Hugging Face's logo

chintagunta85
/
test_ner3

Token Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results
Model card Files
xet
Metrics Community
1
chintagunta85 commited on
Commit
76be0e0
·
1 Parent(s): fe7b6cd

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +108 -0
README.md ADDED
@@ -0,0 +1,108 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ datasets:
6
+ - pv_dataset
7
+ metrics:
8
+ - precision
9
+ - recall
10
+ - f1
11
+ - accuracy
12
+ model-index:
13
+ - name: test_ner3
14
+ results:
15
+ - task:
16
+ name: Token Classification
17
+ type: token-classification
18
+ dataset:
19
+ name: pv_dataset
20
+ type: pv_dataset
21
+ config: PVDatasetCorpus
22
+ split: train
23
+ args: PVDatasetCorpus
24
+ metrics:
25
+ - name: Precision
26
+ type: precision
27
+ value: 0.6698151950718686
28
+ - name: Recall
29
+ type: recall
30
+ value: 0.6499117663801446
31
+ - name: F1
32
+ type: f1
33
+ value: 0.6597133941985438
34
+ - name: Accuracy
35
+ type: accuracy
36
+ value: 0.9606609586670052
37
+ ---
38
+
39
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
40
+ should probably proofread and complete it, then remove this comment. -->
41
+
42
+ # test_ner3
43
+
44
+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pv_dataset dataset.
45
+ It achieves the following results on the evaluation set:
46
+ - Loss: 0.2983
47
+ - Precision: 0.6698
48
+ - Recall: 0.6499
49
+ - F1: 0.6597
50
+ - Accuracy: 0.9607
51
+
52
+ ## Model description
53
+
54
+ More information needed
55
+
56
+ ## Intended uses & limitations
57
+
58
+ More information needed
59
+
60
+ ## Training and evaluation data
61
+
62
+ More information needed
63
+
64
+ ## Training procedure
65
+
66
+ ### Training hyperparameters
67
+
68
+ The following hyperparameters were used during training:
69
+ - learning_rate: 2e-05
70
+ - train_batch_size: 16
71
+ - eval_batch_size: 16
72
+ - seed: 42
73
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
74
+ - lr_scheduler_type: linear
75
+ - num_epochs: 20
76
+
77
+ ### Training results
78
+
79
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
80
+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
81
+ | 0.1106 | 1.0 | 1813 | 0.1128 | 0.6050 | 0.5949 | 0.5999 | 0.9565 |
82
+ | 0.0705 | 2.0 | 3626 | 0.1190 | 0.6279 | 0.6122 | 0.6200 | 0.9585 |
83
+ | 0.0433 | 3.0 | 5439 | 0.1458 | 0.6342 | 0.5983 | 0.6157 | 0.9574 |
84
+ | 0.0301 | 4.0 | 7252 | 0.1453 | 0.6305 | 0.6818 | 0.6552 | 0.9594 |
85
+ | 0.0196 | 5.0 | 9065 | 0.1672 | 0.6358 | 0.6871 | 0.6605 | 0.9594 |
86
+ | 0.0133 | 6.0 | 10878 | 0.1931 | 0.6427 | 0.6138 | 0.6279 | 0.9587 |
87
+ | 0.0104 | 7.0 | 12691 | 0.1948 | 0.6657 | 0.6511 | 0.6583 | 0.9607 |
88
+ | 0.0081 | 8.0 | 14504 | 0.2243 | 0.6341 | 0.6574 | 0.6455 | 0.9586 |
89
+ | 0.0054 | 9.0 | 16317 | 0.2432 | 0.6547 | 0.6318 | 0.6431 | 0.9588 |
90
+ | 0.0041 | 10.0 | 18130 | 0.2422 | 0.6717 | 0.6397 | 0.6553 | 0.9605 |
91
+ | 0.0041 | 11.0 | 19943 | 0.2415 | 0.6571 | 0.6420 | 0.6495 | 0.9601 |
92
+ | 0.0027 | 12.0 | 21756 | 0.2567 | 0.6560 | 0.6590 | 0.6575 | 0.9601 |
93
+ | 0.0023 | 13.0 | 23569 | 0.2609 | 0.6640 | 0.6495 | 0.6566 | 0.9606 |
94
+ | 0.002 | 14.0 | 25382 | 0.2710 | 0.6542 | 0.6670 | 0.6606 | 0.9598 |
95
+ | 0.0012 | 15.0 | 27195 | 0.2766 | 0.6692 | 0.6539 | 0.6615 | 0.9610 |
96
+ | 0.001 | 16.0 | 29008 | 0.2938 | 0.6692 | 0.6415 | 0.6551 | 0.9603 |
97
+ | 0.0007 | 17.0 | 30821 | 0.2969 | 0.6654 | 0.6490 | 0.6571 | 0.9604 |
98
+ | 0.0007 | 18.0 | 32634 | 0.3035 | 0.6628 | 0.6456 | 0.6541 | 0.9601 |
99
+ | 0.0007 | 19.0 | 34447 | 0.2947 | 0.6730 | 0.6489 | 0.6607 | 0.9609 |
100
+ | 0.0004 | 20.0 | 36260 | 0.2983 | 0.6698 | 0.6499 | 0.6597 | 0.9607 |
101
+
102
+
103
+ ### Framework versions
104
+
105
+ - Transformers 4.21.0
106
+ - Pytorch 1.12.0+cu113
107
+ - Datasets 2.4.0
108
+ - Tokenizers 0.12.1