Saed2023 commited on
Commit
e88d5c6
1 Parent(s): b5e885d

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +66 -0
README.md ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - precision
7
+ - recall
8
+ - f1
9
+ - accuracy
10
+ model-index:
11
+ - name: LiLT-finetuned
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
+ # LiLT-finetuned
19
+
20
+ This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
21
+ It achieves the following results on the evaluation set:
22
+ - Loss: 1.7302
23
+ - Precision: 0.2787
24
+ - Recall: 0.2982
25
+ - F1: 0.2881
26
+ - Accuracy: 0.7616
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: 1e-05
46
+ - train_batch_size: 2
47
+ - eval_batch_size: 2
48
+ - seed: 42
49
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
+ - lr_scheduler_type: linear
51
+ - training_steps: 500
52
+
53
+ ### Training results
54
+
55
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
56
+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
57
+ | No log | 41.67 | 250 | 1.4739 | 0.2667 | 0.2807 | 0.2735 | 0.7632 |
58
+ | 0.1955 | 83.33 | 500 | 1.7302 | 0.2787 | 0.2982 | 0.2881 | 0.7616 |
59
+
60
+
61
+ ### Framework versions
62
+
63
+ - Transformers 4.28.1
64
+ - Pytorch 2.0.0+cu118
65
+ - Datasets 2.11.0
66
+ - Tokenizers 0.13.3