Hugging Face's logo

Jyotiyadav
/
model10.0

Token Classification
Transformers
TensorBoard
Safetensors
layoutlmv3
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
Jyotiyadav commited on
Commit
98c2972
•
1 Parent(s): 3bfdb3d

Model save

Browse files
Files changed (3) hide show
  1. README.md +189 -0
  2. model.safetensors +1 -1
  3. runs/May15_08-38-33_a17b87ecd093/events.out.tfevents.1715771437.a17b87ecd093.18908.1 +3 -0
README.md ADDED
@@ -0,0 +1,189 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-nc-sa-4.0
3
+ base_model: microsoft/layoutlmv3-base
4
+ tags:
5
+ - generated_from_trainer
6
+ datasets:
7
+ - datasetprepfrom_gcp
8
+ metrics:
9
+ - precision
10
+ - recall
11
+ - f1
12
+ - accuracy
13
+ model-index:
14
+ - name: model10.0
15
+ results:
16
+ - task:
17
+ name: Token Classification
18
+ type: token-classification
19
+ dataset:
20
+ name: datasetprepfrom_gcp
21
+ type: datasetprepfrom_gcp
22
+ config: discharge
23
+ split: test
24
+ args: discharge
25
+ metrics:
26
+ - name: Precision
27
+ type: precision
28
+ value: 0.7824019024970273
29
+ - name: Recall
30
+ type: recall
31
+ value: 0.7396028475084301
32
+ - name: F1
33
+ type: f1
34
+ value: 0.7604006163328197
35
+ - name: Accuracy
36
+ type: accuracy
37
+ value: 0.9480585417540451
38
+ ---
39
+
40
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
41
+ should probably proofread and complete it, then remove this comment. -->
42
+
43
+ # model10.0
44
+
45
+ This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the datasetprepfrom_gcp dataset.
46
+ It achieves the following results on the evaluation set:
47
+ - Loss: 0.3117
48
+ - Precision: 0.7824
49
+ - Recall: 0.7396
50
+ - F1: 0.7604
51
+ - Accuracy: 0.9481
52
+
53
+ ## Model description
54
+
55
+ More information needed
56
+
57
+ ## Intended uses & limitations
58
+
59
+ More information needed
60
+
61
+ ## Training and evaluation data
62
+
63
+ More information needed
64
+
65
+ ## Training procedure
66
+
67
+ ### Training hyperparameters
68
+
69
+ The following hyperparameters were used during training:
70
+ - learning_rate: 1e-05
71
+ - train_batch_size: 2
72
+ - eval_batch_size: 2
73
+ - seed: 42
74
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
75
+ - lr_scheduler_type: linear
76
+ - training_steps: 10000
77
+
78
+ ### Training results
79
+
80
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
81
+ |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
82
+ | No log | 0.1094 | 100 | 1.1070 | 0.0820 | 0.0037 | 0.0072 | 0.8202 |
83
+ | No log | 0.2188 | 200 | 0.9957 | 0.0449 | 0.0049 | 0.0088 | 0.8244 |
84
+ | No log | 0.3282 | 300 | 0.9355 | 0.1949 | 0.0590 | 0.0906 | 0.8338 |
85
+ | No log | 0.4376 | 400 | 0.8255 | 0.2373 | 0.1223 | 0.1614 | 0.8453 |
