File size: 2,482 Bytes
8c66bb0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8232440
 
 
 
 
 
8c66bb0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8232440
 
8c66bb0
 
 
8232440
8c66bb0
 
 
 
 
8232440
 
 
 
 
 
 
 
 
 
8c66bb0
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
---
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: vit5-base-standardized-color
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit5-base-standardized-color

This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9951
- Rouge1: 74.1102
- Rouge2: 67.9199
- Rougel: 73.686
- Rougelsum: 73.7568
- Gen Len: 7.0148

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 118  | 0.7373          | 74.1623 | 67.7624 | 73.6071 | 73.6764   | 7.3326  |
| No log        | 2.0   | 236  | 0.7758          | 74.1167 | 67.7666 | 73.7039 | 73.8076   | 7.0869  |
| No log        | 3.0   | 354  | 0.8174          | 73.8958 | 67.4854 | 73.3437 | 73.4362   | 7.1822  |
| No log        | 4.0   | 472  | 0.8195          | 74.8085 | 68.4703 | 74.3389 | 74.4854   | 6.7903  |
| 0.2234        | 5.0   | 590  | 0.8848          | 74.1319 | 67.6899 | 73.5608 | 73.6273   | 7.2013  |
| 0.2234        | 6.0   | 708  | 0.9413          | 73.4933 | 67.0495 | 73.0176 | 73.0687   | 7.2839  |
| 0.2234        | 7.0   | 826  | 0.9167          | 74.1512 | 67.7638 | 73.7512 | 73.8058   | 6.9703  |
| 0.2234        | 8.0   | 944  | 0.9577          | 73.8412 | 67.3981 | 73.3697 | 73.4324   | 7.1525  |
| 0.1303        | 9.0   | 1062 | 0.9869          | 73.9929 | 67.64   | 73.4942 | 73.5355   | 7.2309  |
| 0.1303        | 10.0  | 1180 | 0.9951          | 74.1102 | 67.9199 | 73.686  | 73.7568   | 7.0148  |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3