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: 0.6608
+- Answer: {'precision': 0.7201783723522854, 'recall': 0.7985166872682324, 'f1': 0.7573270808909731, 'number': 809}
+- Header: {'precision': 0.31932773109243695, 'recall': 0.31932773109243695, 'f1': 0.31932773109243695, 'number': 119}
+- Question: {'precision': 0.7643478260869565, 'recall': 0.8253521126760563, 'f1': 0.7936794582392775, 'number': 1065}
+- Overall Precision: 0.7216
+- Overall Recall: 0.7842
+- Overall F1: 0.7516
+- Overall Accuracy: 0.8167
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8317        | 1.0   | 10   | 1.6104          | {'precision': 0.027842227378190254, 'recall': 0.029666254635352288, 'f1': 0.02872531418312388, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.2206047032474804, 'recall': 0.18497652582159624, 'f1': 0.20122574055158324, 'number': 1065} | 0.1259            | 0.1109         | 0.1179     | 0.3482           |
+| 1.4526        | 2.0   | 20   | 1.2629          | {'precision': 0.2147165259348613, 'recall': 0.2200247218788628, 'f1': 0.21733821733821734, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.45054095826893353, 'recall': 0.5474178403755868, 'f1': 0.4942772361169987, 'number': 1065}  | 0.3585            | 0.3818         | 0.3698     | 0.5749           |
+| 1.0991        | 3.0   | 30   | 0.9508          | {'precision': 0.4650856389986825, 'recall': 0.4363411619283066, 'f1': 0.45025510204081637, 'number': 809}     | {'precision': 0.05128205128205128, 'recall': 0.01680672268907563, 'f1': 0.025316455696202535, 'number': 119} | {'precision': 0.6182287188306105, 'recall': 0.6751173708920187, 'f1': 0.6454219030520646, 'number': 1065}   | 0.5477            | 0.5389         | 0.5432     | 0.7030           |
+| 0.8223        | 4.0   | 40   | 0.7675          | {'precision': 0.5823863636363636, 'recall': 0.7601977750309024, 'f1': 0.6595174262734583, 'number': 809}      | {'precision': 0.1774193548387097, 'recall': 0.09243697478991597, 'f1': 0.12154696132596685, 'number': 119}   | {'precision': 0.6633249791144528, 'recall': 0.7455399061032864, 'f1': 0.7020335985853228, 'number': 1065}   | 0.6134            | 0.7125         | 0.6592     | 0.7615           |
+| 0.6605        | 5.0   | 50   | 0.6992          | {'precision': 0.6135662898252826, 'recall': 0.7379480840543882, 'f1': 0.6700336700336701, 'number': 809}      | {'precision': 0.273972602739726, 'recall': 0.16806722689075632, 'f1': 0.20833333333333331, 'number': 119}    | {'precision': 0.7077865266841645, 'recall': 0.7596244131455399, 'f1': 0.7327898550724637, 'number': 1065}   | 0.6514            | 0.7155         | 0.6820     | 0.7834           |
+| 0.5625        | 6.0   | 60   | 0.6647          | {'precision': 0.6484784889821616, 'recall': 0.7639060568603214, 'f1': 0.7014755959137344, 'number': 809}      | {'precision': 0.25, 'recall': 0.24369747899159663, 'f1': 0.24680851063829787, 'number': 119}                 | {'precision': 0.7197032151690025, 'recall': 0.819718309859155, 'f1': 0.7664618086040387, 'number': 1065}    | 0.6661            | 0.7627         | 0.7111     | 0.7955           |
+| 0.4838        | 7.0   | 70   | 0.6497          | {'precision': 0.6606189967982924, 'recall': 0.765142150803461, 'f1': 0.7090492554410079, 'number': 809}       | {'precision': 0.29896907216494845, 'recall': 0.24369747899159663, 'f1': 0.2685185185185185, 'number': 119}   | {'precision': 0.7332775919732442, 'recall': 0.8234741784037559, 'f1': 0.7757629367536488, 'number': 1065}   | 0.6839            | 0.7652         | 0.7222     | 0.8043           |
+| 0.4394        | 8.0   | 80   | 0.6342          | {'precision': 0.6813778256189451, 'recall': 0.7824474660074165, 'f1': 0.7284234752589184, 'number': 809}      | {'precision': 0.30701754385964913, 'recall': 0.29411764705882354, 'f1': 0.30042918454935624, 'number': 119}  | {'precision': 0.7540425531914894, 'recall': 0.831924882629108, 'f1': 0.7910714285714286, 'number': 1065}    | 0.7006            | 0.7797         | 0.7381     | 0.8090           |
