End of training

README.md

@@ -0,0 +1,79 @@
+---
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-funsd
+  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-funsd
+
+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: 0.7011
+- Answer: {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809}
+- Header: {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}
+- Question: {'precision': 0.7859712230215827, 'recall': 0.8206572769953052, 'f1': 0.8029398254478639, 'number': 1065}
+- Overall Precision: 0.7250
+- Overall Recall: 0.7873
+- Overall F1: 0.7549
+- Overall Accuracy: 0.8102
+
+## 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: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7566        | 1.0   | 10   | 1.5349          | {'precision': 0.03646308113035551, 'recall': 0.049443757725587144, 'f1': 0.04197271773347323, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.16700819672131148, 'recall': 0.15305164319248826, 'f1': 0.15972562469377757, 'number': 1065} | 0.0979            | 0.1019         | 0.0999     | 0.4336           |
+| 1.4057        | 2.0   | 20   | 1.1865          | {'precision': 0.17656500802568217, 'recall': 0.13597033374536466, 'f1': 0.15363128491620115, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.471847739888977, 'recall': 0.5586854460093896, 'f1': 0.5116079105760963, 'number': 1065}     | 0.3742            | 0.3537         | 0.3637     | 0.6016           |
+| 1.0729        | 3.0   | 30   | 0.9241          | {'precision': 0.49693251533742333, 'recall': 0.5006180469715699, 'f1': 0.4987684729064039, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.6378708551483421, 'recall': 0.6863849765258216, 'f1': 0.6612392582541836, 'number': 1065}    | 0.5691            | 0.5700         | 0.5696     | 0.7181           |
+| 0.8134        | 4.0   | 40   | 0.7831          | {'precision': 0.6211640211640211, 'recall': 0.7255871446229913, 'f1': 0.669327251995439, 'number': 809}      | {'precision': 0.09375, 'recall': 0.05042016806722689, 'f1': 0.0655737704918033, 'number': 119}              | {'precision': 0.6889081455805892, 'recall': 0.7464788732394366, 'f1': 0.7165389815232085, 'number': 1065}    | 0.6417            | 0.6964         | 0.6679     | 0.7640           |
+| 0.6582        | 5.0   | 50   | 0.7298          | {'precision': 0.6422018348623854, 'recall': 0.7787391841779975, 'f1': 0.7039106145251396, 'number': 809}     | {'precision': 0.2361111111111111, 'recall': 0.14285714285714285, 'f1': 0.17801047120418848, 'number': 119}  | {'precision': 0.7311233885819521, 'recall': 0.7455399061032864, 'f1': 0.7382612738261274, 'number': 1065}    | 0.6737            | 0.7230         | 0.6975     | 0.7761           |
+| 0.553         | 6.0   | 60   | 0.6763          | {'precision': 0.6673532440782698, 'recall': 0.8009888751545118, 'f1': 0.7280898876404494, 'number': 809}     | {'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119} | {'precision': 0.735445205479452, 'recall': 0.8065727699530516, 'f1': 0.7693685624720108, 'number': 1065}     | 0.6859            | 0.7682         | 0.7247     | 0.7962           |
+| 0.4805        | 7.0   | 70   | 0.6797          | {'precision': 0.6904255319148936, 'recall': 0.8022249690976514, 'f1': 0.7421383647798742, 'number': 809}     | {'precision': 0.25925925925925924, 'recall': 0.23529411764705882, 'f1': 0.24669603524229072, 'number': 119} | {'precision': 0.7363945578231292, 'recall': 0.8131455399061033, 'f1': 0.7728692547969657, 'number': 1065}    | 0.6938            | 0.7742         | 0.7318     | 0.7970           |
