End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+model-index:
+- name: lilt-en-funsd
+  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. -->
+
+# lilt-en-funsd
+
+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: 1.8731
+- Answer: {'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817}
+- Header: {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119}
+- Question: {'precision': 0.9105166051660517, 'recall': 0.9164345403899722, 'f1': 0.9134659879685332, 'number': 1077}
+- Overall Precision: 0.8792
+- Overall Recall: 0.8857
+- Overall F1: 0.8825
+- Overall Accuracy: 0.7976
+
+## 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: 2500
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4323        | 10.53  | 200  | 1.0423          | {'precision': 0.8369195922989807, 'recall': 0.9045287637698899, 'f1': 0.8694117647058823, 'number': 817} | {'precision': 0.5405405405405406, 'recall': 0.5042016806722689, 'f1': 0.5217391304347826, 'number': 119}   | {'precision': 0.8869323447636701, 'recall': 0.8885793871866295, 'f1': 0.8877551020408162, 'number': 1077} | 0.8471            | 0.8723         | 0.8595     | 0.7981           |
+| 0.045         | 21.05  | 400  | 1.2757          | {'precision': 0.8435374149659864, 'recall': 0.9106487148102815, 'f1': 0.8758092995879929, 'number': 817} | {'precision': 0.5795454545454546, 'recall': 0.42857142857142855, 'f1': 0.49275362318840576, 'number': 119} | {'precision': 0.8626943005181347, 'recall': 0.9275766016713092, 'f1': 0.8939597315436242, 'number': 1077} | 0.8430            | 0.8912         | 0.8665     | 0.8026           |
+| 0.0133        | 31.58  | 600  | 1.4887          | {'precision': 0.8632075471698113, 'recall': 0.8959608323133414, 'f1': 0.8792792792792793, 'number': 817} | {'precision': 0.6020408163265306, 'recall': 0.4957983193277311, 'f1': 0.543778801843318, 'number': 119}    | {'precision': 0.8791887125220459, 'recall': 0.9257195914577531, 'f1': 0.9018543645409318, 'number': 1077} | 0.8596            | 0.8882         | 0.8737     | 0.7983           |
+| 0.0051        | 42.11  | 800  | 1.7382          | {'precision': 0.8601645123384254, 'recall': 0.8959608323133414, 'f1': 0.8776978417266187, 'number': 817} | {'precision': 0.5636363636363636, 'recall': 0.5210084033613446, 'f1': 0.5414847161572053, 'number': 119}   | {'precision': 0.9032558139534884, 'recall': 0.9015784586815228, 'f1': 0.9024163568773235, 'number': 1077} | 0.8669            | 0.8768         | 0.8718     | 0.7925           |
+| 0.004         | 52.63  | 1000 | 1.7599          | {'precision': 0.8307349665924276, 'recall': 0.9130966952264382, 'f1': 0.8699708454810495, 'number': 817} | {'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119}   | {'precision': 0.8939256572982774, 'recall': 0.9155060352831941, 'f1': 0.9045871559633027, 'number': 1077} | 0.8530            | 0.8907         | 0.8714     | 0.7941           |
+| 0.002         | 63.16  | 1200 | 1.8409          | {'precision': 0.8312985571587126, 'recall': 0.9167686658506732, 'f1': 0.8719441210710128, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119}    | {'precision': 0.8814949863263446, 'recall': 0.8978644382544104, 'f1': 0.8896044158233671, 'number': 1077} | 0.8461            | 0.8847         | 0.8650     | 0.7876           |
+| 0.0013        | 73.68  | 1400 | 1.7795          | {'precision': 0.81445523193096, 'recall': 0.9241126070991432, 'f1': 0.8658256880733943, 'number': 817}   | {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119}   | {'precision': 0.888785046728972, 'recall': 0.883008356545961, 'f1': 0.8858872845831393, 'number': 1077}   | 0.8432            | 0.8788         | 0.8606     | 0.7934           |
+| 0.0011        | 84.21  | 1600 | 1.8386          | {'precision': 0.8338833883388339, 'recall': 0.9277845777233782, 'f1': 0.8783314020857474, 'number': 817} | {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}   | {'precision': 0.8943985307621671, 'recall': 0.904363974001857, 'f1': 0.8993536472760849, 'number': 1077}  | 0.8573            | 0.8922         | 0.8744     | 0.7945           |
+| 0.0048        | 94.74  | 1800 | 1.8664          | {'precision': 0.8589595375722543, 'recall': 0.9094247246022031, 'f1': 0.8834720570749108, 'number': 817} | {'precision': 0.6504854368932039, 'recall': 0.5630252100840336, 'f1': 0.6036036036036037, 'number': 119}   | {'precision': 0.9003656307129799, 'recall': 0.914577530176416, 'f1': 0.9074159373560571, 'number': 1077}  | 0.8705            | 0.8917         | 0.8810     | 0.7927           |
+| 0.0004        | 105.26 | 2000 | 1.8672          | {'precision': 0.8634772462077013, 'recall': 0.9057527539779682, 'f1': 0.8841099163679809, 'number': 817} | {'precision': 0.7093023255813954, 'recall': 0.5126050420168067, 'f1': 0.5951219512195123, 'number': 119}   | {'precision': 0.8923076923076924, 'recall': 0.9155060352831941, 'f1': 0.9037580201649862, 'number': 1077} | 0.8726            | 0.8877         | 0.8801     | 0.7953           |
+| 0.0005        | 115.79 | 2200 | 1.8731          | {'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817} | {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119}    | {'precision': 0.9105166051660517, 'recall': 0.9164345403899722, 'f1': 0.9134659879685332, 'number': 1077} | 0.8792            | 0.8857         | 0.8825     | 0.7976           |
+| 0.0002        | 126.32 | 2400 | 1.9408          | {'precision': 0.8408071748878924, 'recall': 0.9179926560587516, 'f1': 0.8777062609713283, 'number': 817} | {'precision': 0.6310679611650486, 'recall': 0.5462184873949579, 'f1': 0.5855855855855856, 'number': 119}   | {'precision': 0.9091760299625468, 'recall': 0.9015784586815228, 'f1': 0.9053613053613054, 'number': 1077} | 0.8657            | 0.8872         | 0.8763     | 0.7935           |
+
+
+### Framework versions
+
+- Transformers 4.24.0
+- Pytorch 1.12.1+cu113
+- Datasets 2.7.1
+- Tokenizers 0.13.2

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