86
+ | 1.0172 | 0.5470 | 500 | 0.7511 | 0.3185 | 0.1510 | 0.2049 | 0.8489 |
87
+ | 1.0172 | 0.6565 | 600 | 0.7203 | 0.3609 | 0.1997 | 0.2571 | 0.8510 |
88
+ | 1.0172 | 0.7659 | 700 | 0.6571 | 0.4256 | 0.2565 | 0.3200 | 0.8755 |
89
+ | 1.0172 | 0.8753 | 800 | 0.5845 | 0.4510 | 0.3127 | 0.3693 | 0.8873 |
90
+ | 1.0172 | 0.9847 | 900 | 0.5614 | 0.5053 | 0.3477 | 0.4119 | 0.8948 |
91
+ | 0.45 | 1.0941 | 1000 | 0.5126 | 0.5561 | 0.3818 | 0.4527 | 0.9042 |
92
+ | 0.45 | 1.2035 | 1100 | 0.5350 | 0.5790 | 0.4176 | 0.4852 | 0.9092 |
93
+ | 0.45 | 1.3129 | 1200 | 0.4846 | 0.5449 | 0.4562 | 0.4966 | 0.9068 |
94
+ | 0.45 | 1.4223 | 1300 | 0.4681 | 0.6153 | 0.4843 | 0.5420 | 0.9149 |
95
+ | 0.45 | 1.5317 | 1400 | 0.4807 | 0.6572 | 0.4874 | 0.5598 | 0.9164 |
96
+ | 0.2388 | 1.6411 | 1500 | 0.4096 | 0.5848 | 0.5264 | 0.5541 | 0.9146 |
97
+ | 0.2388 | 1.7505 | 1600 | 0.3991 | 0.6344 | 0.5474 | 0.5877 | 0.9203 |
98
+ | 0.2388 | 1.8600 | 1700 | 0.4044 | 0.5996 | 0.5691 | 0.5840 | 0.9185 |
99
+ | 0.2388 | 1.9694 | 1800 | 0.4065 | 0.6607 | 0.5869 | 0.6216 | 0.9258 |
100
+ | 0.2388 | 2.0788 | 1900 | 0.4159 | 0.6018 | 0.5802 | 0.5908 | 0.9123 |
101
+ | 0.1768 | 2.1882 | 2000 | 0.4203 | 0.6846 | 0.5822 | 0.6293 | 0.9268 |
102
+ | 0.1768 | 2.2976 | 2100 | 0.3934 | 0.6566 | 0.5954 | 0.6245 | 0.9276 |
103
+ | 0.1768 | 2.4070 | 2200 | 0.3879 | 0.7137 | 0.6038 | 0.6542 | 0.9313 |
104
+ | 0.1768 | 2.5164 | 2300 | 0.3829 | 0.5685 | 0.6326 | 0.5989 | 0.9175 |
105
+ | 0.1768 | 2.6258 | 2400 | 0.3508 | 0.7175 | 0.6191 | 0.6647 | 0.9328 |
106
+ | 0.1399 | 2.7352 | 2500 | 0.3215 | 0.6869 | 0.6311 | 0.6578 | 0.9327 |
107
+ | 0.1399 | 2.8446 | 2600 | 0.3271 | 0.7248 | 0.6171 | 0.6666 | 0.9358 |
108
+ | 0.1399 | 2.9540 | 2700 | 0.3226 | 0.6491 | 0.6544 | 0.6517 | 0.9277 |
109
+ | 0.1399 | 3.0635 | 2800 | 0.3336 | 0.6596 | 0.6386 | 0.6490 | 0.9278 |
110
+ | 0.1399 | 3.1729 | 2900 | 0.3423 | 0.6480 | 0.6624 | 0.6551 | 0.9314 |
111
+ | 0.1083 | 3.2823 | 3000 | 0.3698 | 0.7509 | 0.6566 | 0.7006 | 0.9372 |
112
+ | 0.1083 | 3.3917 | 3100 | 0.3353 | 0.6457 | 0.6649 | 0.6552 | 0.9287 |
113
+ | 0.1083 | 3.5011 | 3200 | 0.3391 | 0.7518 | 0.6626 | 0.7044 | 0.9383 |
114
+ | 0.1083 | 3.6105 | 3300 | 0.3314 | 0.7350 | 0.6699 | 0.7010 | 0.9381 |
115
+ | 0.1083 | 3.7199 | 3400 | 0.3338 | 0.6728 | 0.6832 | 0.6779 | 0.9347 |
116
+ | 0.0988 | 3.8293 | 3500 | 0.3239 | 0.7509 | 0.6753 | 0.7111 | 0.9369 |
117
+ | 0.0988 | 3.9387 | 3600 | 0.3481 | 0.7555 | 0.6564 | 0.7025 | 0.9395 |
118
+ | 0.0988 | 4.0481 | 3700 | 0.3231 | 0.6749 | 0.6883 | 0.6815 | 0.9348 |
119
+ | 0.0988 | 4.1575 | 3800 | 0.3581 | 0.7669 | 0.6699 | 0.7151 | 0.9411 |
120
+ | 0.0988 | 4.2670 | 3900 | 0.3213 | 0.7174 | 0.6873 | 0.7021 | 0.9389 |
