Duplicate from nielsr/layoutlmv3-finetuned-funsd
Browse filesCo-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
- .gitattributes +27 -0
- .gitignore +1 -0
- README.md +98 -0
- all_results.json +17 -0
- config.json +57 -0
- emissions.csv +2 -0
- eval_results.json +12 -0
- merges.txt +0 -0
- preprocessor_config.json +19 -0
- pytorch_model.bin +3 -0
- runs/May02_16-26-18_brutasse/1651508794.4462817/events.out.tfevents.1651508794.brutasse.9557.1 +3 -0
- runs/May02_16-26-18_brutasse/events.out.tfevents.1651508794.brutasse.9557.0 +3 -0
- runs/May02_16-26-18_brutasse/events.out.tfevents.1651510649.brutasse.9557.2 +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- train_results.json +8 -0
- trainer_state.json +157 -0
- training_args.bin +3 -0
- vocab.json +0 -0
.gitattributes
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.gitignore
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checkpoint-*/
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README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- nielsr/funsd-layoutlmv3
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: layoutlmv3-finetuned-funsd
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: nielsr/funsd-layoutlmv3
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type: nielsr/funsd-layoutlmv3
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args: funsd
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metrics:
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- name: Precision
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type: precision
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value: 0.9026198714780029
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- name: Recall
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type: recall
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value: 0.913
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- name: F1
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type: f1
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value: 0.9077802634849614
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- name: Accuracy
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type: accuracy
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value: 0.8330271015158475
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duplicated_from: nielsr/layoutlmv3-finetuned-funsd
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# layoutlmv3-finetuned-funsd
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1164
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- Precision: 0.9026
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- Recall: 0.913
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- F1: 0.9078
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- Accuracy: 0.8330
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The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps: 1000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 10.0 | 100 | 0.5238 | 0.8366 | 0.886 | 0.8606 | 0.8410 |
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| No log | 20.0 | 200 | 0.6930 | 0.8751 | 0.8965 | 0.8857 | 0.8322 |
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| No log | 30.0 | 300 | 0.7784 | 0.8902 | 0.908 | 0.8990 | 0.8414 |
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| No log | 40.0 | 400 | 0.9056 | 0.8916 | 0.905 | 0.8983 | 0.8364 |
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| 0.2429 | 50.0 | 500 | 1.0016 | 0.8954 | 0.9075 | 0.9014 | 0.8298 |
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| 0.2429 | 60.0 | 600 | 1.0097 | 0.8899 | 0.897 | 0.8934 | 0.8294 |
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| 0.2429 | 70.0 | 700 | 1.0722 | 0.9035 | 0.9085 | 0.9060 | 0.8315 |
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| 0.2429 | 80.0 | 800 | 1.0884 | 0.8905 | 0.9105 | 0.9004 | 0.8269 |
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| 0.2429 | 90.0 | 900 | 1.1292 | 0.8938 | 0.909 | 0.9013 | 0.8279 |
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| 0.0098 | 100.0 | 1000 | 1.1164 | 0.9026 | 0.913 | 0.9078 | 0.8330 |
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### Framework versions
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- Transformers 4.19.0.dev0
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- Pytorch 1.11.0+cu113
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- Datasets 2.0.0
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- Tokenizers 0.11.6
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all_results.json
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{
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"epoch": 100.0,
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"eval_accuracy": 0.8330271015158475,
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"eval_f1": 0.9077802634849614,
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"eval_loss": 1.1164220571517944,
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"eval_precision": 0.9026198714780029,
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"eval_recall": 0.913,
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"eval_runtime": 4.5243,
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"eval_samples": 54,
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"eval_samples_per_second": 11.936,
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"eval_steps_per_second": 0.884,
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"train_loss": 0.12633917331695557,
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"train_runtime": 1653.606,
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"train_samples": 150,
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"train_samples_per_second": 9.676,
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"train_steps_per_second": 0.605
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}
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config.json
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{
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"_name_or_path": "microsoft/layoutlmv3-base",
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"architectures": [
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"LayoutLMv3ForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"coordinate_size": 128,
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"eos_token_id": 2,
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"finetuning_task": "ner",
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"has_relative_attention_bias": true,
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"has_spatial_attention_bias": true,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "O",
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"1": "B-HEADER",
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"2": "I-HEADER",
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"3": "B-QUESTION",
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"4": "I-QUESTION",
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"5": "B-ANSWER",
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"6": "I-ANSWER"
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},
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"initializer_range": 0.02,
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"input_size": 224,
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"intermediate_size": 3072,
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"label2id": {
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"B-ANSWER": 5,
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"B-HEADER": 1,
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"B-QUESTION": 3,
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"I-ANSWER": 6,
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"I-HEADER": 2,
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"I-QUESTION": 4,
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"O": 0
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},
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"layer_norm_eps": 1e-05,
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"max_2d_position_embeddings": 1024,
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"max_position_embeddings": 514,
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"max_rel_2d_pos": 256,
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"max_rel_pos": 128,
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"model_type": "layoutlmv3",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"patch_size": 16,
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"rel_2d_pos_bins": 64,
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"rel_pos_bins": 32,
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"second_input_size": 112,
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"shape_size": 128,
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"torch_dtype": "float32",
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"transformers_version": "4.19.0.dev0",
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"type_vocab_size": 1,
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"visual_embed": true,
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"vocab_size": 50265
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}
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emissions.csv
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timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region
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2022-05-02T16:54:11,8eebd56a-eb8f-4892-9c80-7ac739a77655,codecarbon,1653.8408570289612,0.028152538025484036,0.1337565669441834,United States,USA,new york,N,,
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eval_results.json
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{
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"epoch": 100.0,
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"eval_accuracy": 0.8330271015158475,
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"eval_f1": 0.9077802634849614,
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"eval_loss": 1.1164220571517944,
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"eval_precision": 0.9026198714780029,
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"eval_recall": 0.913,
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"eval_runtime": 4.5243,
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"eval_samples": 54,
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"eval_samples_per_second": 11.936,
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"eval_steps_per_second": 0.884
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}
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merges.txt
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preprocessor_config.json
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{
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"apply_ocr": true,
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "LayoutLMv3FeatureExtractor",
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"image_mean": [
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0.5,
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0.5
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"resample": 2,
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"size": 224
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}
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}
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tokenizer.json
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tokenizer_config.json
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training_args.bin
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vocab.json
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