End of training

README.md

@@ -0,0 +1,72 @@
+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+model-index:
+- name: dokki-lilt
+  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. -->
+
+# dokki-lilt
+
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2732
+- Answer: {'precision': 0.859504132231405, 'recall': 0.8910648714810282, 'f1': 0.8750000000000001, 'number': 817}
+- Header: {'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119}
+- Question: {'precision': 0.886672710788758, 'recall': 0.9080779944289693, 'f1': 0.8972477064220182, 'number': 1077}
+- Overall Precision: 0.8624
+- Overall Recall: 0.8813
+- Overall F1: 0.8717
+- Overall Accuracy: 0.8133
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 600
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.6927        | 5.26  | 100  | 0.7696          | {'precision': 0.7593582887700535, 'recall': 0.8690330477356181, 'f1': 0.8105022831050229, 'number': 817} | {'precision': 0.5166666666666667, 'recall': 0.2605042016806723, 'f1': 0.3463687150837989, 'number': 119}  | {'precision': 0.8429973238180196, 'recall': 0.8774373259052924, 'f1': 0.8598726114649682, 'number': 1077} | 0.7968            | 0.8376         | 0.8167     | 0.7782           |
+| 0.1598        | 10.53 | 200  | 1.0235          | {'precision': 0.8254504504504504, 'recall': 0.8971848225214198, 'f1': 0.859824046920821, 'number': 817}  | {'precision': 0.6097560975609756, 'recall': 0.42016806722689076, 'f1': 0.4975124378109453, 'number': 119} | {'precision': 0.8744313011828936, 'recall': 0.8922934076137419, 'f1': 0.8832720588235293, 'number': 1077} | 0.8429            | 0.8664         | 0.8545     | 0.7863           |
+| 0.0536        | 15.79 | 300  | 1.1157          | {'precision': 0.8597785977859779, 'recall': 0.8555691554467564, 'f1': 0.8576687116564417, 'number': 817} | {'precision': 0.5789473684210527, 'recall': 0.46218487394957986, 'f1': 0.514018691588785, 'number': 119}  | {'precision': 0.8663793103448276, 'recall': 0.9331476323119777, 'f1': 0.8985248100134109, 'number': 1077} | 0.8506            | 0.8738         | 0.8620     | 0.8083           |
+| 0.0223        | 21.05 | 400  | 1.2167          | {'precision': 0.8367117117117117, 'recall': 0.9094247246022031, 'f1': 0.8715542521994134, 'number': 817} | {'precision': 0.5149253731343284, 'recall': 0.5798319327731093, 'f1': 0.5454545454545455, 'number': 119}  | {'precision': 0.8898148148148148, 'recall': 0.8922934076137419, 'f1': 0.8910523875753361, 'number': 1077} | 0.8435            | 0.8808         | 0.8617     | 0.8083           |
+| 0.0094        | 26.32 | 500  | 1.2313          | {'precision': 0.8564760793465578, 'recall': 0.8984088127294981, 'f1': 0.8769414575866189, 'number': 817} | {'precision': 0.6534653465346535, 'recall': 0.5546218487394958, 'f1': 0.6000000000000001, 'number': 119}  | {'precision': 0.8853790613718412, 'recall': 0.9108635097493036, 'f1': 0.8979405034324943, 'number': 1077} | 0.8621            | 0.8847         | 0.8733     | 0.8161           |
+| 0.0051        | 31.58 | 600  | 1.2732          | {'precision': 0.859504132231405, 'recall': 0.8910648714810282, 'f1': 0.8750000000000001, 'number': 817}  | {'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119}  | {'precision': 0.886672710788758, 'recall': 0.9080779944289693, 'f1': 0.8972477064220182, 'number': 1077}  | 0.8624            | 0.8813         | 0.8717     | 0.8133           |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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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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+}