End of training

README.md

@@ -0,0 +1,81 @@
+---
+license: mit
+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: 0.6674
+- Answer: {'precision': 0.6954595791805094, 'recall': 0.7762669962917181, 'f1': 0.7336448598130841, 'number': 809}
+- Header: {'precision': 0.32061068702290074, 'recall': 0.35294117647058826, 'f1': 0.336, 'number': 119}
+- Question: {'precision': 0.7581196581196581, 'recall': 0.8328638497652582, 'f1': 0.7937360178970916, 'number': 1065}
+- Overall Precision: 0.7064
+- Overall Recall: 0.7812
+- Overall F1: 0.7420
+- Overall Accuracy: 0.8062
+
+## 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
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7631        | 1.0   | 10   | 1.5642          | {'precision': 0.020954598370197905, 'recall': 0.022249690976514216, 'f1': 0.02158273381294964, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2592137592137592, 'recall': 0.19812206572769953, 'f1': 0.22458754656732305, 'number': 1065} | 0.1369            | 0.1149         | 0.1249     | 0.3744           |
+| 1.4027        | 2.0   | 20   | 1.2058          | {'precision': 0.2994871794871795, 'recall': 0.36093943139678614, 'f1': 0.32735426008968616, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.43074691805656273, 'recall': 0.5577464788732395, 'f1': 0.48608837970540103, 'number': 1065} | 0.3764            | 0.4446         | 0.4076     | 0.6093           |
+| 1.0483        | 3.0   | 30   | 0.9188          | {'precision': 0.5052854122621564, 'recall': 0.5908529048207664, 'f1': 0.5447293447293446, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5563218390804597, 'recall': 0.6816901408450704, 'f1': 0.6126582278481012, 'number': 1065}   | 0.5325            | 0.6041         | 0.5661     | 0.7192           |
+| 0.8029        | 4.0   | 40   | 0.7734          | {'precision': 0.6087408949011447, 'recall': 0.723114956736712, 'f1': 0.6610169491525424, 'number': 809}       | {'precision': 0.0967741935483871, 'recall': 0.05042016806722689, 'f1': 0.06629834254143646, 'number': 119}  | {'precision': 0.646677471636953, 'recall': 0.7492957746478873, 'f1': 0.6942148760330579, 'number': 1065}    | 0.6154            | 0.6969         | 0.6536     | 0.7601           |
+| 0.6459        | 5.0   | 50   | 0.7058          | {'precision': 0.6804932735426009, 'recall': 0.7503090234857849, 'f1': 0.713697824808936, 'number': 809}       | {'precision': 0.1951219512195122, 'recall': 0.13445378151260504, 'f1': 0.15920398009950248, 'number': 119}  | {'precision': 0.697065820777161, 'recall': 0.8253521126760563, 'f1': 0.7558039552880481, 'number': 1065}    | 0.6720            | 0.7536         | 0.7105     | 0.7922           |
+| 0.5563        | 6.0   | 60   | 0.6929          | {'precision': 0.6706008583690987, 'recall': 0.7725587144622992, 'f1': 0.7179781734635267, 'number': 809}      | {'precision': 0.25287356321839083, 'recall': 0.18487394957983194, 'f1': 0.21359223300970878, 'number': 119} | {'precision': 0.730999146029035, 'recall': 0.8037558685446009, 'f1': 0.7656529516994633, 'number': 1065}    | 0.6863            | 0.7541         | 0.7186     | 0.7852           |
+| 0.4789        | 7.0   | 70   | 0.6587          | {'precision': 0.6681127982646421, 'recall': 0.761433868974042, 'f1': 0.7117273252455227, 'number': 809}       | {'precision': 0.24242424242424243, 'recall': 0.20168067226890757, 'f1': 0.2201834862385321, 'number': 119}  | {'precision': 0.7401837928153717, 'recall': 0.831924882629108, 'f1': 0.7833775419982316, 'number': 1065}    | 0.6880            | 0.7657         | 0.7248     | 0.7977           |
+| 0.4306        | 8.0   | 80   | 0.6571          | {'precision': 0.6606765327695561, 'recall': 0.7725587144622992, 'f1': 0.7122507122507123, 'number': 809}      | {'precision': 0.25, 'recall': 0.25210084033613445, 'f1': 0.2510460251046025, 'number': 119}                 | {'precision': 0.7340513670256835, 'recall': 0.831924882629108, 'f1': 0.7799295774647887, 'number': 1065}    | 0.6780            | 0.7732         | 0.7225     | 0.8007           |
