0064 | 3750 | 0.6134 | |
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| 0.6183 | 4.0598 | 3800 | 0.6125 | |
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| 0.6544 | 4.1132 | 3850 | 0.6105 | |
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| 0.6583 | 4.1667 | 3900 | 0.6102 | |
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| 0.6687 | 4.2201 | 3950 | 0.6083 | |
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| 0.6408 | 4.2735 | 4000 | 0.6078 | |
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| 0.6077 | 4.3269 | 4050 | 0.6051 | |
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| 0.5781 | 4.3803 | 4100 | 0.6078 | |
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| 0.645 | 4.4338 | 4150 | 0.6033 | |
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| 0.656 | 4.4872 | 4200 | 0.6044 | |
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| 0.562 | 4.5406 | 4250 | 0.6001 | |
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| 0.6568 | 4.5940 | 4300 | 0.6018 | |
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| 0.6572 | 4.6474 | 4350 | 0.6012 | |
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| 0.5749 | 4.7009 | 4400 | 0.6004 | |
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| 0.5811 | 4.7543 | 4450 | 0.5974 | |
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| 0.5765 | 4.8077 | 4500 | 0.6010 | |
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| 0.6177 | 4.8611 | 4550 | 0.5941 | |
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| 0.5371 | 4.9145 | 4600 | 0.5949 | |
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| 0.5863 | 4.9679 | 4650 | 0.5930 | |
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| 0.6274 | 5.0214 | 4700 | 0.5996 | |
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| 0.592 | 5.0748 | 4750 | 0.5972 | |
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| 0.6286 | 5.1282 | 4800 | 0.5936 | |
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| 0.6056 | 5.1816 | 4850 | 0.5954 | |
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| 0.5697 | 5.2350 | 4900 | 0.5943 | |
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| 0.5562 | 5.2885 | 4950 | 0.5923 | |
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| 0.5173 | 5.3419 | 5000 | 0.5944 | |
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| 0.6037 | 5.3953 | 5050 | 0.5943 | |
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| 0.6125 | 5.4487 | 5100 | 0.5921 | |
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| 0.5729 | 5.5021 | 5150 | 0.5920 | |
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| 0.561 | 5.5556 | 5200 | 0.5908 | |
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| 0.6244 | 5.6090 | 5250 | 0.5877 | |
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| 0.5338 | 5.6624 | 5300 | 0.5876 | |
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| 0.57 | 5.7158 | 5350 | 0.5912 | |
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| 0.555 | 5.7692 | 5400 | 0.5863 | |
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| 0.5914 | 5.8226 | 5450 | 0.5855 | |
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| 0.5785 | 5.8761 | 5500 | 0.5826 | |
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| 0.6014 | 5.9295 | 5550 | 0.5830 | |
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| 0.6192 | 5.9829 | 5600 | 0.5834 | |
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| 0.6025 | 6.0363 | 5650 | 0.5854 | |
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| 0.5327 | 6.0897 | 5700 | 0.5861 | |
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| 0.5958 | 6.1432 | 5750 | 0.5856 | |
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| 0.5319 | 6.1966 | 5800 | 0.5852 | |
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| 0.5722 | 6.25 | 5850 | 0.5848 | |
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| 0.585 | 6.3034 | 5900 | 0.5854 | |
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| 0.5549 | 6.3568 | 5950 | 0.5829 | |
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| 0.5559 | 6.4103 | 6000 | 0.5816 | |
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| 0.5539 | 6.4637 | 6050 | 0.5820 | |
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| 0.5847 | 6.5171 | 6100 | 0.5797 | |
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| 0.5964 | 6.5705 | 6150 | 0.5803 | |
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| 0.5613 | 6.6239 | 6200 | 0.5808 | |
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| 0.5194 | 6.6774 | 6250 | 0.5816 | |
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| 0.5664 | 6.7308 | 6300 | 0.5808 | |
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| 0.5971 | 6.7842 | 6350 | 0.5783 | |
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| 0.4937 | 6.8376 | 6400 | 0.5773 | |
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| 0.577 | 6.8910 | 6450 | 0.5779 | |
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| 0.5422 | 6.9444 | 6500 | 0.5799 | |
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| 0.4881 | 6.9979 | 6550 | 0.5789 | |
