End of training

README.md

@@ -0,0 +1,80 @@
+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv2-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutkv
+  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. -->
+
+# layoutkv
+
+This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6316
+- Answer: {'precision': 0.06269757639620653, 'recall': 0.14709517923362175, 'f1': 0.08792020687107498, 'number': 809}
+- Header: {'precision': 0.02142857142857143, 'recall': 0.025210084033613446, 'f1': 0.023166023166023165, 'number': 119}
+- Question: {'precision': 0.17976470588235294, 'recall': 0.3586854460093897, 'f1': 0.2394984326018809, 'number': 1065}
+- Overall Precision: 0.1211
+- Overall Recall: 0.2529
+- Overall F1: 0.1637
+- Overall Accuracy: 0.3969
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.9011        | 1.0   | 10   | 1.8281          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}                                                       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}                                                 | 0.0               | 0.0            | 0.0        | 0.2901           |
+| 1.7212        | 2.0   | 20   | 1.6755          | {'precision': 0.010714285714285714, 'recall': 0.011124845488257108, 'f1': 0.01091570648878108, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.8, 'recall': 0.003755868544600939, 'f1': 0.007476635514018691, 'number': 1065}               | 0.0154            | 0.0065         | 0.0092     | 0.3484           |
+| 1.6644        | 3.0   | 30   | 1.7453          | {'precision': 0.0030959752321981426, 'recall': 0.0012360939431396785, 'f1': 0.0017667844522968195, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.4895833333333333, 'recall': 0.044131455399061034, 'f1': 0.08096468561584841, 'number': 1065} | 0.1143            | 0.0241         | 0.0398     | 0.3130           |
+| 1.5949        | 4.0   | 40   | 1.7670          | {'precision': 0.03335250143760782, 'recall': 0.07169344870210136, 'f1': 0.04552590266875981, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.13106796116504854, 'recall': 0.2535211267605634, 'f1': 0.1728, 'number': 1065}               | 0.0859            | 0.1646         | 0.1129     | 0.3285           |
+| 1.4559        | 5.0   | 50   | 1.5921          | {'precision': 0.05108940646130729, 'recall': 0.08405438813349815, 'f1': 0.06355140186915888, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.17631224764468373, 'recall': 0.2460093896713615, 'f1': 0.20540964327714623, 'number': 1065}  | 0.1171            | 0.1656         | 0.1372     | 0.3763           |
+| 1.3707        | 6.0   | 60   | 1.6238          | {'precision': 0.049044914816726896, 'recall': 0.11742892459826947, 'f1': 0.06919155134741442, 'number': 809}      | {'precision': 0.006622516556291391, 'recall': 0.008403361344537815, 'f1': 0.007407407407407407, 'number': 119} | {'precision': 0.16497339138848574, 'recall': 0.32018779342723, 'f1': 0.21775223499361432, 'number': 1065}    | 0.1052            | 0.2193         | 0.1422     | 0.3786           |
+| 1.2836        | 7.0   | 70   | 1.5846          | {'precision': 0.058695652173913045, 'recall': 0.1334981458590853, 'f1': 0.08154020385050964, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.15556492411467115, 'recall': 0.3464788732394366, 'f1': 0.2147221414023858, 'number': 1065}   | 0.1120            | 0.2393         | 0.1526     | 0.3851           |
+| 1.2161        | 8.0   | 80   | 1.6814          | {'precision': 0.06025974025974026, 'recall': 0.1433868974042027, 'f1': 0.08485735186539868, 'number': 809}        | {'precision': 0.009433962264150943, 'recall': 0.008403361344537815, 'f1': 0.008888888888888889, 'number': 119} | {'precision': 0.1631912964641886, 'recall': 0.3380281690140845, 'f1': 0.22011617242433507, 'number': 1065}   | 0.1126            | 0.2393         | 0.1531     | 0.3730           |
