Hugging Face's logo

fabgei
/
layoutlm-funsd

Token Classification
Transformers
PyTorch
TensorBoard
layoutlm
Generated from Trainer
Model card Files
xet
Metrics Community
1
fabgei commited on
Commit
e38d8a7
·
1 Parent(s): 909ebdd

End of training

Browse files
Files changed (5) hide show
  1. README.md +27 -27
  2. logs/events.out.tfevents.1685356755.b99a785204dd.35295.0 +2 -2
  3. pytorch_model.bin +1 -1
  4. tokenizer.json +16 -2
  5. tokenizer_config.json +1 -1
README.md CHANGED
@@ -15,14 +15,14 @@ should probably proofread and complete it, then remove this comment. -->
15
 
16
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
17
  It achieves the following results on the evaluation set:
18
- - Loss: 0.7049
19
- - Answer: {'precision': 0.7178051511758119, 'recall': 0.792336217552534, 'f1': 0.7532314923619271, 'number': 809}
20
- - Header: {'precision': 0.2803030303030303, 'recall': 0.31092436974789917, 'f1': 0.29482071713147406, 'number': 119}
21
- - Question: {'precision': 0.7570815450643776, 'recall': 0.828169014084507, 'f1': 0.7910313901345292, 'number': 1065}
22
- - Overall Precision: 0.7123
23
- - Overall Recall: 0.7827
24
- - Overall F1: 0.7459
25
- - Overall Accuracy: 0.8046
26
 
27
  ## Model description
28
 
@@ -42,8 +42,8 @@ More information needed
42
 
43
  The following hyperparameters were used during training:
44
  - learning_rate: 3e-05
45
- - train_batch_size: 16
46
- - eval_batch_size: 8
47
  - seed: 42
48
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
49
  - lr_scheduler_type: linear
@@ -52,23 +52,23 @@ The following hyperparameters were used during training:
52
 