+| 0.3871        | 9.0   | 90   | 0.6447          | {'precision': 0.7117516629711752, 'recall': 0.7935723114956736, 'f1': 0.750438340151958, 'number': 809}       | {'precision': 0.35, 'recall': 0.29411764705882354, 'f1': 0.31963470319634707, 'number': 119}                 | {'precision': 0.7660510114335972, 'recall': 0.8178403755868544, 'f1': 0.7910990009082652, 'number': 1065}   | 0.7237            | 0.7767         | 0.7493     | 0.8132           |
+| 0.3503        | 10.0  | 100  | 0.6390          | {'precision': 0.7056892778993435, 'recall': 0.7972805933250927, 'f1': 0.7486941381311665, 'number': 809}      | {'precision': 0.3431372549019608, 'recall': 0.29411764705882354, 'f1': 0.31674208144796384, 'number': 119}   | {'precision': 0.7638888888888888, 'recall': 0.8262910798122066, 'f1': 0.7938655841226885, 'number': 1065}   | 0.7196            | 0.7827         | 0.7498     | 0.8160           |
+| 0.3196        | 11.0  | 110  | 0.6503          | {'precision': 0.7168338907469343, 'recall': 0.7948084054388134, 'f1': 0.753810082063306, 'number': 809}       | {'precision': 0.29464285714285715, 'recall': 0.2773109243697479, 'f1': 0.28571428571428575, 'number': 119}   | {'precision': 0.7765862377122431, 'recall': 0.815962441314554, 'f1': 0.7957875457875458, 'number': 1065}    | 0.7260            | 0.7752         | 0.7498     | 0.8155           |
+| 0.3023        | 12.0  | 120  | 0.6432          | {'precision': 0.7020810514786419, 'recall': 0.792336217552534, 'f1': 0.7444831591173056, 'number': 809}       | {'precision': 0.3181818181818182, 'recall': 0.29411764705882354, 'f1': 0.3056768558951965, 'number': 119}    | {'precision': 0.7600341588385995, 'recall': 0.8356807511737089, 'f1': 0.7960644007155636, 'number': 1065}   | 0.7138            | 0.7858         | 0.7480     | 0.8181           |
+| 0.289         | 13.0  | 130  | 0.6666          | {'precision': 0.7231638418079096, 'recall': 0.7911001236093943, 'f1': 0.755608028335301, 'number': 809}       | {'precision': 0.29838709677419356, 'recall': 0.31092436974789917, 'f1': 0.3045267489711935, 'number': 119}   | {'precision': 0.7837837837837838, 'recall': 0.8169014084507042, 'f1': 0.8, 'number': 1065}                  | 0.7301            | 0.7762         | 0.7524     | 0.8184           |
+| 0.27          | 14.0  | 140  | 0.6599          | {'precision': 0.7224080267558528, 'recall': 0.8009888751545118, 'f1': 0.7596717467760844, 'number': 809}      | {'precision': 0.32456140350877194, 'recall': 0.31092436974789917, 'f1': 0.31759656652360513, 'number': 119}  | {'precision': 0.763840830449827, 'recall': 0.8291079812206573, 'f1': 0.7951373255290409, 'number': 1065}    | 0.7236            | 0.7868         | 0.7538     | 0.8159           |
+| 0.2686        | 15.0  | 150  | 0.6608          | {'precision': 0.7201783723522854, 'recall': 0.7985166872682324, 'f1': 0.7573270808909731, 'number': 809}      | {'precision': 0.31932773109243695, 'recall': 0.31932773109243695, 'f1': 0.31932773109243695, 'number': 119}  | {'precision': 0.7643478260869565, 'recall': 0.8253521126760563, 'f1': 0.7936794582392775, 'number': 1065}   | 0.7216            | 0.7842         | 0.7516     | 0.8167           |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.1

added_tokens.json

@@ -0,0 +1,7 @@
+{
+  "[CLS]": 101,
+  "[MASK]": 103,
+  "[PAD]": 0,
+  "[SEP]": 102,
+  "[UNK]": 100
+}

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:438e7028dbf0a6d0df555b973d9f119aa4b59f152ebb788ba67ab8171e0971e3
+oid sha256:8defa5a31e4c8a441d08eef31fe6883b1ae6f0a8a6effc20069684001eb7df39
 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 +1,80 @@
+{
+  "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
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "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]"
+}