End of training

README.md

@@ -0,0 +1,84 @@
+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+model-index:
+- name: lilt-en-aadhaar2
+  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-aadhaar2
+
+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 None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0737
+- Adhaar Number: {'precision': 0.975, 'recall': 1.0, 'f1': 0.9873417721518987, 'number': 39}
+- Ame: {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 24}
+- Ather Name: {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3}
+- Ather Name Back: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}
+- Ather Name Front Top: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}
+- Ddress Back: {'precision': 0.953125, 'recall': 0.8970588235294118, 'f1': 0.9242424242424244, 'number': 68}
+- Ddress Front: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 49}
+- Ender: {'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}
+- Ob: {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22}
+- Obile Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
+- Ther: {'precision': 0.9297297297297298, 'recall': 0.9297297297297298, 'f1': 0.9297297297297298, 'number': 185}
+- Overall Precision: 0.9509
+- Overall Recall: 0.9467
+- Overall F1: 0.9488
+- Overall Accuracy: 0.9953
+
+## 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 | Adhaar Number                                                                                           | Ame                                                                                                     | Ather Name                                                                                             | Ather Name Back                                                                                         | Ather Name Front Top                                                                                    | Ddress Back                                                                                             | Ddress Front                                                                                            | Ender                                                                                    | Ob                                                                                                      | Obile Number                                               | Ther                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1686        | 8.33  | 200  | 0.0638          | {'precision': 0.8780487804878049, 'recall': 0.9230769230769231, 'f1': 0.9, 'number': 39}                | {'precision': 0.88, 'recall': 0.9166666666666666, 'f1': 0.8979591836734694, 'number': 24}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.8709677419354839, 'recall': 0.7941176470588235, 'f1': 0.8307692307692308, 'number': 68} | {'precision': 0.9795918367346939, 'recall': 0.9795918367346939, 'f1': 0.9795918367346939, 'number': 49} | {'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}  | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.8852459016393442, 'recall': 0.8756756756756757, 'f1': 0.8804347826086957, 'number': 185} | 0.9101            | 0.9            | 0.9050     | 0.9897           |
+| 0.0036        | 16.67 | 400  | 0.0807          | {'precision': 0.9047619047619048, 'recall': 0.9743589743589743, 'f1': 0.9382716049382716, 'number': 39} | {'precision': 0.88, 'recall': 0.9166666666666666, 'f1': 0.8979591836734694, 'number': 24}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}                                              | {'precision': 0.6521739130434783, 'recall': 0.8823529411764706, 'f1': 0.75, 'number': 17}               | {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}                 | {'precision': 0.7647058823529411, 'recall': 0.7647058823529411, 'f1': 0.7647058823529412, 'number': 68} | {'precision': 0.92, 'recall': 0.9387755102040817, 'f1': 0.9292929292929293, 'number': 49}               | {'precision': 1.0, 'recall': 0.9047619047619048, 'f1': 0.9500000000000001, 'number': 21} | {'precision': 0.9565217391304348, 'recall': 1.0, 'f1': 0.9777777777777777, 'number': 22}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.8716577540106952, 'recall': 0.8810810810810811, 'f1': 0.8763440860215054, 'number': 185} | 0.8664            | 0.8933         | 0.8796     | 0.9885           |
+| 0.0027        | 25.0  | 600  | 0.0797          | {'precision': 0.925, 'recall': 0.9487179487179487, 'f1': 0.9367088607594937, 'number': 39}              | {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 24}               | {'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'number': 3}                              | {'precision': 0.9411764705882353, 'recall': 0.9411764705882353, 'f1': 0.9411764705882353, 'number': 17} | {'precision': 0.9166666666666666, 'recall': 0.9166666666666666, 'f1': 0.9166666666666666, 'number': 12} | {'precision': 0.8405797101449275, 'recall': 0.8529411764705882, 'f1': 0.8467153284671534, 'number': 68} | {'precision': 0.9591836734693877, 'recall': 0.9591836734693877, 'f1': 0.9591836734693877, 'number': 49} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}                               | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.8870967741935484, 'recall': 0.8918918918918919, 'f1': 0.8894878706199462, 'number': 185} | 0.9035            | 0.9156         | 0.9095     | 0.9910           |
+| 0.0024        | 33.33 | 800  | 0.0623          | {'precision': 0.975, 'recall': 1.0, 'f1': 0.9873417721518987, 'number': 39}                             | {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 24}               | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.9375, 'recall': 0.8823529411764706, 'f1': 0.9090909090909091, 'number': 68}             | {'precision': 1.0, 'recall': 0.9795918367346939, 'f1': 0.9896907216494846, 'number': 49}                | {'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}  | {'precision': 0.8695652173913043, 'recall': 0.9090909090909091, 'f1': 0.888888888888889, 'number': 22}  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9239130434782609, 'recall': 0.918918918918919, 'f1': 0.9214092140921409, 'number': 185}  | 0.9439            | 0.9356         | 0.9397     | 0.9947           |
+| 0.0004        | 41.67 | 1000 | 0.0849          | {'precision': 0.95, 'recall': 0.9743589743589743, 'f1': 0.9620253164556962, 'number': 39}               | {'precision': 0.8846153846153846, 'recall': 0.9583333333333334, 'f1': 0.9199999999999999, 'number': 24} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.8769230769230769, 'recall': 0.8382352941176471, 'f1': 0.8571428571428571, 'number': 68} | {'precision': 0.9791666666666666, 'recall': 0.9591836734693877, 'f1': 0.9690721649484536, 'number': 49} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}                               | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.8864864864864865, 'recall': 0.8864864864864865, 'f1': 0.8864864864864865, 'number': 185} | 0.9156            | 0.9156         | 0.9156     | 0.9922           |
