End of training

README.md

@@ -0,0 +1,82 @@
+---
+library_name: transformers
+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: 0.6896
+- Answer: {'precision': 0.7152245345016429, 'recall': 0.8071693448702101, 'f1': 0.7584204413472706, 'number': 809}
+- Header: {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}
+- Question: {'precision': 0.7833333333333333, 'recall': 0.8384976525821596, 'f1': 0.8099773242630386, 'number': 1065}
+- Overall Precision: 0.7320
+- Overall Recall: 0.7978
+- Overall F1: 0.7635
+- Overall Accuracy: 0.8098
+
+## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7932        | 1.0   | 10   | 1.6151          | {'precision': 0.013916500994035786, 'recall': 0.00865265760197775, 'f1': 0.010670731707317074, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.24816176470588236, 'recall': 0.1267605633802817, 'f1': 0.1678060907395898, 'number': 1065} | 0.1356            | 0.0712         | 0.0934     | 0.3264           |
+| 1.4724        | 2.0   | 20   | 1.2863          | {'precision': 0.11666666666666667, 'recall': 0.1211372064276885, 'f1': 0.11885991510006065, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.3751733703190014, 'recall': 0.507981220657277, 'f1': 0.43159154367770247, 'number': 1065}  | 0.2800            | 0.3206         | 0.2989     | 0.5749           |
+| 1.1346        | 3.0   | 30   | 0.9582          | {'precision': 0.44279176201373, 'recall': 0.4783683559950556, 'f1': 0.45989304812834225, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5849647611589663, 'recall': 0.7014084507042253, 'f1': 0.6379163108454312, 'number': 1065}  | 0.5202            | 0.5690         | 0.5435     | 0.7037           |
+| 0.8689        | 4.0   | 40   | 0.7730          | {'precision': 0.6069182389937107, 'recall': 0.715698393077874, 'f1': 0.6568349404424276, 'number': 809}       | {'precision': 0.15384615384615385, 'recall': 0.06722689075630252, 'f1': 0.0935672514619883, 'number': 119}  | {'precision': 0.6672519754170325, 'recall': 0.7136150234741784, 'f1': 0.6896551724137931, 'number': 1065}  | 0.6280            | 0.6759         | 0.6510     | 0.7590           |
+| 0.6834        | 5.0   | 50   | 0.6983          | {'precision': 0.6349206349206349, 'recall': 0.7416563658838071, 'f1': 0.6841505131128848, 'number': 809}      | {'precision': 0.273972602739726, 'recall': 0.16806722689075632, 'f1': 0.20833333333333331, 'number': 119}   | {'precision': 0.6741214057507987, 'recall': 0.7924882629107981, 'f1': 0.7285282693137678, 'number': 1065}  | 0.6449            | 0.7346         | 0.6868     | 0.7813           |
+| 0.5771        | 6.0   | 60   | 0.6775          | {'precision': 0.6438631790744467, 'recall': 0.7911001236093943, 'f1': 0.7099278979478648, 'number': 809}      | {'precision': 0.3424657534246575, 'recall': 0.21008403361344538, 'f1': 0.2604166666666667, 'number': 119}   | {'precision': 0.7335640138408305, 'recall': 0.7962441314553991, 'f1': 0.7636199909950472, 'number': 1065}  | 0.6806            | 0.7592         | 0.7177     | 0.7871           |
+| 0.5055        | 7.0   | 70   | 0.6602          | {'precision': 0.6920529801324503, 'recall': 0.7750309023485785, 'f1': 0.7311953352769679, 'number': 809}      | {'precision': 0.3069306930693069, 'recall': 0.2605042016806723, 'f1': 0.28181818181818186, 'number': 119}   | {'precision': 0.7590788308237378, 'recall': 0.8046948356807512, 'f1': 0.781221513217867, 'number': 1065}   | 0.7093            | 0.7602         | 0.7338     | 0.7950           |
+| 0.4549        | 8.0   | 80   | 0.6456          | {'precision': 0.6804670912951167, 'recall': 0.792336217552534, 'f1': 0.7321530553969159, 'number': 809}       | {'precision': 0.2831858407079646, 'recall': 0.2689075630252101, 'f1': 0.27586206896551724, 'number': 119}   | {'precision': 0.7497865072587532, 'recall': 0.8244131455399061, 'f1': 0.7853309481216457, 'number': 1065}  | 0.6968            | 0.7782         | 0.7352     | 0.8069           |
