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ul2-large-dutch-finetuned-oba-book-search

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Second training rerun on complete dataset

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  1. README.md +40 -21
  2. adapter_model.safetensors +1 -1
  3. runs/Sep27_06-28-56_ml.hihva.nl/events.out.tfevents.1727467156.ml.hihva.nl.1081801.3 +2 -2
README.md CHANGED
@@ -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 [yhavinga/ul2-large-dutch](https://huggingface.co/yhavinga/ul2-large-dutch) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 4.5684
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- - Top-5-accuracy: 0.1158
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  ## Model description
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@@ -48,25 +48,44 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Top-5-accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------------:|
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- | 7.7153 | 0.2577 | 200 | 5.3876 | 0.0 |
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- | 6.8602 | 0.5155 | 400 | 4.8652 | 0.0 |
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- | 6.3689 | 0.7732 | 600 | 4.6435 | 0.0 |
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- | 6.2303 | 1.0309 | 800 | 4.6293 | 0.0579 |
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- | 6.0898 | 1.2887 | 1000 | 4.6395 | 0.0289 |
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- | 6.0367 | 1.5464 | 1200 | 4.5855 | 0.0289 |
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- | 5.8512 | 1.8041 | 1400 | 4.5860 | 0.0579 |
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- | 5.9489 | 2.0619 | 1600 | 4.5672 | 0.0868 |
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- | 5.7601 | 2.3196 | 1800 | 4.5522 | 0.0579 |
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- | 5.7379 | 2.5773 | 2000 | 4.5572 | 0.0868 |
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- | 5.7397 | 2.8351 | 2200 | 4.5559 | 0.0579 |
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- | 5.7488 | 3.0928 | 2400 | 4.5769 | 0.1447 |
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- | 5.7581 | 3.3505 | 2600 | 4.5421 | 0.1158 |
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- | 5.6448 | 3.6082 | 2800 | 4.5174 | 0.1447 |
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- | 5.6551 | 3.8660 | 3000 | 4.5773 | 0.1158 |
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- | 5.6971 | 4.1237 | 3200 | 4.5495 | 0.0868 |
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- | 5.7085 | 4.3814 | 3400 | 4.5392 | 0.1447 |
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- | 5.6689 | 4.6392 | 3600 | 4.5707 | 0.1158 |
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- | 5.5422 | 4.8969 | 3800 | 4.5684 | 0.1158 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [yhavinga/ul2-large-dutch](https://huggingface.co/yhavinga/ul2-large-dutch) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 4.5755
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+ - Top-5-accuracy: 0.0579
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Top-5-accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------------:|
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+ | 7.9158 | 0.1289 | 200 | 5.3305 | 0.0 |
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+ | 7.0161 | 0.2579 | 400 | 4.8351 | 0.0 |
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+ | 6.3673 | 0.3868 | 600 | 4.6915 | 0.0579 |
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+ | 6.1376 | 0.5158 | 800 | 4.7811 | 0.0289 |
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+ | 6.1629 | 0.6447 | 1000 | 4.7614 | 0.0 |
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+ | 5.9541 | 0.7737 | 1200 | 4.6734 | 0.0289 |
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+ | 5.8968 | 0.9026 | 1400 | 4.7609 | 0.0289 |
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+ | 5.9555 | 1.0316 | 1600 | 4.5714 | 0.0289 |
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+ | 5.8876 | 1.1605 | 1800 | 4.7200 | 0.0579 |
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+ | 5.7377 | 1.2895 | 2000 | 4.6012 | 0.0289 |
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+ | 5.7385 | 1.4184 | 2200 | 4.5199 | 0.0289 |
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+ | 5.7584 | 1.5474 | 2400 | 4.5996 | 0.0579 |
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+ | 5.7681 | 1.6763 | 2600 | 4.6556 | 0.0289 |
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+ | 5.7317 | 1.8053 | 2800 | 4.6396 | 0.0289 |
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+ | 5.6363 | 1.9342 | 3000 | 4.5867 | 0.0579 |
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+ | 5.7462 | 2.0632 | 3200 | 4.5472 | 0.0289 |
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+ | 5.6963 | 2.1921 | 3400 | 4.5598 | 0.0289 |
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+ | 5.588 | 2.3211 | 3600 | 4.5316 | 0.0289 |
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+ | 5.5463 | 2.4500 | 3800 | 4.5661 | 0.0289 |
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+ | 5.5491 | 2.5790 | 4000 | 4.5478 | 0.0289 |
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+ | 5.5445 | 2.7079 | 4200 | 4.5253 | 0.0289 |
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+ | 5.5136 | 2.8369 | 4400 | 4.5313 | 0.0289 |
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+ | 5.5705 | 2.9658 | 4600 | 4.5677 | 0.0289 |
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+ | 5.4956 | 3.0948 | 4800 | 4.5268 | 0.0289 |
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+ | 5.4799 | 3.2237 | 5000 | 4.5313 | 0.0289 |
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+ | 5.4992 | 3.3527 | 5200 | 4.5403 | 0.0289 |
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+ | 5.5742 | 3.4816 | 5400 | 4.5124 | 0.0289 |
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+ | 5.4864 | 3.6106 | 5600 | 4.5527 | 0.0579 |
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+ | 5.4896 | 3.7395 | 5800 | 4.5582 | 0.0289 |
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+ | 5.5396 | 3.8685 | 6000 | 4.5680 | 0.0579 |
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+ | 5.4413 | 3.9974 | 6200 | 4.5579 | 0.0579 |
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+ | 5.4534 | 4.1264 | 6400 | 4.5684 | 0.0579 |
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+ | 5.5199 | 4.2553 | 6600 | 4.5726 | 0.0579 |
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+ | 5.5298 | 4.3843 | 6800 | 4.5883 | 0.0579 |
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+ | 5.4346 | 4.5132 | 7000 | 4.5885 | 0.0289 |
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+ | 5.5098 | 4.6422 | 7200 | 4.5895 | 0.0579 |
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+ | 5.489 | 4.7711 | 7400 | 4.5682 | 0.0579 |
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+ | 5.4055 | 4.9001 | 7600 | 4.5755 | 0.0579 |
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  ### Framework versions
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