53
  ### Training results
54
 
55
- | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
56
- |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
57
- | 1.747 | 1.0 | 10 | 1.5873 | {'precision': 0.018205461638491547, 'recall': 0.0173053152039555, 'f1': 0.01774397972116603, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.19186046511627908, 'recall': 0.15492957746478872, 'f1': 0.17142857142857143, 'number': 1065} | 0.1099 | 0.0898 | 0.0988 | 0.3629 |
58
- | 1.4357 | 2.0 | 20 | 1.2348 | {'precision': 0.2678787878787879, 'recall': 0.273176761433869, 'f1': 0.27050183598531213, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4725705329153605, 'recall': 0.5661971830985916, 'f1': 0.5151644596326356, 'number': 1065} | 0.3922 | 0.4134 | 0.4025 | 0.5730 |
59
- | 1.0578 | 3.0 | 30 | 0.9336 | {'precision': 0.4918864097363083, 'recall': 0.5995055624227441, 'f1': 0.5403899721448469, 'number': 809} | {'precision': 0.027777777777777776, 'recall': 0.008403361344537815, 'f1': 0.012903225806451613, 'number': 119} | {'precision': 0.6016260162601627, 'recall': 0.6948356807511737, 'f1': 0.644880174291939, 'number': 1065} | 0.5444 | 0.6152 | 0.5776 | 0.7071 |
60
- | 0.814 | 4.0 | 40 | 0.7665 | {'precision': 0.5914935707220573, 'recall': 0.7391841779975278, 'f1': 0.6571428571428571, 'number': 809} | {'precision': 0.14084507042253522, 'recall': 0.08403361344537816, 'f1': 0.10526315789473685, 'number': 119} | {'precision': 0.6415562913907285, 'recall': 0.7276995305164319, 'f1': 0.6819181698196215, 'number': 1065} | 0.6039 | 0.6939 | 0.6458 | 0.7567 |
61
- | 0.6755 | 5.0 | 50 | 0.7183 | {'precision': 0.6652221018418202, 'recall': 0.7589616810877626, 'f1': 0.7090069284064666, 'number': 809} | {'precision': 0.2692307692307692, 'recall': 0.23529411764705882, 'f1': 0.25112107623318386, 'number': 119} | {'precision': 0.7006039689387403, 'recall': 0.7624413145539906, 'f1': 0.7302158273381295, 'number': 1065} | 0.6651 | 0.7296 | 0.6959 | 0.7844 |
62
- | 0.5514 | 6.0 | 60 | 0.6832 | {'precision': 0.6699134199134199, 'recall': 0.765142150803461, 'f1': 0.7143681477207153, 'number': 809} | {'precision': 0.2625, 'recall': 0.17647058823529413, 'f1': 0.21105527638190955, 'number': 119} | {'precision': 0.7032, 'recall': 0.8253521126760563, 'f1': 0.7593952483801296, 'number': 1065} | 0.6739 | 0.7622 | 0.7153 | 0.7894 |
63
- | 0.4846 | 7.0 | 70 | 0.6668 | {'precision': 0.6698513800424628, 'recall': 0.7799752781211372, 'f1': 0.7207310108509423, 'number': 809} | {'precision': 0.2773109243697479, 'recall': 0.2773109243697479, 'f1': 0.2773109243697479, 'number': 119} | {'precision': 0.719932716568545, 'recall': 0.8037558685446009, 'f1': 0.7595385980479148, 'number': 1065} | 0.6756 | 0.7627 | 0.7165 | 0.7922 |
64
- | 0.4323 | 8.0 | 80 | 0.6610 | {'precision': 0.6934065934065934, 'recall': 0.7799752781211372, 'f1': 0.7341477603257708, 'number': 809} | {'precision': 0.3113207547169811, 'recall': 0.2773109243697479, 'f1': 0.2933333333333334, 'number': 119} | {'precision': 0.7377892030848329, 'recall': 0.8084507042253521, 'f1': 0.771505376344086, 'number': 1065} | 0.6986 | 0.7652 | 0.7304 | 0.7999 |
65
- | 0.3889 | 9.0 | 90 | 0.6681 | {'precision': 0.7054871220604704, 'recall': 0.7787391841779975, 'f1': 0.7403055229142185, 'number': 809} | {'precision': 0.2975206611570248, 'recall': 0.3025210084033613, 'f1': 0.3, 'number': 119} | {'precision': 0.7395309882747069, 'recall': 0.8291079812206573, 'f1': 0.7817618415227978, 'number': 1065} | 0.7015 | 0.7772 | 0.7374 | 0.8046 |
66
- | 0.3514 | 10.0 | 100 | 0.6881 | {'precision': 0.7018909899888766, 'recall': 0.7799752781211372, 'f1': 0.7388758782201406, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.3025210084033613, 'f1': 0.2938775510204082, 'number': 119} | {'precision': 0.7391304347826086, 'recall': 0.8300469483568075, 'f1': 0.7819548872180452, 'number': 1065} | 0.6983 | 0.7782 | 0.7361 | 0.7977 |
67
- | 0.3179 | 11.0 | 110 | 0.6895 | {'precision': 0.6925566343042071, 'recall': 0.7935723114956736, 'f1': 0.73963133640553, 'number': 809} | {'precision': 0.3185840707964602, 'recall': 0.3025210084033613, 'f1': 0.3103448275862069, 'number': 119} | {'precision': 0.7578125, 'recall': 0.819718309859155, 'f1': 0.7875507442489851, 'number': 1065} | 0.7076 | 0.7782 | 0.7412 | 0.8011 |
68
- | 0.3008 | 12.0 | 120 | 0.6971 | {'precision': 0.7183257918552036, 'recall': 0.7849196538936959, 'f1': 0.7501476668635558, 'number': 809} | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119} | {'precision': 0.7574978577549272, 'recall': 0.8300469483568075, 'f1': 0.7921146953405018, 'number': 1065} | 0.7139 | 0.7812 | 0.7460 | 0.8036 |
69
- | 0.2844 | 13.0 | 130 | 0.7024 | {'precision': 0.710352422907489, 'recall': 0.7972805933250927, 'f1': 0.751310425160163, 'number': 809} | {'precision': 0.2868217054263566, 'recall': 0.31092436974789917, 'f1': 0.2983870967741935, 'number': 119} | {'precision': 0.7601031814273431, 'recall': 0.8300469483568075, 'f1': 0.7935368043087971, 'number': 1065} | 0.7118 | 0.7858 | 0.7470 | 0.8027 |
70
- | 0.2735 | 14.0 | 140 | 0.7061 | {'precision': 0.7136514983351832, 'recall': 0.7948084054388134, 'f1': 0.752046783625731, 'number': 809} | {'precision': 0.2824427480916031, 'recall': 0.31092436974789917, 'f1': 0.29600000000000004, 'number': 119} | {'precision': 0.7575236457437661, 'recall': 0.8272300469483568, 'f1': 0.7908438061041293, 'number': 1065} | 0.7112 | 0.7832 | 0.7455 | 0.8040 |
71
- | 0.2676 | 15.0 | 150 | 0.7049 | {'precision': 0.7178051511758119, 'recall': 0.792336217552534, 'f1': 0.7532314923619271, 'number': 809} | {'precision': 0.2803030303030303, 'recall': 0.31092436974789917, 'f1': 0.29482071713147406, 'number': 119} | {'precision': 0.7570815450643776, 'recall': 0.828169014084507, 'f1': 0.7910313901345292, 'number': 1065} | 0.7123 | 0.7827 | 0.7459 | 0.8046 |
72
 
