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: LayoutLM_Invoice6
+  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. -->
+
+# LayoutLM_Invoice6
+
+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: 0.0219
+- Ax Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
+- Endor Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
+- Nvoice Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
+- Otal Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
+- Ustomer Address: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}
+- Ustomer Name: {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}
+- Overall Precision: 0.9846
+- Overall Recall: 0.9697
+- Overall F1: 0.9771
+- Overall Accuracy: 0.9939
+
+## 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: 1e-05
+- train_batch_size: 6
+- eval_batch_size: 3
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 300
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Ax Amount                                                                                | Endor Name                                                                                              | Nvoice Number                                                             | Otal Amount                                                                                             | Ustomer Address                                                                                         | Ustomer Name                                                                                            | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.8763        | 6.25  | 50   | 0.2290          | {'precision': 1.0, 'recall': 0.5454545454545454, 'f1': 0.7058823529411764, 'number': 11} | {'precision': 0.8181818181818182, 'recall': 0.8181818181818182, 'f1': 0.8181818181818182, 'number': 11} | {'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11} | {'precision': 0.5454545454545454, 'recall': 0.5454545454545454, 'f1': 0.5454545454545454, 'number': 11} | {'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11} | {'precision': 0.75, 'recall': 0.8181818181818182, 'f1': 0.7826086956521738, 'number': 11}               | 0.7903            | 0.7424         | 0.7656     | 0.9666           |
+| 0.1315        | 12.5  | 100  | 0.0312          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11}                | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | 0.9701            | 0.9848         | 0.9774     | 0.9970           |
+| 0.0239        | 18.75 | 150  | 0.0371          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}                | 0.9846            | 0.9697         | 0.9771     | 0.9939           |
+| 0.0098        | 25.0  | 200  | 0.0450          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}                | 0.9846            | 0.9697         | 0.9771     | 0.9939           |
+| 0.0085        | 31.25 | 250  | 0.0360          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}                | 0.9846            | 0.9697         | 0.9771     | 0.9939           |
+| 0.0065        | 37.5  | 300  | 0.0219          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}                                              | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}                | 0.9846            | 0.9697         | 0.9771     | 0.9939           |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.2.0+cpu
+- Datasets 2.12.0
+- Tokenizers 0.13.2

config.json

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+{
+  "_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": "Invoice_Number",
+    "1": "Vendor_Name",
+    "2": "Customer_Name",
+    "3": "Customer_Address",
+    "4": "Total_Amount",
+    "5": "Tax_Amount"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Customer_Address": 3,
+    "Customer_Name": 2,
+    "Invoice_Number": 0,
+    "Tax_Amount": 5,
+    "Total_Amount": 4,
+    "Vendor_Name": 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": "4.32.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

preprocessor_config.json

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+{
+  "apply_ocr": true,
+  "do_resize": true,
+  "feature_extractor_type": "LayoutLMv2FeatureExtractor",
+  "image_processor_type": "LayoutLMv2ImageProcessor",
+  "ocr_lang": null,
+  "processor_class": "LayoutLMv2Processor",
+  "resample": 2,
+  "size": {
+    "height": 224,
+    "width": 224
+  },
+  "tesseract_config": ""
+}

pytorch_model.bin

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+size 450598462

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "additional_special_tokens": null,
+  "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:c3ab2634191155fb4c1e86e3b3db1e213d77473c2beb3b8d57b8d631606f4d20
+size 4536