Jezia commited on
Commit
842dda2
1 Parent(s): ddeb3da

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +64 -0
README.md ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - rouge
7
+ model-index:
8
+ - name: AraBART-finetuned-wiki-ar
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ # AraBART-finetuned-wiki-ar
16
+
17
+ This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBART) on the None dataset.
18
+ It achieves the following results on the evaluation set:
19
+ - Loss: 2.6184
20
+ - Rouge1: 0.87
21
+ - Rouge2: 0.1157
22
+ - Rougel: 0.8635
23
+ - Rougelsum: 0.8783
24
+ - Gen Len: 19.3131
25
+
26
+ ## Model description
27
+
28
+ More information needed
29
+
30
+ ## Intended uses & limitations
31
+
32
+ More information needed
33
+
34
+ ## Training and evaluation data
35
+
36
+ More information needed
37
+
38
+ ## Training procedure
39
+
40
+ ### Training hyperparameters
41
+
42
+ The following hyperparameters were used during training:
43
+ - learning_rate: 2e-05
44
+ - train_batch_size: 8
45
+ - eval_batch_size: 8
46
+ - seed: 42
47
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
48
+ - lr_scheduler_type: linear
49
+ - num_epochs: 1
50
+ - mixed_precision_training: Native AMP
51
+
52
+ ### Training results
53
+
54
+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
55
+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
56
+ | 2.9386 | 1.0 | 2556 | 2.6184 | 0.87 | 0.1157 | 0.8635 | 0.8783 | 19.3131 |
57
+
58
+
59
+ ### Framework versions
60
+
61
+ - Transformers 4.25.1
62
+ - Pytorch 1.13.0+cu116
63
+ - Datasets 2.7.1
64
+ - Tokenizers 0.13.2