File size: 3,565 Bytes
c9685b9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fa1a03b
c9685b9
 
 
 
 
 
 
4da0c9a
c9685b9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4da0c9a
 
 
 
 
 
 
 
 
 
 
c9685b9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ja-pl
tags:
- generated_from_trainer
datasets:
- tatoeba
metrics:
- bleu
model-index:
- name: opus_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: tatoeba
      type: tatoeba
      config: ja-pl
      split: train
      args: ja-pl
    metrics:
    - name: Bleu
      type: bleu
      value: 34.4952
---

<!-- 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. -->

# opus_model

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-pl](https://huggingface.co/Helsinki-NLP/opus-mt-ja-pl) on the tatoeba dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1164
- Bleu: 34.4952
- Gen Len: 9.442
- Meteor: 0.5692
- Chrf: 53.728

## Model description

[Helsinki-NLP/opus-mt-ja-pl](https://huggingface.co/Helsinki-NLP/opus-mt-ja-pl) model fine-tuned on tatoeba and some pop culture texts (vn, manga, rpgs).

## Intended uses & limitations

More information needed

## Training and evaluation data

Training with kaggle notebook (GPU) on GPU P100.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Examples

|           Japanese        | Original translation | DeepL | Opus-mt-ja-pl-pop_v2 |
|---------------------------|----------------------|------ |-------------------|
| 今ちょっとやることがあってね   | Mam teraz coś do zrobienia. | Mam teraz kilka rzeczy do zrobienia. | Mam teraz kilka spraw do załatwienia. |
| なぜッあの少女を助けてやらなかったのだ! | Czemu jej nie pomogłeś!  | Dlaczego nie pomogłeś tej dziewczynie? | Dlaczego jej nie pomogłeś?! |
| ここで何をしている? | Czego tu szukacie? | Co ty tu robisz? | Co tu robisz? |
| あんたの協力が要る | Potrzebujemy cię. | Potrzebuję twojej pomocy. | Potrzebuję twojej pomocy. |
| こたえはなに? | A jaka jest właściwie odpowiedź? | Jaka jest odpowiedź? | Co to jest? |
| 一人で寝んのが怖くなったんか? | Boisz się spać sama? | Boisz się spać samotnie? | Boisz się spać sama? |

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Bleu    | Gen Len | Meteor | Chrf    |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:------:|:-------:|
| 2.5658        | 1.0   | 56681  | 1.6196          | 21.6767 | 9.2915  | 0.4586 | 43.4725 |
| 2.3419        | 2.0   | 113362 | 1.4667          | 25.4469 | 9.3688  | 0.4916 | 46.3391 |
| 2.23          | 3.0   | 170043 | 1.3715          | 27.166  | 9.4895  | 0.5089 | 48.2252 |
| 2.1139        | 4.0   | 226724 | 1.2833          | 28.9288 | 9.4581  | 0.5244 | 49.4667 |
| 1.9825        | 5.0   | 283405 | 1.2170          | 31.3751 | 9.3229  | 0.5358 | 51.0005 |
| 1.8982        | 6.0   | 340086 | 1.1660          | 32.9805 | 9.4976  | 0.5563 | 52.5487 |
| 1.8198        | 7.0   | 396767 | 1.1305          | 34.0223 | 9.4436  | 0.5665 | 53.2912 |
| 1.7592        | 8.0   | 453448 | 1.1164          | 34.4952 | 9.442   | 0.5692 | 53.728  |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1