+| 0.3749        | 9.0   | 90   | 0.6571          | {'precision': 0.6827133479212254, 'recall': 0.7713226205191595, 'f1': 0.7243180499129426, 'number': 809}      | {'precision': 0.30434782608695654, 'recall': 0.29411764705882354, 'f1': 0.29914529914529914, 'number': 119} | {'precision': 0.7328370554177006, 'recall': 0.831924882629108, 'f1': 0.7792436235708003, 'number': 1065}    | 0.6903            | 0.7752         | 0.7303     | 0.8022           |
+| 0.3722        | 10.0  | 100  | 0.6581          | {'precision': 0.6774193548387096, 'recall': 0.7787391841779975, 'f1': 0.7245543415756182, 'number': 809}      | {'precision': 0.3, 'recall': 0.3025210084033613, 'f1': 0.301255230125523, 'number': 119}                    | {'precision': 0.7462311557788944, 'recall': 0.8366197183098592, 'f1': 0.7888446215139443, 'number': 1065}   | 0.6939            | 0.7812         | 0.7350     | 0.8036           |
+| 0.3155        | 11.0  | 110  | 0.6649          | {'precision': 0.6891304347826087, 'recall': 0.7836835599505563, 'f1': 0.733371891266628, 'number': 809}       | {'precision': 0.32786885245901637, 'recall': 0.33613445378151263, 'f1': 0.33195020746887965, 'number': 119} | {'precision': 0.7489469250210615, 'recall': 0.8347417840375587, 'f1': 0.7895204262877443, 'number': 1065}   | 0.7012            | 0.7842         | 0.7404     | 0.8070           |
+| 0.2968        | 12.0  | 120  | 0.6687          | {'precision': 0.6901098901098901, 'recall': 0.7762669962917181, 'f1': 0.7306573589296103, 'number': 809}      | {'precision': 0.3106060606060606, 'recall': 0.3445378151260504, 'f1': 0.32669322709163345, 'number': 119}   | {'precision': 0.752129471890971, 'recall': 0.8291079812206573, 'f1': 0.7887449754354622, 'number': 1065}    | 0.7004            | 0.7787         | 0.7375     | 0.8053           |
+| 0.2871        | 13.0  | 130  | 0.6709          | {'precision': 0.6904761904761905, 'recall': 0.788627935723115, 'f1': 0.7362954414310445, 'number': 809}       | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119}  | {'precision': 0.7709790209790209, 'recall': 0.828169014084507, 'f1': 0.7985513807152557, 'number': 1065}    | 0.7121            | 0.7832         | 0.7460     | 0.8055           |
+| 0.2642        | 14.0  | 140  | 0.6679          | {'precision': 0.6991051454138703, 'recall': 0.7725587144622992, 'f1': 0.7339988256018791, 'number': 809}      | {'precision': 0.31851851851851853, 'recall': 0.36134453781512604, 'f1': 0.33858267716535434, 'number': 119} | {'precision': 0.7606112054329371, 'recall': 0.8413145539906103, 'f1': 0.7989300044583147, 'number': 1065}   | 0.7087            | 0.7847         | 0.7448     | 0.8074           |
+| 0.2651        | 15.0  | 150  | 0.6674          | {'precision': 0.6954595791805094, 'recall': 0.7762669962917181, 'f1': 0.7336448598130841, 'number': 809}      | {'precision': 0.32061068702290074, 'recall': 0.35294117647058826, 'f1': 0.336, 'number': 119}               | {'precision': 0.7581196581196581, 'recall': 0.8328638497652582, 'f1': 0.7937360178970916, 'number': 1065}   | 0.7064            | 0.7812         | 0.7420     | 0.8062           |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

logs/events.out.tfevents.1721880625.ab18c22696e9.339.0

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preprocessor_config.json

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

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "rstrip": false,
+    "single_word": false
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+  "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
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+      "lstrip": false,
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+      "rstrip": false,
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+  "cls_token": "[CLS]",
+  "cls_token_box": [
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+  "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": [
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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv2Processor",
+  "sep_token": "[SEP]",
+  "sep_token_box": [
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}