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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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datasets: |
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- imagefolder |
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metrics: |
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- rouge |
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- bleu |
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model-index: |
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- name: saved_model_git-base |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.3058988098589094 |
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- name: Bleu |
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type: bleu |
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value: 0.10580263597345552 |
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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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# saved_model_git-base |
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This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2473 |
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- Wer Score: 2.7325 |
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- Rouge1: 0.3059 |
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- Rouge2: 0.1738 |
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- Rougel: 0.2760 |
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- Rougelsum: 0.2759 |
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- Meteor: 0.4991 |
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- Bleu: 0.1058 |
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- Bleu1: 0.2113 |
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- Bleu2: 0.1272 |
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- Bleu3: 0.0824 |
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- Bleu4: 0.0566 |
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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: 112 |
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- eval_batch_size: 112 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 224 |
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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: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer Score | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | Bleu | Bleu1 | Bleu2 | Bleu3 | Bleu4 | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:------:|:---------:|:------:|:------:|:------:|:------:|:------:|:------:| |
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| 0.774 | 1.7 | 1000 | 0.2771 | 3.5978 | 0.2206 | 0.1145 | 0.1981 | 0.1981 | 0.4163 | 0.0774 | 0.1712 | 0.0965 | 0.0580 | 0.0375 | |
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| 0.2763 | 3.4 | 2000 | 0.2537 | 3.6165 | 0.2273 | 0.1237 | 0.2050 | 0.2050 | 0.4374 | 0.0840 | 0.1757 | 0.1032 | 0.0642 | 0.0428 | |
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| 0.2567 | 5.11 | 3000 | 0.2423 | 3.5963 | 0.2317 | 0.1299 | 0.2105 | 0.2105 | 0.4500 | 0.0881 | 0.1790 | 0.1074 | 0.0681 | 0.0460 | |
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| 0.2447 | 6.81 | 4000 | 0.2349 | 3.5915 | 0.2352 | 0.1336 | 0.2136 | 0.2136 | 0.4573 | 0.0907 | 0.1812 | 0.1100 | 0.0706 | 0.0481 | |
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| 0.2357 | 8.51 | 5000 | 0.2297 | 3.5867 | 0.2364 | 0.1364 | 0.2158 | 0.2158 | 0.4617 | 0.0927 | 0.1820 | 0.1120 | 0.0726 | 0.0499 | |
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| 0.2287 | 10.21 | 6000 | 0.2258 | 3.5781 | 0.2393 | 0.1392 | 0.2183 | 0.2183 | 0.4681 | 0.0947 | 0.1837 | 0.1139 | 0.0745 | 0.0515 | |
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| 0.2228 | 11.91 | 7000 | 0.2223 | 3.5628 | 0.2413 | 0.1419 | 0.2208 | 0.2208 | 0.4734 | 0.0965 | 0.1853 | 0.1158 | 0.0762 | 0.0531 | |
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| 0.2173 | 13.62 | 8000 | 0.2200 | 3.5171 | 0.2459 | 0.1452 | 0.2249 | 0.2249 | 0.4779 | 0.0976 | 0.1860 | 0.1167 | 0.0773 | 0.0540 | |
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| 0.2132 | 15.32 | 9000 | 0.2184 | 3.5207 | 0.2461 | 0.1464 | 0.2253 | 0.2254 | 0.4804 | 0.0994 | 0.1885 | 0.1187 | 0.0789 | 0.0553 | |
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| 0.2085 | 17.02 | 10000 | 0.2174 | 3.5189 | 0.2484 | 0.1468 | 0.2259 | 0.2259 | 0.4842 | 0.0998 | 0.1895 | 0.1190 | 0.0791 | 0.0555 | |
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| 0.2027 | 18.72 | 11000 | 0.2179 | 3.2891 | 0.2656 | 0.1571 | 0.2411 | 0.2411 | 0.4952 | 0.1036 | 0.1970 | 0.1233 | 0.0820 | 0.0577 | |
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| 0.1961 | 20.43 | 12000 | 0.2213 | 3.3457 | 0.2610 | 0.1534 | 0.2367 | 0.2367 | 0.4900 | 0.1025 | 0.1962 | 0.1223 | 0.0810 | 0.0568 | |
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| 0.1886 | 22.13 | 13000 | 0.2260 | 2.9878 | 0.2914 | 0.1696 | 0.2628 | 0.2628 | 0.5028 | 0.1053 | 0.2040 | 0.1257 | 0.0828 | 0.0579 | |
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| 0.1797 | 23.83 | 14000 | 0.2305 | 3.0250 | 0.2874 | 0.1668 | 0.2597 | 0.2597 | 0.4987 | 0.1053 | 0.2051 | 0.1259 | 0.0827 | 0.0575 | |
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| 0.1713 | 25.53 | 15000 | 0.2376 | 2.7048 | 0.3125 | 0.1797 | 0.2822 | 0.2822 | 0.5062 | 0.1078 | 0.2125 | 0.1291 | 0.0843 | 0.0583 | |
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| 0.1646 | 27.23 | 16000 | 0.2438 | 2.7129 | 0.3087 | 0.1761 | 0.2786 | 0.2785 | 0.5021 | 0.1066 | 0.2120 | 0.1281 | 0.0831 | 0.0573 | |
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| 0.159 | 28.94 | 17000 | 0.2473 | 2.7325 | 0.3059 | 0.1738 | 0.2760 | 0.2759 | 0.4991 | 0.1058 | 0.2113 | 0.1272 | 0.0824 | 0.0566 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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