End of training

README.md

@@ -0,0 +1,77 @@
+---
+library_name: transformers
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+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 an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5213
+- Answer: {'precision': 0.8422247446083996, 'recall': 0.9082007343941249, 'f1': 0.8739693757361603, 'number': 817}
+- Header: {'precision': 0.6633663366336634, 'recall': 0.5630252100840336, 'f1': 0.6090909090909091, 'number': 119}
+- Question: {'precision': 0.8962093862815884, 'recall': 0.9220055710306406, 'f1': 0.9089244851258581, 'number': 1077}
+- Overall Precision: 0.8622
+- Overall Recall: 0.8952
+- Overall F1: 0.8784
+- Overall Accuracy: 0.8101
+
+## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4065        | 10.5263  | 200  | 0.9595          | {'precision': 0.7962347729789591, 'recall': 0.8800489596083231, 'f1': 0.8360465116279069, 'number': 817} | {'precision': 0.5544554455445545, 'recall': 0.47058823529411764, 'f1': 0.509090909090909, 'number': 119}  | {'precision': 0.8766999093381687, 'recall': 0.8978644382544104, 'f1': 0.8871559633027524, 'number': 1077} | 0.8268            | 0.8654         | 0.8456     | 0.7996           |
+| 0.051         | 21.0526  | 400  | 1.3283          | {'precision': 0.8666666666666667, 'recall': 0.8910648714810282, 'f1': 0.8786964393482196, 'number': 817} | {'precision': 0.5227272727272727, 'recall': 0.5798319327731093, 'f1': 0.5498007968127491, 'number': 119}  | {'precision': 0.8887841658812441, 'recall': 0.8755803156917363, 'f1': 0.882132834424696, 'number': 1077}  | 0.8559            | 0.8644         | 0.8601     | 0.7931           |
+| 0.0133        | 31.5789  | 600  | 1.4326          | {'precision': 0.8370786516853933, 'recall': 0.9118727050183598, 'f1': 0.8728763913298183, 'number': 817} | {'precision': 0.5144927536231884, 'recall': 0.5966386554621849, 'f1': 0.5525291828793774, 'number': 119}  | {'precision': 0.9006882989183874, 'recall': 0.8505106778087279, 'f1': 0.8748806112702961, 'number': 1077} | 0.8469            | 0.8604         | 0.8536     | 0.7973           |
+| 0.0065        | 42.1053  | 800  | 1.5819          | {'precision': 0.8505096262740657, 'recall': 0.9192166462668299, 'f1': 0.8835294117647059, 'number': 817} | {'precision': 0.6794871794871795, 'recall': 0.44537815126050423, 'f1': 0.5380710659898478, 'number': 119} | {'precision': 0.8934802571166207, 'recall': 0.903435468895079, 'f1': 0.8984302862419206, 'number': 1077}  | 0.8668            | 0.8828         | 0.8747     | 0.8080           |
+| 0.0064        | 52.6316  | 1000 | 1.4306          | {'precision': 0.866902237926973, 'recall': 0.9008567931456548, 'f1': 0.8835534213685473, 'number': 817}  | {'precision': 0.6185567010309279, 'recall': 0.5042016806722689, 'f1': 0.5555555555555556, 'number': 119}  | {'precision': 0.8900279589934762, 'recall': 0.8867223769730733, 'f1': 0.8883720930232558, 'number': 1077} | 0.8673            | 0.8698         | 0.8686     | 0.7997           |
+| 0.0027        | 63.1579  | 1200 | 1.4304          | {'precision': 0.851258581235698, 'recall': 0.9106487148102815, 'f1': 0.8799526907155529, 'number': 817}  | {'precision': 0.648936170212766, 'recall': 0.5126050420168067, 'f1': 0.5727699530516431, 'number': 119}   | {'precision': 0.8839285714285714, 'recall': 0.9192200557103064, 'f1': 0.9012289485662267, 'number': 1077} | 0.8597            | 0.8917         | 0.8754     | 0.8212           |
+| 0.0013        | 73.6842  | 1400 | 1.4734          | {'precision': 0.851808634772462, 'recall': 0.8935128518971848, 'f1': 0.8721624850657107, 'number': 817}  | {'precision': 0.6521739130434783, 'recall': 0.5042016806722689, 'f1': 0.5687203791469194, 'number': 119}  | {'precision': 0.8790035587188612, 'recall': 0.9173630454967502, 'f1': 0.8977737392094502, 'number': 1077} | 0.8577            | 0.8833         | 0.8703     | 0.8152           |
+| 0.0007        | 84.2105  | 1600 | 1.6066          | {'precision': 0.8348115299334812, 'recall': 0.9216646266829865, 'f1': 0.8760907504363001, 'number': 817} | {'precision': 0.6333333333333333, 'recall': 0.4789915966386555, 'f1': 0.5454545454545454, 'number': 119}  | {'precision': 0.8842676311030742, 'recall': 0.9080779944289693, 'f1': 0.8960146587265231, 'number': 1077} | 0.8522            | 0.8882         | 0.8699     | 0.8039           |
+| 0.0005        | 94.7368  | 1800 | 1.5574          | {'precision': 0.8433598183881952, 'recall': 0.9094247246022031, 'f1': 0.8751472320376914, 'number': 817} | {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}              | {'precision': 0.9031657355679702, 'recall': 0.9006499535747446, 'f1': 0.901906090190609, 'number': 1077}  | 0.8659            | 0.8823         | 0.8740     | 0.8016           |
+| 0.0007        | 105.2632 | 2000 | 1.5402          | {'precision': 0.8447488584474886, 'recall': 0.9057527539779682, 'f1': 0.874187832250443, 'number': 817}  | {'precision': 0.6326530612244898, 'recall': 0.5210084033613446, 'f1': 0.5714285714285716, 'number': 119}  | {'precision': 0.8877917414721723, 'recall': 0.9182915506035283, 'f1': 0.9027841168416247, 'number': 1077} | 0.8578            | 0.8897         | 0.8734     | 0.8049           |
+| 0.0002        | 115.7895 | 2200 | 1.5213          | {'precision': 0.8422247446083996, 'recall': 0.9082007343941249, 'f1': 0.8739693757361603, 'number': 817} | {'precision': 0.6633663366336634, 'recall': 0.5630252100840336, 'f1': 0.6090909090909091, 'number': 119}  | {'precision': 0.8962093862815884, 'recall': 0.9220055710306406, 'f1': 0.9089244851258581, 'number': 1077} | 0.8622            | 0.8952         | 0.8784     | 0.8101           |
+| 0.0003        | 126.3158 | 2400 | 1.5403          | {'precision': 0.8424036281179138, 'recall': 0.9094247246022031, 'f1': 0.8746321365509123, 'number': 817} | {'precision': 0.6568627450980392, 'recall': 0.5630252100840336, 'f1': 0.6063348416289592, 'number': 119}  | {'precision': 0.8925318761384335, 'recall': 0.9099350046425255, 'f1': 0.9011494252873563, 'number': 1077} | 0.8598            | 0.8892         | 0.8742     | 0.8084           |
+
+
+### Framework versions
+
+- Transformers 4.48.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

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