+| 0.0002        | 50.0  | 1200 | 0.0699          | {'precision': 0.975, 'recall': 1.0, 'f1': 0.9873417721518987, 'number': 39}                             | {'precision': 0.8461538461538461, 'recall': 0.9166666666666666, 'f1': 0.8799999999999999, 'number': 24} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.9230769230769231, 'recall': 0.8823529411764706, 'f1': 0.9022556390977443, 'number': 68} | {'precision': 1.0, 'recall': 0.9795918367346939, 'f1': 0.9896907216494846, 'number': 49}                | {'precision': 1.0, 'recall': 0.9047619047619048, 'f1': 0.9500000000000001, 'number': 21} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.8972972972972973, 'recall': 0.8972972972972973, 'f1': 0.8972972972972972, 'number': 185} | 0.9286            | 0.9244         | 0.9265     | 0.9938           |
+| 0.0001        | 58.33 | 1400 | 0.0733          | {'precision': 0.95, 'recall': 0.9743589743589743, 'f1': 0.9620253164556962, 'number': 39}               | {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 24}               | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.8923076923076924, 'recall': 0.8529411764705882, 'f1': 0.8721804511278195, 'number': 68} | {'precision': 1.0, 'recall': 0.9795918367346939, 'f1': 0.9896907216494846, 'number': 49}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}                               | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.907608695652174, 'recall': 0.9027027027027027, 'f1': 0.9051490514905148, 'number': 185}  | 0.9308            | 0.9267         | 0.9287     | 0.9941           |
+| 0.0002        | 66.67 | 1600 | 0.0728          | {'precision': 0.975, 'recall': 1.0, 'f1': 0.9873417721518987, 'number': 39}                             | {'precision': 0.8461538461538461, 'recall': 0.9166666666666666, 'f1': 0.8799999999999999, 'number': 24} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.9384615384615385, 'recall': 0.8970588235294118, 'f1': 0.9172932330827067, 'number': 68} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 49}                                              | {'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}  | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9139784946236559, 'recall': 0.918918918918919, 'f1': 0.9164420485175202, 'number': 185}  | 0.9379            | 0.94           | 0.9390     | 0.9947           |
+| 0.0002        | 75.0  | 1800 | 0.0731          | {'precision': 0.975, 'recall': 1.0, 'f1': 0.9873417721518987, 'number': 39}                             | {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 24}               | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.953125, 'recall': 0.8970588235294118, 'f1': 0.9242424242424244, 'number': 68}           | {'precision': 1.0, 'recall': 0.9795918367346939, 'f1': 0.9896907216494846, 'number': 49}                | {'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}  | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9243243243243243, 'recall': 0.9243243243243243, 'f1': 0.9243243243243243, 'number': 185} | 0.9485            | 0.9422         | 0.9454     | 0.9950           |
+| 0.0001        | 83.33 | 2000 | 0.0737          | {'precision': 0.975, 'recall': 1.0, 'f1': 0.9873417721518987, 'number': 39}                             | {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 24}               | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.953125, 'recall': 0.8970588235294118, 'f1': 0.9242424242424244, 'number': 68}           | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 49}                                              | {'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}  | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9297297297297298, 'recall': 0.9297297297297298, 'f1': 0.9297297297297298, 'number': 185} | 0.9509            | 0.9467         | 0.9488     | 0.9953           |
+| 0.0001        | 91.67 | 2200 | 0.0750          | {'precision': 0.975, 'recall': 1.0, 'f1': 0.9873417721518987, 'number': 39}                             | {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 24}               | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.953125, 'recall': 0.8970588235294118, 'f1': 0.9242424242424244, 'number': 68}           | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 49}                                              | {'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}  | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9297297297297298, 'recall': 0.9297297297297298, 'f1': 0.9297297297297298, 'number': 185} | 0.9509            | 0.9467         | 0.9488     | 0.9953           |
+| 0.0001        | 100.0 | 2400 | 0.0751          | {'precision': 0.975, 'recall': 1.0, 'f1': 0.9873417721518987, 'number': 39}                             | {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 24}               | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | {'precision': 0.9375, 'recall': 0.8823529411764706, 'f1': 0.9090909090909091, 'number': 68}             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 49}                                              | {'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}  | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9243243243243243, 'recall': 0.9243243243243243, 'f1': 0.9243243243243243, 'number': 185} | 0.9464            | 0.9422         | 0.9443     | 0.9950           |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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preprocessor_config.json

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+{
+  "apply_ocr": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "LayoutLMv3FeatureExtractor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "ocr_lang": null,
+  "processor_class": "LayoutLMv3Processor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  },
+  "tesseract_config": ""
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "cls_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "only_label_first_subword": true,
+  "pad_token": "<pad>",
+  "pad_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv3Processor",
+  "sep_token": "</s>",
+  "sep_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "tokenizer_class": "LayoutLMv3Tokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}