tmnam20 commited on
Commit
6fa8acd
1 Parent(s): 74ba547

Upload README.md with huggingface_hub

Browse files
Files changed (1) hide show
  1. README.md +78 -0
README.md ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - en
4
+ license: mit
5
+ base_model: xlm-roberta-large
6
+ tags:
7
+ - generated_from_trainer
8
+ datasets:
9
+ - tmnam20/VieGLUE
10
+ metrics:
11
+ - accuracy
12
+ model-index:
13
+ - name: xlm-roberta-large-vsfc-10
14
+ results:
15
+ - task:
16
+ name: Text Classification
17
+ type: text-classification
18
+ dataset:
19
+ name: tmnam20/VieGLUE/VSFC
20
+ type: tmnam20/VieGLUE
21
+ config: vsfc
22
+ split: validation
23
+ args: vsfc
24
+ metrics:
25
+ - name: Accuracy
26
+ type: accuracy
27
+ value: 0.9513581806696146
28
+ ---
29
+
30
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
31
+ should probably proofread and complete it, then remove this comment. -->
32
+
33
+ # xlm-roberta-large-vsfc-10
34
+
35
+ This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the tmnam20/VieGLUE/VSFC dataset.
36
+ It achieves the following results on the evaluation set:
37
+ - Loss: 0.2194
38
+ - Accuracy: 0.9514
39
+
40
+ ## Model description
41
+
42
+ More information needed
43
+
44
+ ## Intended uses & limitations
45
+
46
+ More information needed
47
+
48
+ ## Training and evaluation data
49
+
50
+ More information needed
51
+
52
+ ## Training procedure
53
+
54
+ ### Training hyperparameters
55
+
56
+ The following hyperparameters were used during training:
57
+ - learning_rate: 2e-05
58
+ - train_batch_size: 32
59
+ - eval_batch_size: 16
60
+ - seed: 10
61
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
+ - lr_scheduler_type: linear
63
+ - num_epochs: 3.0
64
+
65
+ ### Training results
66
+
67
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
68
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
69
+ | 0.1964 | 1.4 | 500 | 0.2316 | 0.9356 |
70
+ | 0.1334 | 2.79 | 1000 | 0.2113 | 0.9520 |
71
+
72
+
73
+ ### Framework versions
74
+
75
+ - Transformers 4.36.0
76
+ - Pytorch 2.1.0+cu121
77
+ - Datasets 2.15.0
78
+ - Tokenizers 0.15.0