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-aadhaar-red
+  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-aadhaar-red
+
+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: 0.0287
+- Adhaar Number: {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39}
+- Ame: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}
+- Ather Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}
+- Ather Name Back: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
+- Ather Name Front Top: {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11}
+- Ddress Back: {'precision': 0.9512195121951219, 'recall': 0.9629629629629629, 'f1': 0.9570552147239264, 'number': 81}
+- Ddress Front: {'precision': 0.9615384615384616, 'recall': 0.9615384615384616, 'f1': 0.9615384615384616, 'number': 52}
+- Ender: {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21}
+- Ob: {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}
+- Obile Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
+- Ther: {'precision': 0.958974358974359, 'recall': 0.9689119170984456, 'f1': 0.9639175257731959, 'number': 193}
+- Overall Precision: 0.9623
+- Overall Recall: 0.9725
+- Overall F1: 0.9673
+- Overall Accuracy: 0.9973
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1651        | 10.0  | 200  | 0.0226          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}                                              | {'precision': 0.9130434782608695, 'recall': 0.9130434782608695, 'f1': 0.9130434782608695, 'number': 23} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.926829268292683, 'recall': 0.9382716049382716, 'f1': 0.9325153374233128, 'number': 81}  | {'precision': 0.9811320754716981, 'recall': 1.0, 'f1': 0.9904761904761905, 'number': 52}                | {'precision': 0.9047619047619048, 'recall': 0.9047619047619048, 'f1': 0.9047619047619048, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9384615384615385, 'recall': 0.9481865284974094, 'f1': 0.9432989690721649, 'number': 193} | 0.9497            | 0.9597         | 0.9547     | 0.9962           |
+| 0.004         | 20.0  | 400  | 0.0270          | {'precision': 0.9487179487179487, 'recall': 0.9487179487179487, 'f1': 0.9487179487179487, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.926829268292683, 'recall': 0.9382716049382716, 'f1': 0.9325153374233128, 'number': 81}  | {'precision': 0.9615384615384616, 'recall': 0.9615384615384616, 'f1': 0.9615384615384616, 'number': 52} | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9090909090909091, 'recall': 0.9523809523809523, 'f1': 0.9302325581395349, 'number': 21} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9333333333333333, 'recall': 0.9430051813471503, 'f1': 0.9381443298969072, 'number': 193} | 0.9454            | 0.9534         | 0.9494     | 0.9964           |
+| 0.0016        | 30.0  | 600  | 0.0321          | {'precision': 0.925, 'recall': 0.9487179487179487, 'f1': 0.9367088607594937, 'number': 39}              | {'precision': 0.9565217391304348, 'recall': 0.9565217391304348, 'f1': 0.9565217391304348, 'number': 23} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.9146341463414634, 'recall': 0.9259259259259259, 'f1': 0.9202453987730062, 'number': 81} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 52}                                              | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9282051282051282, 'recall': 0.9378238341968912, 'f1': 0.9329896907216495, 'number': 193} | 0.9414            | 0.9534         | 0.9474     | 0.9959           |
+| 0.0013        | 40.0  | 800  | 0.0243          | {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.9390243902439024, 'recall': 0.9506172839506173, 'f1': 0.9447852760736196, 'number': 81} | {'precision': 0.9803921568627451, 'recall': 0.9615384615384616, 'f1': 0.970873786407767, 'number': 52}  | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9487179487179487, 'recall': 0.9585492227979274, 'f1': 0.9536082474226804, 'number': 193} | 0.96              | 0.9661         | 0.9630     | 0.9973           |
+| 0.0006        | 50.0  | 1000 | 0.0400          | {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 0.8947368421052632, 'f1': 0.9444444444444444, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.8902439024390244, 'recall': 0.9012345679012346, 'f1': 0.8957055214723927, 'number': 81} | {'precision': 0.9803921568627451, 'recall': 0.9615384615384616, 'f1': 0.970873786407767, 'number': 52}  | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9384615384615385, 'recall': 0.9481865284974094, 'f1': 0.9432989690721649, 'number': 193} | 0.9471            | 0.9492         | 0.9481     | 0.9951           |
