End of training

README.md

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+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- layoutlmv3
+model-index:
+- name: Inkaso_beta
+  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. -->
+
+# Inkaso_beta
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0801
+- Creditor address: {'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48}
+- Creditor name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}
+- Creditor proxy: {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37}
+- Debtor address: {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53}
+- Debtor name: {'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33}
+- Doc id: {'precision': 0.85, 'recall': 0.8947368421052632, 'f1': 0.8717948717948718, 'number': 19}
+- Title: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}
+- Overall Precision: 0.9492
+- Overall Recall: 0.9419
+- Overall F1: 0.9455
+- Overall Accuracy: 0.9831
+
+## 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: 3e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Creditor address                                                                                        | Creditor name                                                                                           | Creditor proxy                                                                                          | Debtor address                                                                                          | Debtor name                                                                               | Doc id                                                                                                  | Title                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4642        | 6.6667  | 20   | 0.2502          | {'precision': 0.782608695652174, 'recall': 0.75, 'f1': 0.7659574468085107, 'number': 48}                | {'precision': 0.9354838709677419, 'recall': 0.8529411764705882, 'f1': 0.8923076923076922, 'number': 34} | {'precision': 0.8, 'recall': 0.6486486486486487, 'f1': 0.7164179104477612, 'number': 37}                | {'precision': 0.8205128205128205, 'recall': 0.6037735849056604, 'f1': 0.6956521739130435, 'number': 53} | {'precision': 0.95, 'recall': 0.5757575757575758, 'f1': 0.7169811320754716, 'number': 33} | {'precision': 1.0, 'recall': 0.2631578947368421, 'f1': 0.4166666666666667, 'number': 19}                | {'precision': 0.8461538461538461, 'recall': 0.3235294117647059, 'f1': 0.46808510638297873, 'number': 34} | 0.8478            | 0.6047         | 0.7059     | 0.9330           |
+| 0.1387        | 13.3333 | 40   | 0.0914          | {'precision': 1.0, 'recall': 0.9166666666666666, 'f1': 0.9565217391304348, 'number': 48}                | {'precision': 0.9714285714285714, 'recall': 1.0, 'f1': 0.9855072463768115, 'number': 34}                | {'precision': 0.7777777777777778, 'recall': 0.7567567567567568, 'f1': 0.7671232876712328, 'number': 37} | {'precision': 0.9444444444444444, 'recall': 0.9622641509433962, 'f1': 0.9532710280373832, 'number': 53} | {'precision': 0.8918918918918919, 'recall': 1.0, 'f1': 0.9428571428571428, 'number': 33}  | {'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                               | 0.9234            | 0.9341         | 0.9287     | 0.9795           |
+| 0.0431        | 20.0    | 60   | 0.0774          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 48}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                              | {'precision': 0.8181818181818182, 'recall': 0.7297297297297297, 'f1': 0.7714285714285715, 'number': 37} | {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53}                | {'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33}  | {'precision': 0.7727272727272727, 'recall': 0.8947368421052632, 'f1': 0.8292682926829269, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                               | 0.9425            | 0.9535         | 0.9480     | 0.9837           |
+| 0.0216        | 26.6667 | 80   | 0.0842          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 48}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                              | {'precision': 0.7631578947368421, 'recall': 0.7837837837837838, 'f1': 0.7733333333333334, 'number': 37} | {'precision': 0.9454545454545454, 'recall': 0.9811320754716981, 'f1': 0.9629629629629629, 'number': 53} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33}  | {'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                               | 0.9286            | 0.9574         | 0.9427     | 0.9825           |
+| 0.0142        | 33.3333 | 100  | 0.0840          | {'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48}                             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                              | {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37} | {'precision': 0.9629629629629629, 'recall': 0.9811320754716981, 'f1': 0.9719626168224299, 'number': 53} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33}  | {'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                               | 0.9416            | 0.9380         | 0.9398     | 0.9819           |
+| 0.0105        | 40.0    | 120  | 0.0838          | {'precision': 0.9772727272727273, 'recall': 0.8958333333333334, 'f1': 0.9347826086956522, 'number': 48} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                              | {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37} | {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53}                | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33}  | {'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                               | 0.9385            | 0.9457         | 0.9421     | 0.9819           |
+| 0.0081        | 46.6667 | 140  | 0.0801          | {'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48}                             | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                              | {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37} | {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53}                | {'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33}  | {'precision': 0.85, 'recall': 0.8947368421052632, 'f1': 0.8717948717948718, 'number': 19}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}                                               | 0.9492            | 0.9419         | 0.9455     | 0.9831           |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.3.0+cu118
+- Datasets 2.19.0
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,46 @@
+{
+  "_name_or_path": "microsoft/layoutlm-base-uncased",
+  "architectures": [
+    "LayoutLMForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "title",
+    "2": "creditor name",
+    "3": "creditor address",
+    "4": "creditor proxy",
+    "5": "debtor name",
+    "6": "debtor address",
+    "7": "doc id"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "O": 0,
+    "creditor address": 3,
+    "creditor name": 2,
+    "creditor proxy": 4,
+    "debtor address": 6,
+    "debtor name": 5,
+    "doc id": 7,
+    "title": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_2d_position_embeddings": 1024,
+  "max_position_embeddings": 512,
+  "model_type": "layoutlm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3cb115f3bcfc4c55c55e7ccfd4b046b5689c59450cf817a1ed3b29be013076a5
+size 450561288

preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "apply_ocr",
+    "ocr_lang",
+    "tesseract_config",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "apply_ocr": false,
+  "do_resize": true,
+  "image_processor_type": "LayoutLMv2ImageProcessor",
+  "ocr_lang": null,
+  "processor_class": "LayoutLMv2Processor",
+  "resample": 2,
+  "size": {
+    "height": 224,
+    "width": 224
+  },
+  "tesseract_config": ""
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "apply_ocr": false,
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "cls_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "only_label_first_subword": true,
+  "pad_token": "[PAD]",
+  "pad_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv2Processor",
+  "sep_token": "[SEP]",
+  "sep_token_box": [
+    1000,
+    1000,
+    1000,
+    1000
+  ],
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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+oid sha256:6088b4ef604cc0239bc18108879f041424580d31aeaba2bd072ae705b563bc27
+size 4984