End of training

README.md

@@ -0,0 +1,74 @@
+---
+license: mit
+base_model: naver-clova-ix/donut-base
+tags:
+- generated_from_trainer
+metrics:
+- bleu
+- wer
+model-index:
+- name: donut_experiment_bayesian_trial_3
+  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_3
+
+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: 0.5840
+- Bleu: 0.0667
+- Precisions: [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846]
+- Brevity Penalty: 0.0934
+- Length Ratio: 0.2967
+- Translation Length: 483
+- Reference Length: 1628
+- Cer: 0.7599
+- Wer: 0.8328
+
+## 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: 0.00017060423589132634
+- 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    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
+| 0.2012        | 1.0   | 253  | 0.6694          | 0.0547 | [0.7680851063829788, 0.6682808716707022, 0.6067415730337079, 0.5484949832775919] | 0.0851          | 0.2887       | 470                | 1628             | 0.7597 | 0.8411 |
+| 0.127         | 2.0   | 506  | 0.6071          | 0.0638 | [0.7818930041152263, 0.6876456876456877, 0.6370967741935484, 0.5841269841269842] | 0.0954          | 0.2985       | 486                | 1628             | 0.7570 | 0.8360 |
+| 0.0766        | 3.0   | 759  | 0.5786          | 0.0655 | [0.8125, 0.735224586288416, 0.6885245901639344, 0.6407766990291263]              | 0.0915          | 0.2948       | 480                | 1628             | 0.7564 | 0.8319 |
+| 0.0259        | 4.0   | 1012 | 0.5840          | 0.0667 | [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846] | 0.0934          | 0.2967       | 483                | 1628             | 0.7599 | 0.8328 |
+
+
+### 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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+{
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+  "</s_company>": 57530,
+  "</s_date>": 57528,
+  "</s_total>": 57526,
+  "<s_address>": 57531,
+  "<s_company>": 57529,
+  "<s_date>": 57527,
+  "<s_iitcdip>": 57523,
+  "<s_synthdog>": 57524,
+  "<s_total>": 57525,
+  "<sep/>": 57522
+}

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": "4.40.0"
+}

metrics.jsonl

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 {"eval_loss": 0.6694272756576538, "eval_bleu": 0.0547152661229347, "eval_precisions": [0.7680851063829788, 0.6682808716707022, 0.6067415730337079, 0.5484949832775919], "eval_brevity_penalty": 0.08510837903243891, "eval_length_ratio": 0.28869778869778867, "eval_translation_length": 470, "eval_reference_length": 1628, "eval_cer": 0.7597014088219545, "eval_wer": 0.841103378561845, "eval_runtime": 71.7004, "eval_samples_per_second": 0.795, "eval_steps_per_second": 0.795, "epoch": 1.0}
 {"eval_loss": 0.6070794463157654, "eval_bleu": 0.06379747790299091, "eval_precisions": [0.7818930041152263, 0.6876456876456877, 0.6370967741935484, 0.5841269841269842], "eval_brevity_penalty": 0.095388787518951, "eval_length_ratio": 0.2985257985257985, "eval_translation_length": 486, "eval_reference_length": 1628, "eval_cer": 0.7569562941961688, "eval_wer": 0.8360294120694332, "eval_runtime": 71.3535, "eval_samples_per_second": 0.799, "eval_steps_per_second": 0.799, "epoch": 2.0}
 {"eval_loss": 0.5786373019218445, "eval_bleu": 0.06554351881320032, "eval_precisions": [0.8125, 0.735224586288416, 0.6885245901639344, 0.6407766990291263], "eval_brevity_penalty": 0.09147709493048638, "eval_length_ratio": 0.29484029484029484, "eval_translation_length": 480, "eval_reference_length": 1628, "eval_cer": 0.756439574515847, "eval_wer": 0.8318950156548721, "eval_runtime": 71.2541, "eval_samples_per_second": 0.8, "eval_steps_per_second": 0.8, "epoch": 3.0}
+{"eval_loss": 0.5839809775352478, "eval_bleu": 0.06665420511378176, "eval_precisions": [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846], "eval_brevity_penalty": 0.09342461598030836, "eval_length_ratio": 0.2966830466830467, "eval_translation_length": 483, "eval_reference_length": 1628, "eval_cer": 0.7599193398908264, "eval_wer": 0.8328305920932787, "eval_runtime": 71.1781, "eval_samples_per_second": 0.801, "eval_steps_per_second": 0.801, "epoch": 4.0}

preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_thumbnail",
+    "do_align_long_axis",
+    "do_pad",
+    "random_padding",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "do_align_long_axis": false,
+  "do_normalize": true,
+  "do_pad": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "do_thumbnail": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
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+  "image_processor_type": "DonutImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
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+  "processor_class": "DonutProcessor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": [
+    720,
+    960
+  ]
+}

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sentencepiece.bpe.model

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special_tokens_map.json

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tokenizer_config.json

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+}