Hugging Face's logo

morganchen1007
/
distilbert-base-uncased-finetuned-ner_0220_J_ORIDATA

Token Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
morganchen1007 commited on
Commit
02b5dbe
•
1 Parent(s): 51379f4

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +114 -0
README.md ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - precision
7
+ - recall
8
+ - f1
9
+ - accuracy
10
+ model-index:
11
+ - name: distilbert-base-uncased-finetuned-ner_0220_J_ORIDATA
12
+ results: []
13
+ ---
14
+
15
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
16
+ should probably proofread and complete it, then remove this comment. -->
17
+
18
+ # distilbert-base-uncased-finetuned-ner_0220_J_ORIDATA
19
+
20
+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
21
+ It achieves the following results on the evaluation set:
22
+ - Loss: 0.4949
23
+ - Precision: 0.8987
24
+ - Recall: 0.9551
25
+ - F1: 0.9260
26
+ - Accuracy: 0.9437
27
+
28
+ ## Model description
29
+
30
+ More information needed
31
+
32
+ ## Intended uses & limitations
33
+
34
+ More information needed
35
+
36
+ ## Training and evaluation data
37
+
38
+ More information needed
39
+
40
+ ## Training procedure
41
+
42
+ ### Training hyperparameters
43
+
44
+ The following hyperparameters were used during training:
45
+ - learning_rate: 2e-05
46
+ - train_batch_size: 16
47
+ - eval_batch_size: 16
48
+ - seed: 42
49
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
+ - lr_scheduler_type: linear
51
+ - num_epochs: 50
52
+
53
+ ### Training results
54
+
55
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
56
+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
57
+ | No log | 1.0 | 111 | 0.2996 | 0.7246 | 0.8407 | 0.7783 | 0.9295 |
58
+ | No log | 2.0 | 222 | 0.2545 | 0.8067 | 0.8983 | 0.8500 | 0.9343 |
59
+ | No log | 3.0 | 333 | 0.2398 | 0.8649 | 0.9280 | 0.8953 | 0.9445 |
60
+ | No log | 4.0 | 444 | 0.2326 | 0.8651 | 0.9297 | 0.8962 | 0.9459 |
61
+ | 0.329 | 5.0 | 555 | 0.2200 | 0.8836 | 0.9263 | 0.9044 | 0.9459 |
62
+ | 0.329 | 6.0 | 666 | 0.2382 | 0.8365 | 0.9280 | 0.8799 | 0.9315 |
63
+ | 0.329 | 7.0 | 777 | 0.2349 | 0.8775 | 0.9347 | 0.9052 | 0.9395 |
64
+ | 0.329 | 8.0 | 888 | 0.2604 | 0.8832 | 0.9424 | 0.9118 | 0.9415 |
65
+ | 0.329 | 9.0 | 999 | 0.2705 | 0.8929 | 0.9331 | 0.9126 | 0.9483 |
66
+ | 0.124 | 10.0 | 1110 | 0.2986 | 0.8731 | 0.9390 | 0.9049 | 0.9410 |
67
+ | 0.124 | 11.0 | 1221 | 0.2960 | 0.8805 | 0.9432 | 0.9108 | 0.9441 |
68
+ | 0.124 | 12.0 | 1332 | 0.2798 | 0.8778 | 0.9373 | 0.9066 | 0.9401 |
69
+ | 0.124 | 13.0 | 1443 | 0.3364 | 0.8851 | 0.9534 | 0.9180 | 0.9422 |
70
+ | 0.0735 | 14.0 | 1554 | 0.3546 | 0.8943 | 0.9534 | 0.9229 | 0.9425 |
71
+ | 0.0735 | 15.0 | 1665 | 0.3449 | 0.8917 | 0.9492 | 0.9195 | 0.9443 |
72