+| 0.0003        | 60.0  | 1200 | 0.0323          | {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39} | {'precision': 0.9565217391304348, 'recall': 0.9565217391304348, 'f1': 0.9565217391304348, 'number': 23} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.926829268292683, 'recall': 0.9382716049382716, 'f1': 0.9325153374233128, 'number': 81}  | {'precision': 0.9423076923076923, 'recall': 0.9423076923076923, 'f1': 0.9423076923076923, 'number': 52} | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9384615384615385, 'recall': 0.9481865284974094, 'f1': 0.9432989690721649, 'number': 193} | 0.9455            | 0.9555         | 0.9505     | 0.9964           |
+| 0.0005        | 70.0  | 1400 | 0.0287          | {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9512195121951219, 'recall': 0.9629629629629629, 'f1': 0.9570552147239264, 'number': 81} | {'precision': 0.9615384615384616, 'recall': 0.9615384615384616, 'f1': 0.9615384615384616, 'number': 52} | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.958974358974359, 'recall': 0.9689119170984456, 'f1': 0.9639175257731959, 'number': 193}  | 0.9623            | 0.9725         | 0.9673     | 0.9973           |
+| 0.0004        | 80.0  | 1600 | 0.0417          | {'precision': 0.9487179487179487, 'recall': 0.9487179487179487, 'f1': 0.9487179487179487, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9036144578313253, 'recall': 0.9259259259259259, 'f1': 0.9146341463414634, 'number': 81} | {'precision': 0.9607843137254902, 'recall': 0.9423076923076923, 'f1': 0.9514563106796117, 'number': 52} | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9285714285714286, 'recall': 0.9430051813471503, 'f1': 0.9357326478149101, 'number': 193} | 0.9393            | 0.9513         | 0.9453     | 0.9951           |
+| 0.0001        | 90.0  | 1800 | 0.0362          | {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.9146341463414634, 'recall': 0.9259259259259259, 'f1': 0.9202453987730062, 'number': 81} | {'precision': 0.9803921568627451, 'recall': 0.9615384615384616, 'f1': 0.970873786407767, 'number': 52}  | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9384615384615385, 'recall': 0.9481865284974094, 'f1': 0.9432989690721649, 'number': 193} | 0.9516            | 0.9576         | 0.9546     | 0.9964           |
+| 0.0001        | 100.0 | 2000 | 0.0378          | {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.9146341463414634, 'recall': 0.9259259259259259, 'f1': 0.9202453987730062, 'number': 81} | {'precision': 0.9615384615384616, 'recall': 0.9615384615384616, 'f1': 0.9615384615384616, 'number': 52} | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9336734693877551, 'recall': 0.9481865284974094, 'f1': 0.9408740359897172, 'number': 193} | 0.9476            | 0.9576         | 0.9526     | 0.9962           |
+| 0.0001        | 110.0 | 2200 | 0.0379          | {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39} | {'precision': 0.9565217391304348, 'recall': 0.9565217391304348, 'f1': 0.9565217391304348, 'number': 23} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.9146341463414634, 'recall': 0.9259259259259259, 'f1': 0.9202453987730062, 'number': 81} | {'precision': 0.9615384615384616, 'recall': 0.9615384615384616, 'f1': 0.9615384615384616, 'number': 52} | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9285714285714286, 'recall': 0.9430051813471503, 'f1': 0.9357326478149101, 'number': 193} | 0.9434            | 0.9534         | 0.9484     | 0.9959           |
+| 0.0001        | 120.0 | 2400 | 0.0361          | {'precision': 0.9743589743589743, 'recall': 0.9743589743589743, 'f1': 0.9743589743589743, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 23}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 0.9146341463414634, 'recall': 0.9259259259259259, 'f1': 0.9202453987730062, 'number': 81} | {'precision': 0.9615384615384616, 'recall': 0.9615384615384616, 'f1': 0.9615384615384616, 'number': 52} | {'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21} | {'precision': 0.9545454545454546, 'recall': 1.0, 'f1': 0.9767441860465117, 'number': 21}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9336734693877551, 'recall': 0.9481865284974094, 'f1': 0.9408740359897172, 'number': 193} | 0.9476            | 0.9576         | 0.9526     | 0.9962           |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "apply_ocr",
+    "ocr_lang",
+    "tesseract_config",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "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

@@ -0,0 +1,51 @@
+{
+  "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

@@ -0,0 +1,78 @@
+{
+  "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>"
+}