Hugging Face's logo

yanekyuk
/
bert-uncased-keyword-discriminator

Token Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
2
yanekyuk commited on
Commit
f18a3b0
•
1 Parent(s): 1f587bc

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +79 -0
README.md ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - precision
7
+ - recall
8
+ - accuracy
9
+ - f1
10
+ model-index:
11
+ - name: bert-uncased-keyword-discriminator
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
+ # bert-uncased-keyword-discriminator
19
+
20
+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
21
+ It achieves the following results on the evaluation set:
22
+ - Loss: 0.1296
23
+ - Precision: 0.8439
24
+ - Recall: 0.8722
25
+ - Accuracy: 0.9727
26
+ - F1: 0.8578
27
+ - Ent/precision: 0.8723
28
+ - Ent/accuracy: 0.9077
29
+ - Ent/f1: 0.8896
30
+ - Con/precision: 0.8010
31
+ - Con/accuracy: 0.8196
32
+ - Con/f1: 0.8102
33
+
34
+ ## Model description
35
+
36
+ More information needed
37
+
38
+ ## Intended uses & limitations
39
+
40
+ More information needed
41
+
42
+ ## Training and evaluation data
43
+
44
+ More information needed
45
+
46
+ ## Training procedure
47
+
48
+ ### Training hyperparameters
49
+
50
+ The following hyperparameters were used during training:
51
+ - learning_rate: 2e-05
52
+ - train_batch_size: 16
53
+ - eval_batch_size: 16
54
+ - seed: 42
55
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
56
+ - lr_scheduler_type: linear
57
+ - num_epochs: 8
58
+ - mixed_precision_training: Native AMP
59
+
60
+ ### Training results
61
+
62
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
63
+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
64
+ | 0.1849 | 1.0 | 1875 | 0.1323 | 0.7039 | 0.7428 | 0.9488 | 0.7228 | 0.7522 | 0.8166 | 0.7831 | 0.6268 | 0.6332 | 0.6300 |
65
+ | 0.1357 | 2.0 | 3750 | 0.1132 | 0.7581 | 0.8024 | 0.9592 | 0.7796 | 0.7948 | 0.8785 | 0.8346 | 0.6971 | 0.6895 | 0.6933 |
66
+ | 0.0965 | 3.0 | 5625 | 0.1033 | 0.8086 | 0.7980 | 0.9646 | 0.8032 | 0.8410 | 0.8592 | 0.8500 | 0.7560 | 0.7071 | 0.7307 |
67
+ | 0.0713 | 4.0 | 7500 | 0.1040 | 0.8181 | 0.8435 | 0.9683 | 0.8306 | 0.8526 | 0.8906 | 0.8712 | 0.7652 | 0.7736 | 0.7694 |
68
+ | 0.0525 | 5.0 | 9375 | 0.1126 | 0.8150 | 0.8633 | 0.9689 | 0.8385 | 0.8495 | 0.9064 | 0.8770 | 0.7629 | 0.7993 | 0.7807 |
69
+ | 0.0386 | 6.0 | 11250 | 0.1183 | 0.8374 | 0.8678 | 0.9719 | 0.8523 | 0.8709 | 0.9020 | 0.8862 | 0.7877 | 0.8170 | 0.8021 |
70
+ | 0.03 | 7.0 | 13125 | 0.1237 | 0.8369 | 0.8707 | 0.9723 | 0.8535 | 0.8657 | 0.9079 | 0.8863 | 0.7934 | 0.8155 | 0.8043 |
71
+ | 0.0244 | 8.0 | 15000 | 0.1296 | 0.8439 | 0.8722 | 0.9727 | 0.8578 | 0.8723 | 0.9077 | 0.8896 | 0.8010 | 0.8196 | 0.8102 |
72
+
73
+
74
+ ### Framework versions
75
+
76
+ - Transformers 4.19.2
77
+ - Pytorch 1.11.0+cu113
78
+ - Datasets 2.2.2
79
+ - Tokenizers 0.12.1