Hugging Face's logo

dwitidibyajyoti
/
layoutlm-funsd

Token Classification
Transformers
PyTorch
layoutlm
Generated from Trainer
Inference Endpoints
Model card Files Community
dwitidibyajyoti commited on
Commit
949463b
•
1 Parent(s): bb76f7d

dwitidibyajyoti/layoutmlv2_test_67

Browse files
Files changed (4) hide show
  1. README.md +78 -0
  2. config.json +58 -0
  3. pytorch_model.bin +3 -0
  4. training_args.bin +3 -0
README.md ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: microsoft/layoutlm-base-uncased
3
+ tags:
4
+ - generated_from_trainer
5
+ model-index:
6
+ - name: layoutlm-funsd
7
+ results: []
8
+ ---
9
+
10
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
+ should probably proofread and complete it, then remove this comment. -->
12
+
13
+ # layoutlm-funsd
14
+
15
+ This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
16
+ It achieves the following results on the evaluation set:
17
+ - Loss: 0.8927
18
+ - Column: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}
19
+ - Ignore: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
20
+ - Key: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}
21
+ - Value: {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33}
22
+ - Overall Precision: 0.6875
23
+ - Overall Recall: 0.4231
24
+ - Overall F1: 0.5238
25
+ - Overall Accuracy: 0.7947
26
+
27
+ ## Model description
28
+
29
+ More information needed
30
+
31
+ ## Intended uses & limitations
32
+
33
+ More information needed
34
+
35
+ ## Training and evaluation data
36
+
37
+ More information needed
38
+
39
+ ## Training procedure
40
+
41
+ ### Training hyperparameters
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
50
+ - num_epochs: 15
51
+
52
+ ### Training results
53
+
54
+ | Training Loss | Epoch | Step | Validation Loss | Column | Ignore | Key | Value | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
55
+ |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
56
+ | 2.4627 | 1.0 | 2 | 2.1288 | {'precision': 0.23529411764705882, 'recall': 0.16, 'f1': 0.19047619047619052, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.06060606060606061, 'recall': 0.06060606060606061, 'f1': 0.06060606060606061, 'number': 33} | 0.0870 | 0.0769 | 0.0816 | 0.6887 |
57
+ | 2.1025 | 2.0 | 4 | 1.7650 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} | 0.0 | 0.0 | 0.0 | 0.6921 |
58
+ | 1.7503 | 3.0 | 6 | 1.4611 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} | 0.0 | 0.0 | 0.0 | 0.6904 |
59
+ | 1.4557 | 4.0 | 8 | 1.2624 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} | 0.0 | 0.0 | 0.0 | 0.6904 |
60
+ | 1.3067 | 5.0 | 10 | 1.1889 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} | 0.0 | 0.0 | 0.0 | 0.6904 |
61
+ | 1.1884 | 6.0 | 12 | 1.1436 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} | 0.0 | 0.0 | 0.0 | 0.6904 |
62
+ | 1.1456 | 7.0 | 14 | 1.0901 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} | 0.0 | 0.0 | 0.0 | 0.6904 |
63
+ | 1.0915 | 8.0 | 16 | 1.0410 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 0.11764705882352941, 'f1': 0.21052631578947367, 'number': 17} | {'precision': 0.3333333333333333, 'recall': 0.030303030303030304, 'f1': 0.05555555555555555, 'number': 33} | 0.6 | 0.0385 | 0.0723 | 0.6937 |
64
