End of training

README.md

@@ -18,15 +18,15 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4919
-- Bleu: 0.0652
-- Precisions: [0.7938775510204081, 0.7020785219399538, 0.6462765957446809, 0.6050156739811913]
-- Brevity Penalty: 0.0955
-- Length Ratio: 0.2986
-- Translation Length: 490
+- Loss: 0.4671
+- Bleu: 0.0662
+- Precisions: [0.785140562248996, 0.6825396825396826, 0.6197916666666666, 0.5626911314984709]
+- Brevity Penalty: 0.1007
+- Length Ratio: 0.3035
+- Translation Length: 498
 - Reference Length: 1641
-- Cer: 0.7510
-- Wer: 0.8349
+- Cer: 0.7528
+- Wer: 0.8385
 
 ## Model description
 
@@ -58,12 +58,12 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Bleu   | Precisions                                                                       | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer    | Wer    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
-| 4.0081        | 1.0   | 253  | 1.6452          | 0.0    | [0.4795539033457249, 0.14622641509433962, 0.03205128205128205, 0.0]              | 0.0061          | 0.1639       | 269                | 1641             | 0.8712 | 0.9425 |
-| 1.2573        | 2.0   | 506  | 0.6901          | 0.0379 | [0.6787234042553192, 0.5036319612590799, 0.4044943820224719, 0.3177257525083612] | 0.0828          | 0.2864       | 470                | 1641             | 0.7727 | 0.8821 |
-| 0.9461        | 3.0   | 759  | 0.5654          | 0.0600 | [0.7469635627530364, 0.631578947368421, 0.5684210526315789, 0.5232198142414861]  | 0.0981          | 0.3010       | 494                | 1641             | 0.7547 | 0.8479 |
-| 0.669         | 4.0   | 1012 | 0.4919          | 0.0652 | [0.7938775510204081, 0.7020785219399538, 0.6462765957446809, 0.6050156739811913] | 0.0955          | 0.2986       | 490                | 1641             | 0.7510 | 0.8349 |
+| Training Loss | Epoch | Step | Validation Loss | Bleu   | Precisions                                                                         | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer    | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
+| 3.6559        | 1.0   | 253  | 1.5613          | 0.0007 | [0.5056179775280899, 0.1943127962085308, 0.07692307692307693, 0.02830188679245283] | 0.0058          | 0.1627       | 267                | 1641             | 0.8768 | 0.9436 |
+| 1.2493        | 2.0   | 506  | 0.6697          | 0.0409 | [0.6560509554140127, 0.5048309178743962, 0.4481792717086835, 0.39]                 | 0.0834          | 0.2870       | 471                | 1641             | 0.7766 | 0.8837 |
+| 0.9257        | 3.0   | 759  | 0.5168          | 0.0594 | [0.75, 0.6275862068965518, 0.5714285714285714, 0.5264797507788161]                 | 0.0968          | 0.2998       | 492                | 1641             | 0.7570 | 0.8499 |
+| 0.6416        | 4.0   | 1012 | 0.4671          | 0.0662 | [0.785140562248996, 0.6825396825396826, 0.6197916666666666, 0.5626911314984709]    | 0.1007          | 0.3035       | 498                | 1641             | 0.7528 | 0.8385 |
 
 
 ### Framework versions

model.safetensors

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