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--- |
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license: apache-2.0 |
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base_model: Falconsai/text_summarization |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: text_summarization-finetuned-stocknews |
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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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# text_summarization-finetuned-stocknews |
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This model is a fine-tuned version of [Falconsai/text_summarization](https://huggingface.co/Falconsai/text_summarization) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5087 |
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- Rouge1: 28.1323 |
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- Rouge2: 14.1505 |
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- Rougel: 23.7163 |
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- Rougelsum: 24.743 |
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- Gen Len: 19.0 |
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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: 2e-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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- 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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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| No log | 1.0 | 25 | 1.8901 | 26.1517 | 11.6615 | 21.4583 | 22.9556 | 19.0 | |
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| No log | 2.0 | 50 | 1.7909 | 25.9481 | 11.4621 | 21.1748 | 22.8127 | 19.0 | |
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| No log | 3.0 | 75 | 1.7388 | 26.412 | 12.1797 | 21.744 | 23.3289 | 19.0 | |
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| No log | 4.0 | 100 | 1.6988 | 26.4465 | 12.2417 | 21.7109 | 23.2402 | 19.0 | |
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| No log | 5.0 | 125 | 1.6752 | 26.6441 | 12.4313 | 21.7396 | 23.2725 | 19.0 | |
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| No log | 6.0 | 150 | 1.6531 | 26.4585 | 12.2979 | 21.7528 | 23.1338 | 19.0 | |
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| No log | 7.0 | 175 | 1.6386 | 26.6186 | 12.4271 | 21.8074 | 23.2756 | 19.0 | |
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| No log | 8.0 | 200 | 1.6263 | 26.4223 | 12.3512 | 21.7575 | 23.3278 | 19.0 | |
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| No log | 9.0 | 225 | 1.6124 | 26.5846 | 12.49 | 21.9218 | 23.433 | 19.0 | |
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| No log | 10.0 | 250 | 1.6035 | 26.8364 | 12.6954 | 22.2409 | 23.6239 | 19.0 | |
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| No log | 11.0 | 275 | 1.5926 | 27.0986 | 12.7881 | 22.2246 | 23.6203 | 19.0 | |
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| No log | 12.0 | 300 | 1.5844 | 27.4875 | 13.1342 | 22.717 | 24.0836 | 19.0 | |
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| No log | 13.0 | 325 | 1.5757 | 27.6863 | 13.2919 | 22.8203 | 24.1659 | 19.0 | |
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| No log | 14.0 | 350 | 1.5688 | 27.69 | 13.295 | 22.8364 | 24.2587 | 19.0 | |
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| No log | 15.0 | 375 | 1.5643 | 27.7651 | 13.5588 | 23.01 | 24.5047 | 19.0 | |
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| No log | 16.0 | 400 | 1.5586 | 27.8662 | 13.8812 | 23.1299 | 24.5692 | 19.0 | |
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| No log | 17.0 | 425 | 1.5525 | 27.5329 | 13.5729 | 22.8646 | 24.2491 | 19.0 | |
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| No log | 18.0 | 450 | 1.5466 | 27.2864 | 13.6465 | 22.754 | 24.0451 | 19.0 | |
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| No log | 19.0 | 475 | 1.5434 | 27.3062 | 13.664 | 22.7509 | 24.015 | 19.0 | |
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| 1.7497 | 20.0 | 500 | 1.5401 | 27.3177 | 13.8162 | 22.8012 | 24.0359 | 19.0 | |