+ | 1.0428 | 9.0 | 18 | 0.9990 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 0.29411764705882354, 'f1': 0.45454545454545453, 'number': 17} | {'precision': 0.23529411764705882, 'recall': 0.12121212121212122, 'f1': 0.16, 'number': 33} | 0.2727 | 0.1154 | 0.1622 | 0.7252 |
65
+ | 0.9819 | 10.0 | 20 | 0.9639 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 0.4117647058823529, 'f1': 0.5833333333333334, 'number': 17} | {'precision': 0.2631578947368421, 'recall': 0.15151515151515152, 'f1': 0.19230769230769232, 'number': 33} | 0.3243 | 0.1538 | 0.2087 | 0.7517 |
66
+ | 0.9592 | 11.0 | 22 | 0.9344 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 0.6470588235294118, 'f1': 0.7857142857142858, 'number': 17} | {'precision': 0.3684210526315789, 'recall': 0.21212121212121213, 'f1': 0.2692307692307693, 'number': 33} | 0.4737 | 0.2308 | 0.3103 | 0.7781 |
67
+ | 0.9011 | 12.0 | 24 | 0.9105 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17} | {'precision': 0.64, 'recall': 0.48484848484848486, 'f1': 0.5517241379310344, 'number': 33} | 0.66 | 0.4231 | 0.5156 | 0.7930 |
68
+ | 0.9426 | 13.0 | 26 | 0.8927 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17} | {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33} | 0.6875 | 0.4231 | 0.5238 | 0.7947 |
69
+ | 0.8809 | 14.0 | 28 | 0.8821 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17} | {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33} | 0.6875 | 0.4231 | 0.5238 | 0.7947 |
70
+ | 0.9188 | 15.0 | 30 | 0.8774 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17} | {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33} | 0.6875 | 0.4231 | 0.5238 | 0.7947 |
71
+
72
+
73
+ ### Framework versions
74
+
75
+ - Transformers 4.32.1
76
+ - Pytorch 2.0.1+cu118
77
+ - Datasets 2.14.4
78
+ - Tokenizers 0.13.3
config.json ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "microsoft/layoutlm-base-uncased",
3
+ "architectures": [
4
+ "LayoutLMForTokenClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "hidden_act": "gelu",
8
+ "hidden_dropout_prob": 0.1,
9
+ "hidden_size": 768,
10
+ "id2label": {
11
+ "0": "S-VALUE",
12
+ "1": "I-VALUE",
13
+ "2": "E-IGNORE",
14
+ "3": "S-KEY",
15
+ "4": "E-COLUMN",
16
+ "5": "B-IGNORE",
17
+ "6": "S-COLUMN",
18
+ "7": "I-COLUMN",
19
+ "8": "O",
20
+ "9": "E-VALUE",
21
+ "10": "B-COLUMN",
22
+ "11": "S-IGNORE",
23
+ "12": "I-IGNORE",
24
+ "13": "B-VALUE"
25
+ },
26
+ "initializer_range": 0.02,
27
+ "intermediate_size": 3072,
28
+ "label2id": {
29
+ "B-COLUMN": 10,
30
+ "B-IGNORE": 5,
31
+ "B-VALUE": 13,
32
+ "E-COLUMN": 4,
33
+ "E-IGNORE": 2,
34
+ "E-VALUE": 9,
35
+ "I-COLUMN": 7,
36
+ "I-IGNORE": 12,
37
+ "I-VALUE": 1,
38
+ "O": 8,
39
+ "S-COLUMN": 6,
40
+ "S-IGNORE": 11,
41
+ "S-KEY": 3,
42
+ "S-VALUE": 0
43
+ },
44
+ "layer_norm_eps": 1e-12,
45
+ "max_2d_position_embeddings": 1024,
46
+ "max_position_embeddings": 512,
47
+ "model_type": "layoutlm",
48
+ "num_attention_heads": 12,
49
+ "num_hidden_layers": 12,
50
+ "output_past": true,
51
+ "pad_token_id": 0,
52
+ "position_embedding_type": "absolute",
53
+ "torch_dtype": "float32",
54
+ "transformers_version": "4.32.1",
55
+ "type_vocab_size": 2,
56
+ "use_cache": true,
57
+ "vocab_size": 30522
58
+ }
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2352db3198e37db8a0fdb5b48c3298de173a728a360eb75bd091a690703b5ddc
3
+ size 450625473
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1a5f8bbc581939b48341cf7d6d9d40338dad72bad41bd7c001758fbba35697a1
3
+ size 4027