+| 0.4259        | 8.0   | 80   | 0.6726          | {'precision': 0.689401888772298, 'recall': 0.8121137206427689, 'f1': 0.7457434733257663, 'number': 809}      | {'precision': 0.24786324786324787, 'recall': 0.24369747899159663, 'f1': 0.24576271186440676, 'number': 119} | {'precision': 0.7463581833761782, 'recall': 0.8178403755868544, 'f1': 0.7804659498207885, 'number': 1065}    | 0.6960            | 0.7812         | 0.7362     | 0.8020           |
+| 0.3787        | 9.0   | 90   | 0.6784          | {'precision': 0.7043956043956044, 'recall': 0.792336217552534, 'f1': 0.7457824316463061, 'number': 809}      | {'precision': 0.26229508196721313, 'recall': 0.2689075630252101, 'f1': 0.26556016597510373, 'number': 119}  | {'precision': 0.779707495429616, 'recall': 0.8009389671361502, 'f1': 0.7901806391848076, 'number': 1065}     | 0.7178            | 0.7657         | 0.7410     | 0.8026           |
+| 0.3411        | 10.0  | 100  | 0.6821          | {'precision': 0.7015086206896551, 'recall': 0.8046971569839307, 'f1': 0.7495682210708117, 'number': 809}     | {'precision': 0.2708333333333333, 'recall': 0.3277310924369748, 'f1': 0.2965779467680608, 'number': 119}    | {'precision': 0.775200713648528, 'recall': 0.815962441314554, 'f1': 0.7950594693504116, 'number': 1065}      | 0.7109            | 0.7822         | 0.7449     | 0.8047           |
+| 0.313         | 11.0  | 110  | 0.7129          | {'precision': 0.7111111111111111, 'recall': 0.7911001236093943, 'f1': 0.7489760093622002, 'number': 809}     | {'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119}  | {'precision': 0.7816711590296496, 'recall': 0.8169014084507042, 'f1': 0.7988980716253444, 'number': 1065}    | 0.7210            | 0.7767         | 0.7478     | 0.7994           |
+| 0.297         | 12.0  | 120  | 0.6955          | {'precision': 0.708779443254818, 'recall': 0.8182941903584673, 'f1': 0.759609868043603, 'number': 809}       | {'precision': 0.291044776119403, 'recall': 0.3277310924369748, 'f1': 0.308300395256917, 'number': 119}      | {'precision': 0.783978397839784, 'recall': 0.8178403755868544, 'f1': 0.8005514705882352, 'number': 1065}     | 0.7214            | 0.7888         | 0.7536     | 0.8103           |
+| 0.2907        | 13.0  | 130  | 0.7098          | {'precision': 0.7092511013215859, 'recall': 0.796044499381953, 'f1': 0.7501456027955737, 'number': 809}      | {'precision': 0.3142857142857143, 'recall': 0.3697478991596639, 'f1': 0.33976833976833976, 'number': 119}   | {'precision': 0.7896678966789668, 'recall': 0.8037558685446009, 'f1': 0.796649604467194, 'number': 1065}     | 0.7242            | 0.7747         | 0.7486     | 0.8052           |
+| 0.2701        | 14.0  | 140  | 0.7006          | {'precision': 0.7133479212253829, 'recall': 0.8059332509270705, 'f1': 0.7568195008705745, 'number': 809}     | {'precision': 0.3037037037037037, 'recall': 0.3445378151260504, 'f1': 0.3228346456692913, 'number': 119}    | {'precision': 0.7894736842105263, 'recall': 0.8169014084507042, 'f1': 0.8029533917858791, 'number': 1065}    | 0.7266            | 0.7842         | 0.7543     | 0.8091           |
+| 0.2649        | 15.0  | 150  | 0.7011          | {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809}     | {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}  | {'precision': 0.7859712230215827, 'recall': 0.8206572769953052, 'f1': 0.8029398254478639, 'number': 1065}    | 0.7250            | 0.7873         | 0.7549     | 0.8102           |
+
+
+### Framework versions
+
+- Transformers 4.32.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3

preprocessor_config.json

@@ -0,0 +1,14 @@
+{
+  "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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+oid sha256:073ece5b4990116f91de6ced14685a8bb00af0f17fa77679c8cfcf015309bf93
 size 450603969

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,38 @@
+{
+  "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]"
+}