121
+ | 0.0775 | 4.3764 | 4000 | 0.3244 | 0.7433 | 0.6738 | 0.7069 | 0.9387 |
122
+ | 0.0775 | 4.4858 | 4100 | 0.3275 | 0.7370 | 0.6868 | 0.7110 | 0.9405 |
123
+ | 0.0775 | 4.5952 | 4200 | 0.3197 | 0.7405 | 0.6997 | 0.7195 | 0.9413 |
124
+ | 0.0775 | 4.7046 | 4300 | 0.3183 | 0.7419 | 0.6935 | 0.7169 | 0.9415 |
125
+ | 0.0775 | 4.8140 | 4400 | 0.2961 | 0.7445 | 0.6933 | 0.7180 | 0.9408 |
126
+ | 0.0771 | 4.9234 | 4500 | 0.3195 | 0.7542 | 0.6986 | 0.7253 | 0.9426 |
127
+ | 0.0771 | 5.0328 | 4600 | 0.3295 | 0.7637 | 0.7010 | 0.7310 | 0.9435 |
128
+ | 0.0771 | 5.1422 | 4700 | 0.3204 | 0.7603 | 0.7006 | 0.7293 | 0.9434 |
129
+ | 0.0771 | 5.2516 | 4800 | 0.2992 | 0.7443 | 0.6995 | 0.7212 | 0.9395 |
130
+ | 0.0771 | 5.3611 | 4900 | 0.2978 | 0.7312 | 0.7033 | 0.7170 | 0.9393 |
131
+ | 0.0647 | 5.4705 | 5000 | 0.3324 | 0.7608 | 0.7079 | 0.7334 | 0.9432 |
132
+ | 0.0647 | 5.5799 | 5100 | 0.3356 | 0.7635 | 0.7038 | 0.7324 | 0.9430 |
133
+ | 0.0647 | 5.6893 | 5200 | 0.3121 | 0.7634 | 0.7121 | 0.7368 | 0.9430 |
134
+ | 0.0647 | 5.7987 | 5300 | 0.3392 | 0.7858 | 0.7003 | 0.7406 | 0.9448 |
135
+ | 0.0647 | 5.9081 | 5400 | 0.2952 | 0.7265 | 0.7220 | 0.7242 | 0.9412 |
136
+ | 0.0573 | 6.0175 | 5500 | 0.3070 | 0.7311 | 0.7211 | 0.7260 | 0.9429 |
137
+ | 0.0573 | 6.1269 | 5600 | 0.3207 | 0.7414 | 0.7241 | 0.7326 | 0.9435 |
138
+ | 0.0573 | 6.2363 | 5700 | 0.3130 | 0.7685 | 0.7231 | 0.7451 | 0.9455 |
139
+ | 0.0573 | 6.3457 | 5800 | 0.3441 | 0.7752 | 0.7139 | 0.7433 | 0.9447 |
140
+ | 0.0573 | 6.4551 | 5900 | 0.3196 | 0.7818 | 0.7128 | 0.7457 | 0.9458 |
141
+ | 0.0529 | 6.5646 | 6000 | 0.3369 | 0.7907 | 0.7164 | 0.7517 | 0.9456 |
142
+ | 0.0529 | 6.6740 | 6100 | 0.3059 | 0.7394 | 0.7267 | 0.7330 | 0.9435 |
143
+ | 0.0529 | 6.7834 | 6200 | 0.3043 | 0.7624 | 0.7231 | 0.7422 | 0.9444 |
144
+ | 0.0529 | 6.8928 | 6300 | 0.3028 | 0.7527 | 0.7252 | 0.7387 | 0.9441 |
145
+ | 0.0529 | 7.0022 | 6400 | 0.3089 | 0.7596 | 0.7293 | 0.7441 | 0.9457 |
146
+ | 0.0542 | 7.1116 | 6500 | 0.2927 | 0.7306 | 0.7286 | 0.7296 | 0.9408 |
147
+ | 0.0542 | 7.2210 | 6600 | 0.3178 | 0.7785 | 0.7274 | 0.7521 | 0.9456 |
148
+ | 0.0542 | 7.3304 | 6700 | 0.3267 | 0.7653 | 0.7304 | 0.7474 | 0.9450 |
149
+ | 0.0542 | 7.4398 | 6800 | 0.3254 | 0.7618 | 0.7280 | 0.7445 | 0.9450 |
150
+ | 0.0542 | 7.5492 | 6900 | 0.3240 | 0.7856 | 0.7254 | 0.7543 | 0.9464 |
151
+ | 0.0416 | 7.6586 | 7000 | 0.3203 | 0.7682 | 0.7319 | 0.7496 | 0.9463 |
152
+ | 0.0416 | 7.7681 | 7100 | 0.3176 | 0.7801 | 0.7299 | 0.7542 | 0.9468 |
153
+ | 0.0416 | 7.8775 | 7200 | 0.3012 | 0.7601 | 0.7355 | 0.7476 | 0.9470 |
154
+ | 0.0416 | 7.9869 | 7300 | 0.3092 | 0.7336 | 0.7377 | 0.7357 | 0.9436 |
155
+ | 0.0416 | 8.0963 | 7400 | 0.3025 | 0.7782 | 0.7349 | 0.7559 | 0.9480 |
