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: 1.5741
+- Answer: {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817}
+- Header: {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119}
+- Question: {'precision': 0.8917710196779964, 'recall': 0.9257195914577531, 'f1': 0.9084282460136676, 'number': 1077}
+- Overall Precision: 0.8736
+- Overall Recall: 0.8962
+- Overall F1: 0.8847
+- Overall Accuracy: 0.8189
+
+## 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4062        | 10.5263  | 200  | 1.0024          | {'precision': 0.8494749124854143, 'recall': 0.8910648714810282, 'f1': 0.8697729988052569, 'number': 817} | {'precision': 0.49572649572649574, 'recall': 0.48739495798319327, 'f1': 0.4915254237288135, 'number': 119} | {'precision': 0.8590308370044053, 'recall': 0.9052924791086351, 'f1': 0.8815551537070525, 'number': 1077} | 0.8350            | 0.8748         | 0.8544     | 0.7952           |
+| 0.0472        | 21.0526  | 400  | 1.2129          | {'precision': 0.8469626168224299, 'recall': 0.8873929008567931, 'f1': 0.8667065152420801, 'number': 817} | {'precision': 0.5660377358490566, 'recall': 0.5042016806722689, 'f1': 0.5333333333333334, 'number': 119}   | {'precision': 0.862280701754386, 'recall': 0.9127205199628597, 'f1': 0.8867839422643211, 'number': 1077}  | 0.8411            | 0.8783         | 0.8593     | 0.8137           |
+| 0.015         | 31.5789  | 600  | 1.4348          | {'precision': 0.8698296836982968, 'recall': 0.8751529987760098, 'f1': 0.8724832214765101, 'number': 817} | {'precision': 0.5445544554455446, 'recall': 0.46218487394957986, 'f1': 0.5, 'number': 119}                 | {'precision': 0.8731277533039647, 'recall': 0.9201485608170845, 'f1': 0.8960216998191681, 'number': 1077} | 0.8557            | 0.8748         | 0.8651     | 0.7975           |
+| 0.0058        | 42.1053  | 800  | 1.5168          | {'precision': 0.847926267281106, 'recall': 0.9008567931456548, 'f1': 0.8735905044510385, 'number': 817}  | {'precision': 0.5862068965517241, 'recall': 0.42857142857142855, 'f1': 0.49514563106796117, 'number': 119} | {'precision': 0.8852157943067034, 'recall': 0.8950789229340761, 'f1': 0.8901200369344413, 'number': 1077} | 0.8567            | 0.8698         | 0.8632     | 0.7969           |
+| 0.0044        | 52.6316  | 1000 | 1.5665          | {'precision': 0.8824969400244798, 'recall': 0.8824969400244798, 'f1': 0.8824969400244798, 'number': 817} | {'precision': 0.5929203539823009, 'recall': 0.5630252100840336, 'f1': 0.5775862068965517, 'number': 119}   | {'precision': 0.9010367577756834, 'recall': 0.8876508820798514, 'f1': 0.8942937324602432, 'number': 1077} | 0.8759            | 0.8664         | 0.8711     | 0.8027           |
+| 0.0028        | 63.1579  | 1200 | 1.5793          | {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817}   | {'precision': 0.5862068965517241, 'recall': 0.5714285714285714, 'f1': 0.5787234042553192, 'number': 119}   | {'precision': 0.8631211857018308, 'recall': 0.9192200557103064, 'f1': 0.8902877697841727, 'number': 1077} | 0.8539            | 0.8887         | 0.8710     | 0.8024           |
+| 0.0021        | 73.6842  | 1400 | 1.4760          | {'precision': 0.8803827751196173, 'recall': 0.9008567931456548, 'f1': 0.8905021173623715, 'number': 817} | {'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119}    | {'precision': 0.8951686417502279, 'recall': 0.9117920148560817, 'f1': 0.9034038638454462, 'number': 1077} | 0.8738            | 0.8872         | 0.8805     | 0.8198           |
+| 0.0007        | 84.2105  | 1600 | 1.5122          | {'precision': 0.8801897983392646, 'recall': 0.9082007343941249, 'f1': 0.8939759036144578, 'number': 817} | {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119}   | {'precision': 0.8805309734513275, 'recall': 0.9238625812441968, 'f1': 0.9016764839148165, 'number': 1077} | 0.8670            | 0.8937         | 0.8801     | 0.8148           |
+| 0.0008        | 94.7368  | 1800 | 1.5741          | {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817} | {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119}    | {'precision': 0.8917710196779964, 'recall': 0.9257195914577531, 'f1': 0.9084282460136676, 'number': 1077} | 0.8736            | 0.8962         | 0.8847     | 0.8189           |
+| 0.0006        | 105.2632 | 2000 | 1.5883          | {'precision': 0.8730904817861339, 'recall': 0.9094247246022031, 'f1': 0.8908872901678656, 'number': 817} | {'precision': 0.5803571428571429, 'recall': 0.5462184873949579, 'f1': 0.5627705627705628, 'number': 119}   | {'precision': 0.8871978513876455, 'recall': 0.9201485608170845, 'f1': 0.9033728350045579, 'number': 1077} | 0.8649            | 0.8937         | 0.8791     | 0.8142           |
+| 0.0004        | 115.7895 | 2200 | 1.6092          | {'precision': 0.8803317535545023, 'recall': 0.9094247246022031, 'f1': 0.8946417820590005, 'number': 817} | {'precision': 0.5609756097560976, 'recall': 0.5798319327731093, 'f1': 0.5702479338842975, 'number': 119}   | {'precision': 0.8968036529680365, 'recall': 0.9117920148560817, 'f1': 0.9042357274401475, 'number': 1077} | 0.8700            | 0.8912         | 0.8805     | 0.8116           |
+| 0.0002        | 126.3158 | 2400 | 1.6144          | {'precision': 0.8698710433763188, 'recall': 0.9082007343941249, 'f1': 0.8886227544910178, 'number': 817} | {'precision': 0.5652173913043478, 'recall': 0.5462184873949579, 'f1': 0.5555555555555555, 'number': 119}   | {'precision': 0.8953594176524113, 'recall': 0.9136490250696379, 'f1': 0.9044117647058824, 'number': 1077} | 0.8665            | 0.8897         | 0.8779     | 0.8129           |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.6.0
+- Tokenizers 0.21.1

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