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tlam25
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bart_finetuned_clarify_aspects

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Safetensors
bart
text2text-generation
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End of training

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  1. README.md +30 -30
  2. model.safetensors +1 -1
README.md CHANGED
@@ -19,16 +19,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0559
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- - Micro Precision: 0.2830
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- - Micro Recall: 0.0156
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- - Micro F1: 0.0296
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- - Macro Precision: 0.2476
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- - Macro Recall: 0.0138
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- - Macro F1: 0.0261
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- - Bleu: 0.8552
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- - Rouge1: 0.8175
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- - Rouge2: 0.5280
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  ## Model description
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@@ -60,26 +60,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 | Bleu | Rouge1 | Rouge2 |
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  |:-------------:|:------:|:----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------:|:------:|:------:|
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- | 4.8047 | 0.2404 | 50 | 2.1143 | 0.1819 | 0.1342 | 0.1545 | 0.0874 | 0.1550 | 0.1118 | 0.6819 | 0.7405 | 0.4216 |
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- | 1.7948 | 0.4808 | 100 | 0.8959 | 0.1847 | 0.1384 | 0.1582 | 0.0897 | 0.1631 | 0.1157 | 0.6860 | 0.7494 | 0.4382 |
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- | 0.7302 | 0.7212 | 150 | 0.2402 | 0.2198 | 0.0926 | 0.1303 | 0.1110 | 0.0917 | 0.1004 | 0.7096 | 0.7106 | 0.4485 |
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- | 0.22 | 0.9615 | 200 | 0.0950 | 0.2607 | 0.0822 | 0.125 | 0.1220 | 0.0916 | 0.1047 | 0.7933 | 0.7722 | 0.4549 |
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- | 0.1001 | 1.2019 | 250 | 0.0701 | 0.2150 | 0.1103 | 0.1458 | 0.3188 | 0.1074 | 0.1606 | 0.7383 | 0.7525 | 0.4464 |
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- | 0.0813 | 1.4423 | 300 | 0.0741 | 0.1605 | 0.0583 | 0.0855 | 0.0970 | 0.0513 | 0.0671 | 0.8036 | 0.7915 | 0.4605 |
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- | 0.0748 | 1.6827 | 350 | 0.0678 | 0.3088 | 0.0656 | 0.1082 | 0.1544 | 0.0539 | 0.0799 | 0.8468 | 0.8137 | 0.4406 |
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- | 0.0799 | 1.9231 | 400 | 0.0677 | 0.2314 | 0.1103 | 0.1494 | 0.2083 | 0.1133 | 0.1468 | 0.7620 | 0.7821 | 0.4864 |
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- | 0.0714 | 2.1635 | 450 | 0.0678 | 0.2184 | 0.0198 | 0.0363 | 0.1602 | 0.0174 | 0.0313 | 0.8540 | 0.7976 | 0.4690 |
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- | 0.0705 | 2.4038 | 500 | 0.0631 | 0.2251 | 0.0447 | 0.0747 | 0.2159 | 0.0404 | 0.0681 | 0.8610 | 0.8234 | 0.5213 |
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- | 0.0677 | 2.6442 | 550 | 0.0641 | 0.176 | 0.0229 | 0.0405 | 0.1932 | 0.0210 | 0.0378 | 0.8560 | 0.8067 | 0.5004 |
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- | 0.0664 | 2.8846 | 600 | 0.0602 | 0.5714 | 0.0083 | 0.0164 | 0.5071 | 0.0074 | 0.0145 | 0.8537 | 0.8111 | 0.5045 |
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- | 0.0656 | 3.125 | 650 | 0.0614 | 0.2527 | 0.0239 | 0.0437 | 0.2838 | 0.0241 | 0.0445 | 0.8586 | 0.8297 | 0.5281 |
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- | 0.0632 | 3.3654 | 700 | 0.0608 | 0.2414 | 0.0073 | 0.0141 | 0.2010 | 0.0064 | 0.0124 | 0.8518 | 0.8081 | 0.5000 |
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- | 0.0627 | 3.6058 | 750 | 0.0567 | 0.3333 | 0.0198 | 0.0373 | 0.2735 | 0.0169 | 0.0318 | 0.8443 | 0.8096 | 0.5126 |
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- | 0.0594 | 3.8462 | 800 | 0.0571 | 0.1765 | 0.0031 | 0.0061 | 0.125 | 0.0026 | 0.0051 | 0.8510 | 0.8219 | 0.5244 |
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- | 0.0603 | 4.0865 | 850 | 0.0560 | 0.3103 | 0.0187 | 0.0353 | 0.2715 | 0.0166 | 0.0313 | 0.8526 | 0.8184 | 0.5321 |
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- | 0.0588 | 4.3269 | 900 | 0.0565 | 0.375 | 0.0125 | 0.0242 | 0.3155 | 0.0109 | 0.0211 | 0.8569 | 0.8194 | 0.5249 |
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- | 0.06 | 4.5673 | 950 | 0.0564 | 0.3077 | 0.0166 | 0.0316 | 0.2793 | 0.0156 | 0.0295 | 0.8565 | 0.8186 | 0.5323 |
