update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,68 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: mit
|
3 |
+
tags:
|
4 |
+
- generated_from_trainer
|
5 |
+
metrics:
|
6 |
+
- rouge
|
7 |
+
model-index:
|
8 |
+
- name: finetune-newwiki-summarization-ver2
|
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 |
+
# finetune-newwiki-summarization-ver2
|
16 |
+
|
17 |
+
This model is a fine-tuned version of [minnehwg/finetune-newwiki-summarization-ver1](https://huggingface.co/minnehwg/finetune-newwiki-summarization-ver1) on the None dataset.
|
18 |
+
It achieves the following results on the evaluation set:
|
19 |
+
- Loss: 0.4697
|
20 |
+
- Rouge1: 48.1659
|
21 |
+
- Rouge2: 25.1491
|
22 |
+
- Rougel: 34.7794
|
23 |
+
- Rougelsum: 37.0893
|
24 |
+
|
25 |
+
## Model description
|
26 |
+
|
27 |
+
More information needed
|
28 |
+
|
29 |
+
## Intended uses & limitations
|
30 |
+
|
31 |
+
More information needed
|
32 |
+
|
33 |
+
## Training and evaluation data
|
34 |
+
|
35 |
+
More information needed
|
36 |
+
|
37 |
+
## Training procedure
|
38 |
+
|
39 |
+
### Training hyperparameters
|
40 |
+
|
41 |
+
The following hyperparameters were used during training:
|
42 |
+
- learning_rate: 1e-06
|
43 |
+
- train_batch_size: 8
|
44 |
+
- eval_batch_size: 8
|
45 |
+
- seed: 42
|
46 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
47 |
+
- lr_scheduler_type: linear
|
48 |
+
- lr_scheduler_warmup_ratio: 0.1
|
49 |
+
- num_epochs: 7
|
50 |
+
- mixed_precision_training: Native AMP
|
51 |
+
|
52 |
+
### Training results
|
53 |
+
|
54 |
+
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|
55 |
+
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
|
56 |
+
| 0.4912 | 1.0 | 990 | 0.4701 | 48.1754 | 25.0221 | 34.7613 | 37.0734 |
|
57 |
+
| 0.4748 | 2.0 | 1980 | 0.4694 | 48.3629 | 25.3649 | 35.0239 | 37.3084 |
|
58 |
+
| 0.4755 | 3.0 | 2970 | 0.4695 | 48.2770 | 25.1907 | 34.8456 | 37.1930 |
|
59 |
+
| 0.4703 | 4.0 | 3960 | 0.4696 | 48.1801 | 25.1769 | 34.8004 | 37.0817 |
|
60 |
+
| 0.468 | 5.0 | 4950 | 0.4697 | 48.1659 | 25.1491 | 34.7794 | 37.0893 |
|
61 |
+
|
62 |
+
|
63 |
+
### Framework versions
|
64 |
+
|
65 |
+
- Transformers 4.17.0
|
66 |
+
- Pytorch 2.1.2
|
67 |
+
- Datasets 2.18.0
|
68 |
+
- Tokenizers 0.15.2
|