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--- |
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license: mit |
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base_model: arthurmluz/ptt5-xlsumm-30epochs |
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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: ptt5-xlsumm-cstnews-1024 |
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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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# ptt5-xlsumm-cstnews-1024 |
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This model is a fine-tuned version of [arthurmluz/ptt5-xlsumm-30epochs](https://huggingface.co/arthurmluz/ptt5-xlsumm-30epochs) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1450 |
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- Rouge1: 0.2635 |
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- Rouge2: 0.2018 |
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- Rougel: 0.2421 |
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- Rougelsum: 0.2586 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 30 |
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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 | 47 | 1.3343 | 0.2102 | 0.108 | 0.1696 | 0.1927 | 19.0 | |
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| No log | 2.0 | 94 | 1.2369 | 0.2236 | 0.1347 | 0.1817 | 0.2103 | 18.9677 | |
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| No log | 3.0 | 141 | 1.1886 | 0.2301 | 0.1508 | 0.1939 | 0.2167 | 18.871 | |
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| No log | 4.0 | 188 | 1.1635 | 0.2506 | 0.1844 | 0.2273 | 0.2415 | 18.871 | |
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| 1.6578 | 5.0 | 235 | 1.1491 | 0.2521 | 0.1888 | 0.2309 | 0.2462 | 18.871 | |
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| 1.6578 | 6.0 | 282 | 1.1369 | 0.2594 | 0.1985 | 0.2372 | 0.2527 | 18.871 | |
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| 1.6578 | 7.0 | 329 | 1.1308 | 0.2611 | 0.1998 | 0.2399 | 0.2543 | 18.871 | |
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| 1.6578 | 8.0 | 376 | 1.1277 | 0.2579 | 0.1959 | 0.2361 | 0.2521 | 18.871 | |
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| 1.259 | 9.0 | 423 | 1.1209 | 0.261 | 0.1967 | 0.2382 | 0.2544 | 19.0 | |
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| 1.259 | 10.0 | 470 | 1.1200 | 0.2625 | 0.1991 | 0.2403 | 0.2549 | 19.0 | |
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| 1.259 | 11.0 | 517 | 1.1163 | 0.2617 | 0.1995 | 0.2403 | 0.2555 | 19.0 | |
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| 1.259 | 12.0 | 564 | 1.1162 | 0.2662 | 0.2014 | 0.2435 | 0.26 | 19.0 | |
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| 1.1096 | 13.0 | 611 | 1.1183 | 0.2676 | 0.2029 | 0.2466 | 0.2614 | 19.0 | |
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| 1.1096 | 14.0 | 658 | 1.1149 | 0.2677 | 0.2015 | 0.2454 | 0.2611 | 19.0 | |
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| 1.1096 | 15.0 | 705 | 1.1182 | 0.2677 | 0.2015 | 0.2454 | 0.2611 | 19.0 | |
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| 1.1096 | 16.0 | 752 | 1.1211 | 0.2663 | 0.2043 | 0.2467 | 0.2616 | 19.0 | |
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| 1.1096 | 17.0 | 799 | 1.1246 | 0.2654 | 0.2018 | 0.2445 | 0.261 | 19.0 | |
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| 0.9916 | 18.0 | 846 | 1.1246 | 0.2665 | 0.2038 | 0.2455 | 0.2615 | 19.0 | |
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| 0.9916 | 19.0 | 893 | 1.1278 | 0.2661 | 0.2035 | 0.2457 | 0.2622 | 19.0 | |
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| 0.9916 | 20.0 | 940 | 1.1273 | 0.265 | 0.2028 | 0.2439 | 0.2614 | 19.0 | |
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| 0.9916 | 21.0 | 987 | 1.1326 | 0.2661 | 0.2035 | 0.2457 | 0.2622 | 19.0 | |
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| 0.9003 | 22.0 | 1034 | 1.1372 | 0.2656 | 0.2027 | 0.2449 | 0.2615 | 19.0 | |
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| 0.9003 | 23.0 | 1081 | 1.1406 | 0.264 | 0.1994 | 0.2418 | 0.2591 | 19.0 | |
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| 0.9003 | 24.0 | 1128 | 1.1407 | 0.2644 | 0.2015 | 0.2419 | 0.2591 | 19.0 | |
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| 0.9003 | 25.0 | 1175 | 1.1430 | 0.263 | 0.1998 | 0.2415 | 0.2586 | 19.0 | |
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| 0.8442 | 26.0 | 1222 | 1.1426 | 0.2635 | 0.2018 | 0.2421 | 0.2586 | 19.0 | |
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| 0.8442 | 27.0 | 1269 | 1.1439 | 0.2635 | 0.2018 | 0.2421 | 0.2586 | 19.0 | |
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| 0.8442 | 28.0 | 1316 | 1.1451 | 0.2635 | 0.2018 | 0.2421 | 0.2586 | 19.0 | |
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| 0.8442 | 29.0 | 1363 | 1.1448 | 0.2635 | 0.2018 | 0.2421 | 0.2586 | 19.0 | |
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| 0.8285 | 30.0 | 1410 | 1.1450 | 0.2635 | 0.2018 | 0.2421 | 0.2586 | 19.0 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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