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alex2awesome
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newsdiscourse-model

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PyTorch
roberta
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update model card README.md

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@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7252
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- - F1: 0.5583
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  ## Model description
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@@ -48,41 +48,41 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | No log | 0.14 | 100 | 1.3049 | 0.3592 |
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- | No log | 0.28 | 200 | 1.2728 | 0.4052 |
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- | No log | 0.43 | 300 | 1.1451 | 0.4152 |
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- | No log | 0.57 | 400 | 1.3513 | 0.5019 |
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- | 1.2057 | 0.71 | 500 | 1.2897 | 0.4742 |
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- | 1.2057 | 0.85 | 600 | 1.2340 | 0.4944 |
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- | 1.2057 | 1.0 | 700 | 1.2076 | 0.4783 |
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- | 1.2057 | 1.14 | 800 | 1.2074 | 0.4953 |
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- | 1.2057 | 1.28 | 900 | 1.1214 | 0.4909 |
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- | 0.9162 | 1.42 | 1000 | 1.2604 | 0.5207 |
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- | 0.9162 | 1.57 | 1100 | 1.2455 | 0.4893 |
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- | 0.9162 | 1.71 | 1200 | 1.0983 | 0.4994 |
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- | 0.9162 | 1.85 | 1300 | 1.1237 | 0.5027 |
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- | 0.9162 | 1.99 | 1400 | 1.1781 | 0.5253 |
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- | 0.8166 | 2.14 | 1500 | 1.2813 | 0.5183 |
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- | 0.8166 | 2.28 | 1600 | 1.3799 | 0.5398 |
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- | 0.8166 | 2.42 | 1700 | 1.3371 | 0.5228 |
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- | 0.8166 | 2.56 | 1800 | 1.2438 | 0.5227 |
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- | 0.8166 | 2.71 | 1900 | 1.3400 | 0.5314 |
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- | 0.6229 | 2.85 | 2000 | 1.3777 | 0.5415 |
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- | 0.6229 | 2.99 | 2100 | 1.3483 | 0.5526 |
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- | 0.6229 | 3.13 | 2200 | 1.6263 | 0.5232 |
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- | 0.6229 | 3.28 | 2300 | 1.5368 | 0.5557 |
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- | 0.6229 | 3.42 | 2400 | 1.5507 | 0.5658 |
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- | 0.4661 | 3.56 | 2500 | 1.5510 | 0.5247 |
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- | 0.4661 | 3.7 | 2600 | 1.6305 | 0.5355 |
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- | 0.4661 | 3.85 | 2700 | 1.5574 | 0.5427 |
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- | 0.4661 | 3.99 | 2800 | 1.4871 | 0.5414 |
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- | 0.4661 | 4.13 | 2900 | 1.6329 | 0.5543 |
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- | 0.3667 | 4.27 | 3000 | 1.6794 | 0.5502 |
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- | 0.3667 | 4.42 | 3100 | 1.6820 | 0.5418 |
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- | 0.3667 | 4.56 | 3200 | 1.7638 | 0.5529 |
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- | 0.3667 | 4.7 | 3300 | 1.7321 | 0.5513 |
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- | 0.3667 | 4.84 | 3400 | 1.7443 | 0.5548 |
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- | 0.2999 | 4.99 | 3500 | 1.7252 | 0.5583 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.7194
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+ - F1: 0.5515
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | No log | 0.14 | 100 | 1.3632 | 0.3762 |
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+ | No log | 0.28 | 200 | 1.2278 | 0.4162 |
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+ | No log | 0.43 | 300 | 1.1802 | 0.4159 |
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+ | No log | 0.57 | 400 | 1.3237 | 0.4879 |
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+ | 1.2 | 0.71 | 500 | 1.2971 | 0.4645 |
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+ | 1.2 | 0.85 | 600 | 1.2550 | 0.5020 |
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+ | 1.2 | 1.0 | 700 | 1.1854 | 0.4806 |
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+ | 1.2 | 1.14 | 800 | 1.1788 | 0.5012 |
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+ | 1.2 | 1.28 | 900 | 1.0935 | 0.4964 |
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+ | 0.9189 | 1.42 | 1000 | 1.2862 | 0.4986 |
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+ | 0.9189 | 1.57 | 1100 | 1.2223 | 0.4930 |
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+ | 0.9189 | 1.71 | 1200 | 1.1197 | 0.4954 |
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+ | 0.9189 | 1.85 | 1300 | 1.1257 | 0.5153 |
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+ | 0.9189 | 1.99 | 1400 | 1.1729 | 0.5264 |
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+ | 0.8143 | 2.14 | 1500 | 1.2722 | 0.5165 |
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+ | 0.8143 | 2.28 | 1600 | 1.3218 | 0.5395 |
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+ | 0.8143 | 2.42 | 1700 | 1.3383 | 0.5170 |
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+ | 0.8143 | 2.56 | 1800 | 1.2503 | 0.5139 |
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+ | 0.8143 | 2.71 | 1900 | 1.3630 | 0.5240 |
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+ | 0.6175 | 2.85 | 2000 | 1.4028 | 0.5305 |
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+ | 0.6175 | 2.99 | 2100 | 1.4017 | 0.5408 |
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+ | 0.6175 | 3.13 | 2200 | 1.5930 | 0.5413 |
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+ | 0.6175 | 3.28 | 2300 | 1.5373 | 0.5565 |
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+ | 0.6175 | 3.42 | 2400 | 1.5013 | 0.5722 |
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+ | 0.4726 | 3.56 | 2500 | 1.5704 | 0.5226 |
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+ | 0.4726 | 3.7 | 2600 | 1.5891 | 0.5484 |
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+ | 0.4726 | 3.85 | 2700 | 1.5236 | 0.5630 |
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+ | 0.4726 | 3.99 | 2800 | 1.5233 | 0.5422 |
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+ | 0.4726 | 4.13 | 2900 | 1.6105 | 0.5470 |
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+ | 0.3745 | 4.27 | 3000 | 1.7136 | 0.5525 |
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+ | 0.3745 | 4.42 | 3100 | 1.6561 | 0.5539 |
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+ | 0.3745 | 4.56 | 3200 | 1.7664 | 0.5504 |
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+ | 0.3745 | 4.7 | 3300 | 1.7505 | 0.5494 |
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+ | 0.3745 | 4.84 | 3400 | 1.7313 | 0.5516 |
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+ | 0.307 | 4.99 | 3500 | 1.7194 | 0.5515 |
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  ### Framework versions