+ | 0.0735 | 16.0 | 1776 | 0.3789 | 0.8817 | 0.9407 | 0.9102 | 0.9381 |
73
+ | 0.0735 | 17.0 | 1887 | 0.3727 | 0.8803 | 0.9407 | 0.9095 | 0.9443 |
74
+ | 0.0735 | 18.0 | 1998 | 0.3633 | 0.8841 | 0.9373 | 0.9099 | 0.9387 |
75
+ | 0.0453 | 19.0 | 2109 | 0.4279 | 0.8816 | 0.9398 | 0.9098 | 0.9328 |
76
+ | 0.0453 | 20.0 | 2220 | 0.4055 | 0.8939 | 0.95 | 0.9211 | 0.9431 |
77
+ | 0.0453 | 21.0 | 2331 | 0.4138 | 0.8924 | 0.9492 | 0.9199 | 0.9441 |
78
+ | 0.0453 | 22.0 | 2442 | 0.4275 | 0.8948 | 0.9441 | 0.9188 | 0.9422 |
79
+ | 0.0283 | 23.0 | 2553 | 0.4319 | 0.8955 | 0.9508 | 0.9223 | 0.9418 |
80
+ | 0.0283 | 24.0 | 2664 | 0.4179 | 0.8878 | 0.9525 | 0.9191 | 0.9464 |
81
+ | 0.0283 | 25.0 | 2775 | 0.4341 | 0.8937 | 0.9407 | 0.9166 | 0.9420 |
82
+ | 0.0283 | 26.0 | 2886 | 0.4420 | 0.9063 | 0.9508 | 0.9280 | 0.9439 |
83
+ | 0.0283 | 27.0 | 2997 | 0.4370 | 0.8986 | 0.9466 | 0.9220 | 0.9432 |
84
+ | 0.0188 | 28.0 | 3108 | 0.4557 | 0.8953 | 0.9492 | 0.9214 | 0.9430 |
85
+ | 0.0188 | 29.0 | 3219 | 0.4458 | 0.8934 | 0.9449 | 0.9185 | 0.9437 |
86
+ | 0.0188 | 30.0 | 3330 | 0.4461 | 0.8973 | 0.9475 | 0.9217 | 0.9425 |
87
+ | 0.0188 | 31.0 | 3441 | 0.4638 | 0.9014 | 0.9525 | 0.9262 | 0.9426 |
88
+ | 0.0132 | 32.0 | 3552 | 0.4732 | 0.9029 | 0.9534 | 0.9275 | 0.9437 |
89
+ | 0.0132 | 33.0 | 3663 | 0.4645 | 0.9062 | 0.9576 | 0.9312 | 0.9453 |
90
+ | 0.0132 | 34.0 | 3774 | 0.4542 | 0.8981 | 0.9483 | 0.9225 | 0.9447 |
91
+ | 0.0132 | 35.0 | 3885 | 0.4702 | 0.8974 | 0.9492 | 0.9226 | 0.9431 |
92
+ | 0.0132 | 36.0 | 3996 | 0.4824 | 0.9081 | 0.9542 | 0.9306 | 0.9428 |
93
+ | 0.0101 | 37.0 | 4107 | 0.4757 | 0.8978 | 0.9534 | 0.9248 | 0.9442 |
94
+ | 0.0101 | 38.0 | 4218 | 0.4750 | 0.8971 | 0.9534 | 0.9244 | 0.9453 |
95
+ | 0.0101 | 39.0 | 4329 | 0.4843 | 0.9008 | 0.9542 | 0.9267 | 0.9446 |
96
+ | 0.0101 | 40.0 | 4440 | 0.4840 | 0.9019 | 0.9585 | 0.9293 | 0.9464 |
97
+ | 0.0077 | 41.0 | 4551 | 0.4852 | 0.8939 | 0.9492 | 0.9207 | 0.9436 |
98
+ | 0.0077 | 42.0 | 4662 | 0.4864 | 0.9051 | 0.9542 | 0.9290 | 0.9447 |
99
+ | 0.0077 | 43.0 | 4773 | 0.4801 | 0.9010 | 0.9559 | 0.9276 | 0.9431 |
100
+ | 0.0077 | 44.0 | 4884 | 0.4887 | 0.9016 | 0.9551 | 0.9276 | 0.9435 |
101
+ | 0.0077 | 45.0 | 4995 | 0.4973 | 0.8972 | 0.9542 | 0.9248 | 0.9430 |
102
+ | 0.0065 | 46.0 | 5106 | 0.4942 | 0.9 | 0.9534 | 0.9259 | 0.9436 |
103
+ | 0.0065 | 47.0 | 5217 | 0.4933 | 0.9007 | 0.9534 | 0.9263 | 0.9436 |
104
+ | 0.0065 | 48.0 | 5328 | 0.4979 | 0.8987 | 0.9551 | 0.9260 | 0.9434 |
105
+ | 0.0065 | 49.0 | 5439 | 0.4966 | 0.9009 | 0.9551 | 0.9272 | 0.9434 |
106
+ | 0.0059 | 50.0 | 5550 | 0.4949 | 0.8987 | 0.9551 | 0.9260 | 0.9437 |
107
+
108
+
109
+ ### Framework versions
110
+
111
+ - Transformers 4.20.1
112
+ - Pytorch 1.13.0+cu117
113
+ - Datasets 2.8.0
114
+ - Tokenizers 0.12.1