End of training

README.md

@@ -0,0 +1,75 @@
+---
+license: mit
+base_model: naver-clova-ix/donut-base
+tags:
+- generated_from_trainer
+metrics:
+- bleu
+- wer
+model-index:
+- name: donut_experiment_bayesian_trial_7
+  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_bayesian_trial_7
+
+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: 0.3786
+- Bleu: 0.0669
+- Precisions: [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332]
+- Brevity Penalty: 0.0870
+- Length Ratio: 0.2905
+- Translation Length: 473
+- Reference Length: 1628
+- Cer: 0.7532
+- Wer: 0.8192
+
+## 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: 3.540464175534869e-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: 5
+- 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    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
+| 0.5323        | 1.0   | 253  | 0.4204          | 0.0580 | [0.7710084033613446, 0.6778042959427207, 0.6132596685082873, 0.5639344262295082] | 0.0889          | 0.2924       | 476                | 1628             | 0.7617 | 0.8431 |
+| 0.2487        | 2.0   | 506  | 0.3788          | 0.0609 | [0.8123667377398721, 0.7402912621359223, 0.6929577464788732, 0.6476510067114094] | 0.0845          | 0.2881       | 469                | 1628             | 0.7561 | 0.8279 |
+| 0.1746        | 3.0   | 759  | 0.3551          | 0.0652 | [0.836864406779661, 0.7759036144578313, 0.729050279329609, 0.6843853820598007]   | 0.0864          | 0.2899       | 472                | 1628             | 0.7541 | 0.8213 |
+| 0.1191        | 4.0   | 1012 | 0.3690          | 0.0680 | [0.8547368421052631, 0.784688995215311, 0.7451523545706371, 0.7039473684210527]  | 0.0883          | 0.2918       | 475                | 1628             | 0.7514 | 0.8192 |
+| 0.1072        | 5.0   | 1265 | 0.3786          | 0.0669 | [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332] | 0.0870          | 0.2905       | 473                | 1628             | 0.7532 | 0.8192 |
+
+
+### Framework versions
+
+- Transformers 4.40.0
+- Pytorch 2.1.0
+- Datasets 2.18.0
+- Tokenizers 0.19.1

added_tokens.json

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+  "</s_company>": 57530,
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+}

generation_config.json

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+{
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+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "pad_token_id": 1,
+  "transformers_version": "4.40.0"
+}

metrics.jsonl

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 {"eval_loss": 0.3787934184074402, "eval_bleu": 0.060892730383914066, "eval_precisions": [0.8123667377398721, 0.7402912621359223, 0.6929577464788732, 0.6476510067114094], "eval_brevity_penalty": 0.0844821210838816, "eval_length_ratio": 0.2880835380835381, "eval_translation_length": 469, "eval_reference_length": 1628, "eval_cer": 0.7560976138404875, "eval_wer": 0.8279176428607711, "eval_runtime": 74.3094, "eval_samples_per_second": 0.767, "eval_steps_per_second": 0.767, "epoch": 2.0}
 {"eval_loss": 0.355084091424942, "eval_bleu": 0.06515936373352159, "eval_precisions": [0.836864406779661, 0.7759036144578313, 0.729050279329609, 0.6843853820598007], "eval_brevity_penalty": 0.0863667476535313, "eval_length_ratio": 0.28992628992628994, "eval_translation_length": 472, "eval_reference_length": 1628, "eval_cer": 0.7540589021599197, "eval_wer": 0.8213041650395974, "eval_runtime": 74.1866, "eval_samples_per_second": 0.768, "eval_steps_per_second": 0.768, "epoch": 3.0}
 {"eval_loss": 0.36895015835762024, "eval_bleu": 0.06798077363306983, "eval_precisions": [0.8547368421052631, 0.784688995215311, 0.7451523545706371, 0.7039473684210527], "eval_brevity_penalty": 0.08826881356184386, "eval_length_ratio": 0.2917690417690418, "eval_translation_length": 475, "eval_reference_length": 1628, "eval_cer": 0.7513930040104869, "eval_wer": 0.8192443845253602, "eval_runtime": 73.963, "eval_samples_per_second": 0.771, "eval_steps_per_second": 0.771, "epoch": 4.0}
+{"eval_loss": 0.3786037862300873, "eval_bleu": 0.06690654459858245, "eval_precisions": [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332], "eval_brevity_penalty": 0.08699884249108106, "eval_length_ratio": 0.2905405405405405, "eval_translation_length": 473, "eval_reference_length": 1628, "eval_cer": 0.753238674494809, "eval_wer": 0.8192097128659745, "eval_runtime": 74.3868, "eval_samples_per_second": 0.766, "eval_steps_per_second": 0.766, "epoch": 5.0}

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preprocessor_config.json

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special_tokens_map.json

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tokenizer_config.json

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