+| 1.1499        | 9.0   | 90   | 1.6027          | {'precision': 0.06253521126760564, 'recall': 0.13720642768850433, 'f1': 0.08591331269349846, 'number': 809}       | {'precision': 0.011111111111111112, 'recall': 0.008403361344537815, 'f1': 0.009569377990430622, 'number': 119} | {'precision': 0.1547870097005483, 'recall': 0.34460093896713617, 'f1': 0.21362048894062866, 'number': 1065}  | 0.1131            | 0.2403         | 0.1538     | 0.3710           |
+| 1.1199        | 10.0  | 100  | 1.6616          | {'precision': 0.055350553505535055, 'recall': 0.12978986402966625, 'f1': 0.07760532150776053, 'number': 809}      | {'precision': 0.019867549668874173, 'recall': 0.025210084033613446, 'f1': 0.022222222222222223, 'number': 119} | {'precision': 0.16207042851081885, 'recall': 0.3586854460093897, 'f1': 0.22326125073056693, 'number': 1065}  | 0.1112            | 0.2459         | 0.1532     | 0.3719           |
+| 1.0651        | 11.0  | 110  | 1.6100          | {'precision': 0.06031016657093624, 'recall': 0.12978986402966625, 'f1': 0.08235294117647059, 'number': 809}       | {'precision': 0.015151515151515152, 'recall': 0.01680672268907563, 'f1': 0.01593625498007968, 'number': 119}   | {'precision': 0.163854351687389, 'recall': 0.3464788732394366, 'f1': 0.22249020198974978, 'number': 1065}    | 0.1154            | 0.2388         | 0.1556     | 0.3805           |
+| 1.0454        | 12.0  | 120  | 1.5988          | {'precision': 0.0639269406392694, 'recall': 0.138442521631644, 'f1': 0.08746583365872705, 'number': 809}          | {'precision': 0.022222222222222223, 'recall': 0.025210084033613446, 'f1': 0.02362204724409449, 'number': 119}  | {'precision': 0.17867298578199053, 'recall': 0.3539906103286385, 'f1': 0.23748031496062993, 'number': 1065}  | 0.1231            | 0.2469         | 0.1643     | 0.3977           |
+| 1.0279        | 13.0  | 130  | 1.6209          | {'precision': 0.06463104325699745, 'recall': 0.15698393077873918, 'f1': 0.09156452775775054, 'number': 809}       | {'precision': 0.022388059701492536, 'recall': 0.025210084033613446, 'f1': 0.02371541501976284, 'number': 119}  | {'precision': 0.18118811881188118, 'recall': 0.3436619718309859, 'f1': 0.23727714748784443, 'number': 1065}  | 0.1204            | 0.2489         | 0.1623     | 0.3998           |
+| 1.008         | 14.0  | 140  | 1.6538          | {'precision': 0.0633116883116883, 'recall': 0.1446229913473424, 'f1': 0.08806925103500188, 'number': 809}         | {'precision': 0.022556390977443608, 'recall': 0.025210084033613446, 'f1': 0.023809523809523808, 'number': 119} | {'precision': 0.18536350505536833, 'recall': 0.3615023474178404, 'f1': 0.24506683640992996, 'number': 1065}  | 0.1244            | 0.2534         | 0.1669     | 0.3965           |
+| 0.9812        | 15.0  | 150  | 1.6316          | {'precision': 0.06269757639620653, 'recall': 0.14709517923362175, 'f1': 0.08792020687107498, 'number': 809}       | {'precision': 0.02142857142857143, 'recall': 0.025210084033613446, 'f1': 0.023166023166023165, 'number': 119}  | {'precision': 0.17976470588235294, 'recall': 0.3586854460093897, 'f1': 0.2394984326018809, 'number': 1065}   | 0.1211            | 0.2529         | 0.1637     | 0.3969           |
+
+
+### Framework versions
+
+- Transformers 4.32.0
+- Pytorch 2.0.0+cu118
+- Datasets 2.17.1
+- Tokenizers 0.13.2

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 sha256:35cb34339010527d8dfa3f5e4035eb7549eeefe2c362357578e1527c2d046e4a
+oid sha256:0857f9b4dda04a5e00a8771a362581d17958109ca248b550be742aff0b6da5de
 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]"
+}