End of training

README.md

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+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-funsd1
+  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-funsd1
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6653
+- Answer: {'precision': 0.6705756929637526, 'recall': 0.7775030902348579, 'f1': 0.7200915855752718, 'number': 809}
+- Header: {'precision': 0.30275229357798167, 'recall': 0.2773109243697479, 'f1': 0.28947368421052627, 'number': 119}
+- Question: {'precision': 0.7173732335827099, 'recall': 0.8103286384976526, 'f1': 0.7610229276895942, 'number': 1065}
+- Overall Precision: 0.6778
+- Overall Recall: 0.7652
+- Overall F1: 0.7188
+- Overall Accuracy: 0.7992
+
+## 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: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                         | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8388        | 1.0   | 10   | 1.6345          | {'precision': 0.010158013544018058, 'recall': 0.011124845488257108, 'f1': 0.010619469026548672, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.12983770287141075, 'recall': 0.09765258215962441, 'f1': 0.11146838156484459, 'number': 1065} | 0.0670            | 0.0567         | 0.0614     | 0.3424           |
+| 1.5101        | 2.0   | 20   | 1.3279          | {'precision': 0.10227272727272728, 'recall': 0.08899876390605686, 'f1': 0.09517514871116987, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.3082191780821918, 'recall': 0.4225352112676056, 'f1': 0.3564356435643564, 'number': 1065}    | 0.2412            | 0.2619         | 0.2511     | 0.5546           |
+| 1.196         | 3.0   | 30   | 1.0812          | {'precision': 0.33375, 'recall': 0.3300370828182942, 'f1': 0.3318831572405221, 'number': 809}                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.4708233413269384, 'recall': 0.5530516431924882, 'f1': 0.5086355785837651, 'number': 1065}    | 0.4153            | 0.4295         | 0.4223     | 0.6283           |
+| 0.957         | 4.0   | 40   | 0.8960          | {'precision': 0.5760082730093071, 'recall': 0.688504326328801, 'f1': 0.6272522522522522, 'number': 809}        | {'precision': 0.027777777777777776, 'recall': 0.008403361344537815, 'f1': 0.012903225806451613, 'number': 119} | {'precision': 0.6268939393939394, 'recall': 0.6215962441314554, 'f1': 0.6242338519566243, 'number': 1065}    | 0.5925            | 0.6121         | 0.6022     | 0.7315           |
+| 0.7609        | 5.0   | 50   | 0.7756          | {'precision': 0.608955223880597, 'recall': 0.7564894932014833, 'f1': 0.6747519294377067, 'number': 809}        | {'precision': 0.11428571428571428, 'recall': 0.06722689075630252, 'f1': 0.08465608465608465, 'number': 119}    | {'precision': 0.6362098138747885, 'recall': 0.7061032863849765, 'f1': 0.669336893635959, 'number': 1065}     | 0.6079            | 0.6884         | 0.6456     | 0.7649           |
+| 0.634         | 6.0   | 60   | 0.7261          | {'precision': 0.6207951070336392, 'recall': 0.7527812113720643, 'f1': 0.6804469273743018, 'number': 809}       | {'precision': 0.24, 'recall': 0.15126050420168066, 'f1': 0.18556701030927833, 'number': 119}                   | {'precision': 0.6666666666666666, 'recall': 0.7380281690140845, 'f1': 0.7005347593582888, 'number': 1065}    | 0.6322            | 0.7090         | 0.6684     | 0.7783           |
+| 0.5815        | 7.0   | 70   | 0.6992          | {'precision': 0.6612377850162866, 'recall': 0.7527812113720643, 'f1': 0.7040462427745664, 'number': 809}       | {'precision': 0.27586206896551724, 'recall': 0.20168067226890757, 'f1': 0.23300970873786409, 'number': 119}    | {'precision': 0.6899841017488076, 'recall': 0.8150234741784037, 'f1': 0.7473095135600517, 'number': 1065}    | 0.6624            | 0.7531         | 0.7049     | 0.7906           |
+| 0.5279        | 8.0   | 80   | 0.6827          | {'precision': 0.6687631027253669, 'recall': 0.788627935723115, 'f1': 0.7237663074305162, 'number': 809}        | {'precision': 0.3010752688172043, 'recall': 0.23529411764705882, 'f1': 0.2641509433962264, 'number': 119}      | {'precision': 0.7285464098073555, 'recall': 0.7812206572769953, 'f1': 0.7539646579066607, 'number': 1065}    | 0.6843            | 0.7516         | 0.7164     | 0.7973           |
+| 0.4907        | 9.0   | 90   | 0.6732          | {'precision': 0.6609442060085837, 'recall': 0.761433868974042, 'f1': 0.707639287765652, 'number': 809}         | {'precision': 0.3148148148148148, 'recall': 0.2857142857142857, 'f1': 0.29955947136563876, 'number': 119}      | {'precision': 0.7145214521452146, 'recall': 0.8131455399061033, 'f1': 0.7606499780412823, 'number': 1065}    | 0.6732            | 0.7607         | 0.7143     | 0.7971           |
+| 0.4734        | 10.0  | 100  | 0.6653          | {'precision': 0.6705756929637526, 'recall': 0.7775030902348579, 'f1': 0.7200915855752718, 'number': 809}       | {'precision': 0.30275229357798167, 'recall': 0.2773109243697479, 'f1': 0.28947368421052627, 'number': 119}     | {'precision': 0.7173732335827099, 'recall': 0.8103286384976526, 'f1': 0.7610229276895942, 'number': 1065}    | 0.6778            | 0.7652         | 0.7188     | 0.7992           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,44 @@
+{
+  "_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-HEADER",
+    "2": "I-HEADER",
+    "3": "B-QUESTION",
+    "4": "I-QUESTION",
+    "5": "B-ANSWER",
+    "6": "I-ANSWER"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-ANSWER": 5,
+    "B-HEADER": 1,
+    "B-QUESTION": 3,
+    "I-ANSWER": 6,
+    "I-HEADER": 2,
+    "I-QUESTION": 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": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

logs/events.out.tfevents.1719878418.HCIDC-SV-DMZ-ORC-NODE02.112661.0

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model.safetensors

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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": true,
+  "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

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+{
+  "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
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+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "special": true
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+      "lstrip": false,
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+      "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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