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| 1.7497 | 21.0 | 525 | 1.5369 | 27.4956 | 13.9869 | 23.0248 | 24.2922 | 19.0 | |
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| 1.7497 | 22.0 | 550 | 1.5345 | 27.4794 | 13.7914 | 23.0306 | 24.2942 | 19.0 | |
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| 1.7497 | 23.0 | 575 | 1.5324 | 27.4794 | 13.7914 | 23.0306 | 24.2942 | 19.0 | |
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| 1.7497 | 24.0 | 600 | 1.5302 | 27.529 | 13.8756 | 23.1045 | 24.3861 | 19.0 | |
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| 1.7497 | 25.0 | 625 | 1.5266 | 27.8738 | 14.0877 | 23.4826 | 24.7471 | 19.0 | |
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| 1.7497 | 26.0 | 650 | 1.5252 | 27.9294 | 13.9793 | 23.4775 | 24.669 | 19.0 | |
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| 1.7497 | 27.0 | 675 | 1.5247 | 28.0046 | 14.0835 | 23.4865 | 24.7035 | 19.0 | |
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| 1.7497 | 28.0 | 700 | 1.5239 | 28.0085 | 14.1428 | 23.6155 | 24.8178 | 19.0 | |
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| 1.7497 | 29.0 | 725 | 1.5224 | 27.9738 | 14.1251 | 23.6146 | 24.7919 | 19.0 | |
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| 1.7497 | 30.0 | 750 | 1.5200 | 28.007 | 14.1042 | 23.653 | 24.7639 | 19.0 | |
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| 1.7497 | 31.0 | 775 | 1.5192 | 27.9376 | 14.0443 | 23.5673 | 24.6209 | 19.0 | |
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| 1.7497 | 32.0 | 800 | 1.5177 | 28.0251 | 14.0888 | 23.6316 | 24.6779 | 19.0 | |
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| 1.7497 | 33.0 | 825 | 1.5165 | 28.0519 | 14.0867 | 23.6242 | 24.6728 | 19.0 | |
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| 1.7497 | 34.0 | 850 | 1.5164 | 28.1185 | 14.1615 | 23.6657 | 24.7177 | 19.0 | |
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| 1.7497 | 35.0 | 875 | 1.5146 | 28.0809 | 14.1228 | 23.6657 | 24.7177 | 19.0 | |
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| 1.7497 | 36.0 | 900 | 1.5134 | 28.1107 | 14.1889 | 23.6946 | 24.7532 | 19.0 | |
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| 1.7497 | 37.0 | 925 | 1.5130 | 28.0476 | 14.0937 | 23.6232 | 24.6671 | 19.0 | |
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| 1.7497 | 38.0 | 950 | 1.5123 | 27.9979 | 14.0209 | 23.5935 | 24.6298 | 19.0 | |
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| 1.7497 | 39.0 | 975 | 1.5114 | 28.001 | 14.1042 | 23.6265 | 24.6735 | 19.0 | |
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| 1.5033 | 40.0 | 1000 | 1.5100 | 28.004 | 14.1355 | 23.6552 | 24.6776 | 19.0 | |
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| 1.5033 | 41.0 | 1025 | 1.5100 | 28.0346 | 14.1432 | 23.6432 | 24.7052 | 19.0 | |
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| 1.5033 | 42.0 | 1050 | 1.5098 | 28.052 | 14.1387 | 23.6401 | 24.6953 | 19.0 | |
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| 1.5033 | 43.0 | 1075 | 1.5098 | 28.1032 | 14.1743 | 23.6401 | 24.6953 | 19.0 | |
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| 1.5033 | 44.0 | 1100 | 1.5096 | 28.129 | 14.1847 | 23.7406 | 24.805 | 19.0 | |
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| 1.5033 | 45.0 | 1125 | 1.5093 | 28.1763 | 14.2264 | 23.7075 | 24.783 | 19.0 | |
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| 1.5033 | 46.0 | 1150 | 1.5090 | 28.1336 | 14.1871 | 23.7075 | 24.783 | 19.0 | |
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| 1.5033 | 47.0 | 1175 | 1.5089 | 28.1336 | 14.1871 | 23.7075 | 24.783 | 19.0 | |
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| 1.5033 | 48.0 | 1200 | 1.5088 | 28.1336 | 14.1871 | 23.7075 | 24.783 | 19.0 | |
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| 1.5033 | 49.0 | 1225 | 1.5087 | 28.129 | 14.1847 | 23.7406 | 24.805 | 19.0 | |
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| 1.5033 | 50.0 | 1250 | 1.5087 | 28.1323 | 14.1505 | 23.7163 | 24.743 | 19.0 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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