73
 
74
  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 1.7205
19
+ - Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}
20
+ - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
21
+ - Question: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1070}
22
+ - Overall Precision: 0.0
23
+ - Overall Recall: 0.0
24
+ - Overall F1: 0.0
25
+ - Overall Accuracy: 0.2854
26
 
27
  ## Model description
28
 
 
42
 
43
  The following hyperparameters were used during training:
44
  - learning_rate: 3e-05
45
+ - train_batch_size: 8
46
+ - eval_batch_size: 4
47
  - seed: 42
48
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
49
  - lr_scheduler_type: linear
 
52
 
53
  ### Training results
54
 
55
+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
56
+ |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
57
+ | 1.8104 | 1.0 | 19 | 1.7227 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
58
+ | 1.7658 | 2.0 | 38 | 1.7254 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
59
+ | 1.7511 | 3.0 | 57 | 1.7137 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
60
+ | 1.7532 | 4.0 | 76 | 1.7184 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
61
+ | 1.7589 | 5.0 | 95 | 1.7141 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
62
+ | 1.748 | 6.0 | 114 | 1.7016 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
63
+ | 1.7487 | 7.0 | 133 | 1.7239 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
64
+ | 1.7483 | 8.0 | 152 | 1.7207 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
65
+ | 1.7465 | 9.0 | 171 | 1.7119 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
66
+ | 1.7458 | 10.0 | 190 | 1.7169 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
67
+ | 1.7419 | 11.0 | 209 | 1.7125 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
68
+ | 1.7425 | 12.0 | 228 | 1.7218 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
69
+ | 1.7424 | 13.0 | 247 | 1.7250 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
70
+ | 1.7412 | 14.0 | 266 | 1.7232 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
71
+ | 1.7389 | 15.0 | 285 | 1.7205 | {'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': 1070} | 0.0 | 0.0 | 0.0 | 0.2854 |
72
 
73
 
74
  ### Framework versions
logs/events.out.tfevents.1685356755.b99a785204dd.35295.0 CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:8f5656d4437fcc3fd635018cb83768afa4662bff22ed17437e4624348ca78dd5
3
- size 12768
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c84b04dbf822c3b21f8f948e1e8daa7dbb8bac127bdb0ba9a56849a353e45a87
3
+ size 14444
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:873f9b6fa0d98c61175df99fe72da0767c79828a057972fb0908aa0c6a67f89e
3
  size 452185349
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fcc11ca585021c8c341a6fa73d00adec1c01f5648ac209053c7bd703593eb521
3
  size 452185349
tokenizer.json CHANGED
@@ -1,7 +1,21 @@
1
  {
2
  "version": "1.0",
3
- "truncation": null,
4
- "padding": null,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5
  "added_tokens": [
6
  {
7
  "id": 0,
 
1
  {
2
  "version": "1.0",
3
+ "truncation": {
4
+ "direction": "Right",
5
+ "max_length": 1024,
6
+ "strategy": "LongestFirst",
7
+ "stride": 0
8
+ },
9
+ "padding": {
10
+ "strategy": {
11
+ "Fixed": 1024
12
+ },
13
+ "direction": "Right",
14
+ "pad_to_multiple_of": null,
15
+ "pad_id": 0,
16
+ "pad_type_id": 0,
17
+ "pad_token": "[PAD]"
18
+ },
19
  "added_tokens": [
20
  {
21
  "id": 0,
tokenizer_config.json CHANGED
@@ -12,7 +12,7 @@
12
  "do_basic_tokenize": true,
13
  "do_lower_case": true,
14
  "mask_token": "[MASK]",
15
- "model_max_length": 512,
16
  "never_split": null,
17
  "only_label_first_subword": true,
18
  "pad_token": "[PAD]",
 
12
  "do_basic_tokenize": true,
13
  "do_lower_case": true,
14
  "mask_token": "[MASK]",
15
+ "model_max_length": 1024,
16
  "never_split": null,
17
  "only_label_first_subword": true,
18
  "pad_token": "[PAD]",