156
+ | 0.0422 | 8.2057 | 7500 | 0.3046 | 0.7594 | 0.7340 | 0.7465 | 0.9459 |
157
+ | 0.0422 | 8.3151 | 7600 | 0.3113 | 0.7640 | 0.7332 | 0.7483 | 0.9458 |
158
+ | 0.0422 | 8.4245 | 7700 | 0.3002 | 0.7579 | 0.7394 | 0.7485 | 0.9461 |
159
+ | 0.0422 | 8.5339 | 7800 | 0.3173 | 0.7742 | 0.7321 | 0.7526 | 0.9464 |
160
+ | 0.0422 | 8.6433 | 7900 | 0.3084 | 0.7766 | 0.7334 | 0.7544 | 0.9467 |
161
+ | 0.041 | 8.7527 | 8000 | 0.3118 | 0.7829 | 0.7325 | 0.7569 | 0.9477 |
162
+ | 0.041 | 8.8621 | 8100 | 0.3145 | 0.7788 | 0.7389 | 0.7583 | 0.9473 |
163
+ | 0.041 | 8.9716 | 8200 | 0.3123 | 0.7788 | 0.7366 | 0.7571 | 0.9480 |
164
+ | 0.041 | 9.0810 | 8300 | 0.3088 | 0.7754 | 0.7398 | 0.7572 | 0.9476 |
165
+ | 0.041 | 9.1904 | 8400 | 0.3101 | 0.7804 | 0.7415 | 0.7604 | 0.9491 |
166
+ | 0.0323 | 9.2998 | 8500 | 0.3152 | 0.7829 | 0.7357 | 0.7585 | 0.9482 |
167
+ | 0.0323 | 9.4092 | 8600 | 0.3061 | 0.7734 | 0.7398 | 0.7562 | 0.9476 |
168
+ | 0.0323 | 9.5186 | 8700 | 0.3086 | 0.7636 | 0.7437 | 0.7535 | 0.9476 |
169
+ | 0.0323 | 9.6280 | 8800 | 0.3162 | 0.7723 | 0.7390 | 0.7553 | 0.9476 |
170
+ | 0.0323 | 9.7374 | 8900 | 0.3070 | 0.7605 | 0.7419 | 0.7511 | 0.9467 |
171
+ | 0.0357 | 9.8468 | 9000 | 0.3117 | 0.7824 | 0.7396 | 0.7604 | 0.9481 |
172
+ | 0.0357 | 9.9562 | 9100 | 0.3130 | 0.7750 | 0.7396 | 0.7569 | 0.9472 |
173
+ | 0.0357 | 10.0656 | 9200 | 0.3095 | 0.7673 | 0.7405 | 0.7537 | 0.9476 |
174
+ | 0.0357 | 10.1751 | 9300 | 0.3179 | 0.7868 | 0.7357 | 0.7604 | 0.9477 |
175
+ | 0.0357 | 10.2845 | 9400 | 0.3077 | 0.7645 | 0.7405 | 0.7523 | 0.9472 |
176
+ | 0.0359 | 10.3939 | 9500 | 0.3128 | 0.7798 | 0.7366 | 0.7576 | 0.9476 |
177
+ | 0.0359 | 10.5033 | 9600 | 0.3151 | 0.7784 | 0.7377 | 0.7575 | 0.9475 |
178
+ | 0.0359 | 10.6127 | 9700 | 0.3138 | 0.7744 | 0.7420 | 0.7579 | 0.9478 |
179
+ | 0.0359 | 10.7221 | 9800 | 0.3115 | 0.7688 | 0.7415 | 0.7549 | 0.9475 |
180
+ | 0.0359 | 10.8315 | 9900 | 0.3097 | 0.7673 | 0.7411 | 0.7540 | 0.9472 |
181
+ | 0.0301 | 10.9409 | 10000 | 0.3095 | 0.7674 | 0.7409 | 0.7539 | 0.9474 |
182
+
183
+
184
+ ### Framework versions
185
+
186
+ - Transformers 4.41.0.dev0
187
+ - Pytorch 2.2.1+cu121
188
+ - Datasets 2.19.1
189
+ - Tokenizers 0.19.1
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:69fc95f8442fccb6e39ab06412d7cbad74e5f3642295ec4e0ea2ebb826d3d9b0
3
  size 503844248
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a2a791eddf7ff1981e7b710d36788e4139503103b41490d51a7a66501c88f45d
3
  size 503844248
runs/May15_08-38-33_a17b87ecd093/events.out.tfevents.1715771437.a17b87ecd093.18908.1 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:070dff5fa21a1d6017c95348ab24f9b8a197c943350d8df6ef6225c2dc29c399
3
+ size 560