End of training

README.md

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+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- layoutlmv4
+model-index:
+- name: layoutlm_alltags
+  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_alltags
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the layoutlmv4 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0891
+- Customer Address: {'precision': 0.7764705882352941, 'recall': 0.8048780487804879, 'f1': 0.7904191616766466, 'number': 82}
+- Customer Name: {'precision': 0.6666666666666666, 'recall': 0.8333333333333334, 'f1': 0.7407407407407408, 'number': 12}
+- Invoice Number: {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}
+- Tax Amount: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
+- Total Amount: {'precision': 0.7142857142857143, 'recall': 0.9090909090909091, 'f1': 0.8, 'number': 11}
+- Vendor Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}
+- Overall Precision: 0.7857
+- Overall Recall: 0.8397
+- Overall F1: 0.8118
+- Overall Accuracy: 0.9801
+
+## 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
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Customer Address                                                                                           | Customer Name                                                                                            | Invoice Number                                                                                          | Tax Amount                                                | Total Amount                                                                             | Vendor Name                                                                                             | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.8211        | 6.67  | 20   | 0.3797          | {'precision': 0.25316455696202533, 'recall': 0.24390243902439024, 'f1': 0.24844720496894412, 'number': 82} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}                                               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}                                              | 0.2532            | 0.1527         | 0.1905     | 0.9050           |
+| 0.3036        | 13.33 | 40   | 0.1941          | {'precision': 0.6448598130841121, 'recall': 0.8414634146341463, 'f1': 0.73015873015873, 'number': 82}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}                                               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                               | {'precision': 0.75, 'recall': 0.75, 'f1': 0.75, 'number': 12}                                           | 0.6555            | 0.5954         | 0.624      | 0.9493           |
+| 0.1537        | 20.0  | 60   | 0.1153          | {'precision': 0.7157894736842105, 'recall': 0.8292682926829268, 'f1': 0.768361581920904, 'number': 82}     | {'precision': 0.35714285714285715, 'recall': 0.4166666666666667, 'f1': 0.3846153846153846, 'number': 12} | {'precision': 0.8461538461538461, 'recall': 0.9166666666666666, 'f1': 0.8799999999999999, 'number': 12} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                               | {'precision': 0.8461538461538461, 'recall': 0.9166666666666666, 'f1': 0.8799999999999999, 'number': 12} | 0.7037            | 0.7252         | 0.7143     | 0.9663           |
+| 0.0862        | 26.67 | 80   | 0.0953          | {'precision': 0.8, 'recall': 0.8292682926829268, 'f1': 0.8143712574850299, 'number': 82}                   | {'precision': 0.6, 'recall': 0.75, 'f1': 0.6666666666666665, 'number': 12}                               | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 12}                               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                               | {'precision': 0.9166666666666666, 'recall': 0.9166666666666666, 'f1': 0.9166666666666666, 'number': 12} | 0.7519            | 0.7634         | 0.7576     | 0.9757           |
+| 0.0509        | 33.33 | 100  | 0.0846          | {'precision': 0.7857142857142857, 'recall': 0.8048780487804879, 'f1': 0.7951807228915663, 'number': 82}    | {'precision': 0.7333333333333333, 'recall': 0.9166666666666666, 'f1': 0.8148148148148148, 'number': 12}  | {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 1.0, 'recall': 0.5454545454545454, 'f1': 0.7058823529411764, 'number': 11} | {'precision': 0.8461538461538461, 'recall': 0.9166666666666666, 'f1': 0.8799999999999999, 'number': 12} | 0.8030            | 0.8092         | 0.8061     | 0.9775           |
+| 0.0354        | 40.0  | 120  | 0.0852          | {'precision': 0.7710843373493976, 'recall': 0.7804878048780488, 'f1': 0.7757575757575758, 'number': 82}    | {'precision': 0.6666666666666666, 'recall': 0.8333333333333334, 'f1': 0.7407407407407408, 'number': 12}  | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 12}                                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.7142857142857143, 'recall': 0.9090909090909091, 'f1': 0.8, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | 0.7770            | 0.8244         | 0.8        | 0.9797           |
+| 0.0297        | 46.67 | 140  | 0.0891          | {'precision': 0.7764705882352941, 'recall': 0.8048780487804879, 'f1': 0.7904191616766466, 'number': 82}    | {'precision': 0.6666666666666666, 'recall': 0.8333333333333334, 'f1': 0.7407407407407408, 'number': 12}  | {'precision': 0.8571428571428571, 'recall': 1.0, 'f1': 0.923076923076923, 'number': 12}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.7142857142857143, 'recall': 0.9090909090909091, 'f1': 0.8, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}                                              | 0.7857            | 0.8397         | 0.8118     | 0.9801           |
+
+
+### 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": "O",
+    "1": "B-Invoice_Number",
+    "2": "I-Invoice_Number",
+    "3": "B-Vendor_Name",
+    "4": "I-Vendor_Name",
+    "5": "B-Customer_Name",
+    "6": "I-Customer_Name",
+    "7": "B-Customer_Address",
+    "8": "I-Customer_Address",
+    "9": "B-Total_Amount",
+    "10": "I-Total_Amount",
+    "11": "B-Tax_Amount",
+    "12": "I-Tax_Amount"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-Customer_Address": 7,
+    "B-Customer_Name": 5,
+    "B-Invoice_Number": 1,
+    "B-Tax_Amount": 11,
+    "B-Total_Amount": 9,
+    "B-Vendor_Name": 3,
+    "I-Customer_Address": 8,
+    "I-Customer_Name": 6,
+    "I-Invoice_Number": 2,
+    "I-Tax_Amount": 12,
+    "I-Total_Amount": 10,
+    "I-Vendor_Name": 4,
+    "O": 0
+  },
+  "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": false,
+  "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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+oid sha256:a72141fc31374c9366f582e17745ad232e89f498953e7305c54c994c59da88c4
+size 450619966

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:a8bfcbd38eb26be6a7aa888692d577c56eb758cfcee5c85e17b6cdd0c748ce13
+size 4536