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- | 0.0612 | 4.8077 | 1000 | 0.0559 | 0.2830 | 0.0156 | 0.0296 | 0.2476 | 0.0138 | 0.0261 | 0.8552 | 0.8175 | 0.5280 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0560
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+ - Micro Precision: 0.2456
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+ - Micro Recall: 0.0146
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+ - Micro F1: 0.0275
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+ - Macro Precision: 0.2301
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+ - Macro Recall: 0.0132
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+ - Macro F1: 0.0251
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+ - Bleu: 0.8555
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+ - Rouge1: 0.8184
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+ - Rouge2: 0.5282
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 | Bleu | Rouge1 | Rouge2 |
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  |:-------------:|:------:|:----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------:|:------:|:------:|
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+ | 4.8047 | 0.2404 | 50 | 2.1144 | 0.1810 | 0.1332 | 0.1535 | 0.0870 | 0.1541 | 0.1112 | 0.6815 | 0.7396 | 0.4212 |
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+ | 1.7948 | 0.4808 | 100 | 0.8959 | 0.1842 | 0.1384 | 0.1581 | 0.0894 | 0.1631 | 0.1155 | 0.6860 | 0.7497 | 0.4381 |
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+ | 0.7301 | 0.7212 | 150 | 0.2402 | 0.2187 | 0.0926 | 0.1301 | 0.1095 | 0.0917 | 0.0998 | 0.7092 | 0.7108 | 0.4486 |
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+ | 0.22 | 0.9615 | 200 | 0.0950 | 0.2541 | 0.0812 | 0.1230 | 0.3683 | 0.0905 | 0.1453 | 0.7932 | 0.7730 | 0.4542 |
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+ | 0.1005 | 1.2019 | 250 | 0.0699 | 0.2168 | 0.1290 | 0.1618 | 0.3337 | 0.1228 | 0.1796 | 0.7490 | 0.7693 | 0.4552 |
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+ | 0.0814 | 1.4423 | 300 | 0.0750 | 0.1692 | 0.0468 | 0.0733 | 0.1176 | 0.0407 | 0.0605 | 0.8194 | 0.7906 | 0.4550 |
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+ | 0.075 | 1.6827 | 350 | 0.0681 | 0.2938 | 0.0593 | 0.0987 | 0.1469 | 0.0486 | 0.0731 | 0.8457 | 0.8197 | 0.4467 |
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+ | 0.08 | 1.9231 | 400 | 0.0682 | 0.2427 | 0.1041 | 0.1457 | 0.2116 | 0.1018 | 0.1375 | 0.7777 | 0.7904 | 0.4945 |
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+ | 0.0712 | 2.1635 | 450 | 0.0682 | 0.3137 | 0.0166 | 0.0316 | 0.2348 | 0.0147 | 0.0277 | 0.8509 | 0.7969 | 0.4711 |
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+ | 0.0706 | 2.4038 | 500 | 0.0632 | 0.3026 | 0.0239 | 0.0444 | 0.2769 | 0.0211 | 0.0392 | 0.8550 | 0.8220 | 0.5130 |
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+ | 0.0677 | 2.6442 | 550 | 0.0642 | 0.1622 | 0.0062 | 0.0120 | 0.1548 | 0.0055 | 0.0105 | 0.8520 | 0.8070 | 0.4974 |
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+ | 0.0664 | 2.8846 | 600 | 0.0604 | 0.3846 | 0.0052 | 0.0103 | 0.4167 | 0.0050 | 0.0098 | 0.8548 | 0.8162 | 0.5217 |
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+ | 0.0657 | 3.125 | 650 | 0.0613 | 0.2763 | 0.0219 | 0.0405 | 0.2929 | 0.0218 | 0.0405 | 0.8583 | 0.8304 | 0.5329 |
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+ | 0.0634 | 3.3654 | 700 | 0.0608 | 0.1786 | 0.0052 | 0.0101 | 0.1548 | 0.0047 | 0.0092 | 0.8513 | 0.8091 | 0.5055 |
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+ | 0.0627 | 3.6058 | 750 | 0.0568 | 0.3265 | 0.0166 | 0.0317 | 0.3129 | 0.0147 | 0.0280 | 0.8463 | 0.8097 | 0.5113 |
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+ | 0.0595 | 3.8462 | 800 | 0.0572 | 0.1 | 0.0010 | 0.0021 | 0.05 | 0.0007 | 0.0014 | 0.8508 | 0.8198 | 0.5226 |
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+ | 0.0603 | 4.0865 | 850 | 0.0562 | 0.2381 | 0.0156 | 0.0293 | 0.2455 | 0.0153 | 0.0288 | 0.8528 | 0.8172 | 0.5253 |
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+ | 0.0588 | 4.3269 | 900 | 0.0565 | 0.2955 | 0.0135 | 0.0259 | 0.3193 | 0.0132 | 0.0253 | 0.8559 | 0.8178 | 0.5243 |
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+ | 0.06 | 4.5673 | 950 | 0.0565 | 0.2931 | 0.0177 | 0.0334 | 0.3056 | 0.0167 | 0.0317 | 0.8559 | 0.8159 | 0.5255 |
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+ | 0.0614 | 4.8077 | 1000 | 0.0560 | 0.2456 | 0.0146 | 0.0275 | 0.2301 | 0.0132 | 0.0251 | 0.8555 | 0.8184 | 0.5282 |
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  ### Framework versions
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