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| 0.5806 | 7.0513 | 6600 | 0.5802 | |
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| 0.5029 | 7.1047 | 6650 | 0.5798 | |
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| 0.5436 | 7.1581 | 6700 | 0.5782 | |
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| 0.5298 | 7.2115 | 6750 | 0.5783 | |
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| 0.5947 | 7.2650 | 6800 | 0.5777 | |
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| 0.4975 | 7.3184 | 6850 | 0.5762 | |
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| 0.5652 | 7.3718 | 6900 | 0.5761 | |
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| 0.5214 | 7.4252 | 6950 | 0.5793 | |
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| 0.535 | 7.4786 | 7000 | 0.5791 | |
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| 0.5035 | 7.5321 | 7050 | 0.5774 | |
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| 0.5004 | 7.5855 | 7100 | 0.5769 | |
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| 0.6187 | 7.6389 | 7150 | 0.5778 | |
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| 0.5042 | 7.6923 | 7200 | 0.5770 | |
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| 0.5116 | 7.7457 | 7250 | 0.5760 | |
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| 0.5178 | 7.7991 | 7300 | 0.5744 | |
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| 0.5154 | 7.8526 | 7350 | 0.5748 | |
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| 0.5641 | 7.9060 | 7400 | 0.5764 | |
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| 0.536 | 7.9594 | 7450 | 0.5762 | |
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| 0.5423 | 8.0128 | 7500 | 0.5743 | |
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| 0.5402 | 8.0662 | 7550 | 0.5755 | |
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| 0.5559 | 8.1197 | 7600 | 0.5755 | |
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| 0.4998 | 8.1731 | 7650 | 0.5769 | |
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| 0.5895 | 8.2265 | 7700 | 0.5777 | |
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| 0.5252 | 8.2799 | 7750 | 0.5786 | |
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| 0.4802 | 8.3333 | 7800 | 0.5774 | |
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| 0.5285 | 8.3868 | 7850 | 0.5742 | |
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| 0.4931 | 8.4402 | 7900 | 0.5740 | |
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| 0.5126 | 8.4936 | 7950 | 0.5761 | |
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| 0.5376 | 8.5470 | 8000 | 0.5754 | |
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| 0.4825 | 8.6004 | 8050 | 0.5761 | |
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| 0.509 | 8.6538 | 8100 | 0.5762 | |
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| 0.4823 | 8.7073 | 8150 | 0.5747 | |
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| 0.5379 | 8.7607 | 8200 | 0.5733 | |
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| 0.5283 | 8.8141 | 8250 | 0.5740 | |
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| 0.4662 | 8.8675 | 8300 | 0.5743 | |
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| 0.5325 | 8.9209 | 8350 | 0.5727 | |
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| 0.5628 | 8.9744 | 8400 | 0.5727 | |
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| 0.4885 | 9.0278 | 8450 | 0.5731 | |
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| 0.5187 | 9.0812 | 8500 | 0.5761 | |
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| 0.5286 | 9.1346 | 8550 | 0.5752 | |
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| 0.4738 | 9.1880 | 8600 | 0.5744 | |
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| 0.4931 | 9.2415 | 8650 | 0.5733 | |
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| 0.5403 | 9.2949 | 8700 | 0.5751 | |
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| 0.4927 | 9.3483 | 8750 | 0.5755 | |
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| 0.5415 | 9.4017 | 8800 | 0.5743 | |
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| 0.5627 | 9.4551 | 8850 | 0.5746 | |
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| 0.468 | 9.5085 | 8900 | 0.5743 | |
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| 0.5002 | 9.5620 | 8950 | 0.5751 | |
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| 0.4891 | 9.6154 | 9000 | 0.5740 | |
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| 0.5456 | 9.6688 | 9050 | 0.5747 | |
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| 0.5054 | 9.7222 | 9100 | 0.5747 | |
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| 0.5453 | 9.7756 | 9150 | 0.5741 | |
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| 0.477 | 9.8291 | 9200 | 0.5745 | |
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| 0.4798 | 9.8825 | 9250 | 0.5747 | |
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| 0.5003 | 9.9359 | 9300 | 0.5748 | |
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| 0.4785 | 9.9893 | 9350 | 0.5748 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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