README.md
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1 ---
2 license: mit
3 base_model: flax-community/indonesian-roberta-base
4 tags:
5 - generated_from_trainer
6 datasets:
7 - indonlu
8 language:
9 - ind
10 metrics:
11 - precision
12 - recall
13 - f1
14 - accuracy
15 model-index:
16 - name: indonesian-roberta-base-posp-tagger
17 results:
18 - task:
19 name: Token Classification
20 type: token-classification
21 dataset:
22 name: indonlu
23 type: indonlu
24 config: posp
25 split: test
26 args: posp
27 metrics:
28 - name: Precision
29 type: precision
30 value: 0.9625100240577386
31 - name: Recall
32 type: recall
33 value: 0.9625100240577386
34 - name: F1
35 type: f1
36 value: 0.9625100240577386
37 - name: Accuracy
38 type: accuracy
39 value: 0.9625100240577386
40 ---
41
42 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
43 should probably proofread and complete it, then remove this comment. -->
44
45 # indonesian-roberta-base-posp-tagger
46
47 This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on the indonlu dataset.
48 It achieves the following results on the evaluation set:
49 - Loss: 0.1395
50 - Precision: 0.9625
51 - Recall: 0.9625
52 - F1: 0.9625
53 - Accuracy: 0.9625
54
55 ## Model description
56
57 More information needed
58
59 ## Intended uses & limitations
60
61 More information needed
62
63 ## Training and evaluation data
64
65 More information needed
66
67 ## Training procedure
68
69 ### Training hyperparameters
70
71 The following hyperparameters were used during training:
72 - learning_rate: 2e-05
73 - train_batch_size: 16
74 - eval_batch_size: 16
75 - seed: 42
76 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
77 - lr_scheduler_type: linear
78 - num_epochs: 10
79
80 ### Training results
81
82 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
83 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
84 | No log | 1.0 | 420 | 0.2254 | 0.9313 | 0.9313 | 0.9313 | 0.9313 |
85 | 0.4398 | 2.0 | 840 | 0.1617 | 0.9499 | 0.9499 | 0.9499 | 0.9499 |
86 | 0.1566 | 3.0 | 1260 | 0.1431 | 0.9569 | 0.9569 | 0.9569 | 0.9569 |
87 | 0.103 | 4.0 | 1680 | 0.1412 | 0.9605 | 0.9605 | 0.9605 | 0.9605 |
88 | 0.0723 | 5.0 | 2100 | 0.1408 | 0.9635 | 0.9635 | 0.9635 | 0.9635 |
89 | 0.051 | 6.0 | 2520 | 0.1408 | 0.9642 | 0.9642 | 0.9642 | 0.9642 |
90 | 0.051 | 7.0 | 2940 | 0.1510 | 0.9635 | 0.9635 | 0.9635 | 0.9635 |
91 | 0.0368 | 8.0 | 3360 | 0.1653 | 0.9645 | 0.9645 | 0.9645 | 0.9645 |
92 | 0.0277 | 9.0 | 3780 | 0.1664 | 0.9644 | 0.9644 | 0.9644 | 0.9644 |
93 | 0.0231 | 10.0 | 4200 | 0.1668 | 0.9646 | 0.9646 | 0.9646 | 0.9646 |
94
95
96 ### Framework versions
97
98 - Transformers 4.37.2
99 - Pytorch 2.2.0+cu118
100 - Datasets 2.16.1
101 - Tokenizers 0.15.1
102