End of training

README.md

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+---
+license: mit
+base_model: naver-clova-ix/donut-base
+tags:
+- generated_from_trainer
+metrics:
+- bleu
+- wer
+model-index:
+- name: donut_experiment_1
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# donut_experiment_1
+
+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: 0.4233
+- Bleu: 0.0659
+- Precisions: [0.8058455114822547, 0.7440758293838863, 0.7013698630136986, 0.6590909090909091]
+- Brevity Penalty: 0.0908
+- Length Ratio: 0.2942
+- Translation Length: 479
+- Reference Length: 1628
+- Cer: 0.7576
+- Wer: 0.8295
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 2
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Bleu   | Precisions                                                                       | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer    | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
+| 0.8942        | 1.0   | 253  | 0.5716          | 0.0571 | [0.7436974789915967, 0.6610978520286396, 0.6104972375690608, 0.5672131147540984] | 0.0889          | 0.2924       | 476                | 1628             | 0.7669 | 0.8416 |
+| 0.3794        | 2.0   | 506  | 0.4522          | 0.0594 | [0.770042194092827, 0.697841726618705, 0.6472222222222223, 0.6072607260726073]   | 0.0876          | 0.2912       | 474                | 1628             | 0.7642 | 0.8415 |
+| 0.3017        | 3.0   | 759  | 0.4154          | 0.0642 | [0.8029350104821803, 0.7357142857142858, 0.6887052341597796, 0.6503267973856209] | 0.0895          | 0.2930       | 477                | 1628             | 0.7577 | 0.8320 |
+| 0.222         | 4.0   | 1012 | 0.4233          | 0.0659 | [0.8058455114822547, 0.7440758293838863, 0.7013698630136986, 0.6590909090909091] | 0.0908          | 0.2942       | 479                | 1628             | 0.7576 | 0.8295 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.1.0
+- Datasets 2.18.0
+- Tokenizers 0.19.1

generation_config.json

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+{
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "pad_token_id": 1,
+  "transformers_version": "4.40.2"
+}

runs/May08_19-09-45_ip-172-16-185-56.ec2.internal/events.out.tfevents.1715195385.ip-172-16-185-56.ec2.internal.13114.0

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