+| 0.3945        | 9.0   | 90   | 0.6484          | {'precision': 0.6906552094522019, 'recall': 0.7948084054388134, 'f1': 0.7390804597701149, 'number': 809}      | {'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119}  | {'precision': 0.7660869565217391, 'recall': 0.8272300469483568, 'f1': 0.7954853273137698, 'number': 1065}  | 0.7092            | 0.7832         | 0.7444     | 0.8067           |
+| 0.3887        | 10.0  | 100  | 0.6674          | {'precision': 0.6968085106382979, 'recall': 0.8096415327564895, 'f1': 0.7489994282447112, 'number': 809}      | {'precision': 0.31, 'recall': 0.2605042016806723, 'f1': 0.2831050228310502, 'number': 119}                  | {'precision': 0.790990990990991, 'recall': 0.8244131455399061, 'f1': 0.8073563218390805, 'number': 1065}   | 0.7274            | 0.7847         | 0.7550     | 0.8115           |
+| 0.3299        | 11.0  | 110  | 0.6748          | {'precision': 0.7125550660792952, 'recall': 0.799752781211372, 'f1': 0.7536400698893418, 'number': 809}       | {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}  | {'precision': 0.7663230240549829, 'recall': 0.8375586854460094, 'f1': 0.800358905338717, 'number': 1065}   | 0.7200            | 0.7923         | 0.7544     | 0.8053           |
+| 0.3088        | 12.0  | 120  | 0.6757          | {'precision': 0.7155361050328227, 'recall': 0.8084054388133498, 'f1': 0.759141033081834, 'number': 809}       | {'precision': 0.3904761904761905, 'recall': 0.3445378151260504, 'f1': 0.36607142857142855, 'number': 119}   | {'precision': 0.7783641160949868, 'recall': 0.8309859154929577, 'f1': 0.8038147138964576, 'number': 1065}  | 0.7328            | 0.7928         | 0.7616     | 0.8076           |
+| 0.2922        | 13.0  | 130  | 0.6892          | {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809}      | {'precision': 0.38461538461538464, 'recall': 0.37815126050420167, 'f1': 0.38135593220338987, 'number': 119} | {'precision': 0.7850133809099019, 'recall': 0.8262910798122066, 'f1': 0.8051235132662397, 'number': 1065}  | 0.7332            | 0.7928         | 0.7618     | 0.8076           |
+| 0.2692        | 14.0  | 140  | 0.6906          | {'precision': 0.7212389380530974, 'recall': 0.8059332509270705, 'f1': 0.7612375948628138, 'number': 809}      | {'precision': 0.375, 'recall': 0.37815126050420167, 'f1': 0.37656903765690375, 'number': 119}               | {'precision': 0.7841409691629956, 'recall': 0.8356807511737089, 'f1': 0.8090909090909091, 'number': 1065}  | 0.7351            | 0.7963         | 0.7645     | 0.8087           |
+| 0.2735        | 15.0  | 150  | 0.6896          | {'precision': 0.7152245345016429, 'recall': 0.8071693448702101, 'f1': 0.7584204413472706, 'number': 809}      | {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}    | {'precision': 0.7833333333333333, 'recall': 0.8384976525821596, 'f1': 0.8099773242630386, 'number': 1065}  | 0.7320            | 0.7978         | 0.7635     | 0.8098           |
+
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0

logs/events.out.tfevents.1735195853.c33e6a290e06.456.0

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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
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+  "sep_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "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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+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "normalized": false,
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+  "additional_special_tokens": [],
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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "cls_token_box": [
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+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "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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+    1000,
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}