End of training

README.md

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+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+model-index:
+- name: lilt-invoices2
+  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-invoices2
+
+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: 0.0032
+- Amount: {'precision': 0.9982517482517482, 'recall': 1.0, 'f1': 0.9991251093613298, 'number': 571}
+- Billingaddress: {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161}
+- Description: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612}
+- Invoicedate: {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172}
+- Invoicetotal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 207}
+- Quantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 545}
+- Subtotal: {'precision': 1.0, 'recall': 0.9933774834437086, 'f1': 0.9966777408637874, 'number': 151}
+- Totaltax: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139}
+- Unitprice: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 492}
+- Vendorname: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208}
+- Overall Precision: 0.9994
+- Overall Recall: 0.9994
+- Overall F1: 0.9994
+- Overall Accuracy: 0.9994
+
+## 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: 500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Amount                                                                                                   | Billingaddress                                                                                           | Description                                                                                              | Invoicedate                                                                                              | Invoicetotal                                                                                             | Quantity                                                                                                | Subtotal                                                                                                 | Totaltax                                                                                                 | Unitprice                                                                                                | Vendorname                                                                                              | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.6178        | 4.35  | 100  | 0.1659          | {'precision': 0.8553654743390358, 'recall': 0.9632224168126094, 'f1': 0.9060955518945634, 'number': 571} | {'precision': 0.9815950920245399, 'recall': 0.9937888198757764, 'f1': 0.9876543209876544, 'number': 161} | {'precision': 0.9775641025641025, 'recall': 0.9967320261437909, 'f1': 0.9870550161812297, 'number': 612} | {'precision': 0.9940476190476191, 'recall': 0.9709302325581395, 'f1': 0.9823529411764705, 'number': 172} | {'precision': 0.8571428571428571, 'recall': 0.8985507246376812, 'f1': 0.8773584905660375, 'number': 207} | {'precision': 0.9890909090909091, 'recall': 0.998165137614679, 'f1': 0.993607305936073, 'number': 545}  | {'precision': 0.7664233576642335, 'recall': 0.695364238410596, 'f1': 0.7291666666666665, 'number': 151}  | {'precision': 0.8818897637795275, 'recall': 0.8057553956834532, 'f1': 0.8421052631578947, 'number': 139} | {'precision': 0.9809523809523809, 'recall': 0.8373983739837398, 'f1': 0.9035087719298245, 'number': 492} | {'precision': 0.9856459330143541, 'recall': 0.9903846153846154, 'f1': 0.988009592326139, 'number': 208} | 0.9368            | 0.9368         | 0.9368     | 0.9368           |
+| 0.1653        | 8.7   | 200  | 0.0668          | {'precision': 0.9420529801324503, 'recall': 0.9964973730297724, 'f1': 0.9685106382978723, 'number': 571} | {'precision': 0.9876543209876543, 'recall': 0.9937888198757764, 'f1': 0.9907120743034055, 'number': 161} | {'precision': 1.0, 'recall': 0.9901960784313726, 'f1': 0.9950738916256158, 'number': 612}                | {'precision': 0.9941520467836257, 'recall': 0.9883720930232558, 'f1': 0.9912536443148688, 'number': 172} | {'precision': 0.9140271493212669, 'recall': 0.9758454106280193, 'f1': 0.9439252336448598, 'number': 207} | {'precision': 0.9945255474452555, 'recall': 1.0, 'f1': 0.9972552607502287, 'number': 545}               | {'precision': 0.9328358208955224, 'recall': 0.8278145695364238, 'f1': 0.8771929824561403, 'number': 151} | {'precision': 0.9615384615384616, 'recall': 0.8992805755395683, 'f1': 0.929368029739777, 'number': 139}  | {'precision': 0.9978947368421053, 'recall': 0.9634146341463414, 'f1': 0.9803516028955533, 'number': 492} | {'precision': 1.0, 'recall': 0.9951923076923077, 'f1': 0.9975903614457832, 'number': 208}               | 0.9770            | 0.9770         | 0.9770     | 0.9770           |
+| 0.0676        | 13.04 | 300  | 0.0208          | {'precision': 0.9861111111111112, 'recall': 0.9947460595446584, 'f1': 0.990409764603313, 'number': 571}  | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612}                                              | {'precision': 0.9941860465116279, 'recall': 0.9941860465116279, 'f1': 0.9941860465116279, 'number': 172} | {'precision': 0.9951219512195122, 'recall': 0.9855072463768116, 'f1': 0.9902912621359223, 'number': 207} | {'precision': 0.9963369963369964, 'recall': 0.998165137614679, 'f1': 0.9972502291475711, 'number': 545} | {'precision': 1.0, 'recall': 0.9602649006622517, 'f1': 0.9797297297297297, 'number': 151}                | {'precision': 0.9787234042553191, 'recall': 0.9928057553956835, 'f1': 0.9857142857142858, 'number': 139} | {'precision': 0.9918864097363083, 'recall': 0.9939024390243902, 'f1': 0.9928934010152284, 'number': 492} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208}                                             | 0.9942            | 0.9942         | 0.9942     | 0.9942           |
+| 0.0296        | 17.39 | 400  | 0.0067          | {'precision': 0.9982456140350877, 'recall': 0.9964973730297724, 'f1': 0.9973707274320772, 'number': 571} | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612}                                              | {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172}                | {'precision': 0.9951923076923077, 'recall': 1.0, 'f1': 0.9975903614457832, 'number': 207}                | {'precision': 0.9981684981684982, 'recall': 1.0, 'f1': 0.999083409715857, 'number': 545}                | {'precision': 0.9933333333333333, 'recall': 0.9867549668874173, 'f1': 0.9900332225913622, 'number': 151} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139}                                              | {'precision': 0.9979674796747967, 'recall': 0.9979674796747967, 'f1': 0.9979674796747967, 'number': 492} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208}                                             | 0.9982            | 0.9982         | 0.9982     | 0.9982           |
+| 0.0143        | 21.74 | 500  | 0.0032          | {'precision': 0.9982517482517482, 'recall': 1.0, 'f1': 0.9991251093613298, 'number': 571}                | {'precision': 1.0, 'recall': 0.9937888198757764, 'f1': 0.9968847352024921, 'number': 161}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 612}                                              | {'precision': 0.9942196531791907, 'recall': 1.0, 'f1': 0.9971014492753623, 'number': 172}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 207}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 545}                                             | {'precision': 1.0, 'recall': 0.9933774834437086, 'f1': 0.9966777408637874, 'number': 151}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 139}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 492}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 208}                                             | 0.9994            | 0.9994         | 0.9994     | 0.9994           |
+
+
+### Framework versions
+
+- Transformers 4.32.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3

preprocessor_config.json

@@ -0,0 +1,26 @@
+{
+  "apply_ocr": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "LayoutLMv3ImageProcessor",
+  "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 +1,85 @@
+{
+  "add_prefix_space": true,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "only_label_first_subword": true,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv3Processor",
+  "sep_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "tokenizer_class": "LayoutLMv3Tokenizer",
+  "trim_offsets": true,
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}