Hugging Face's logo

kabelomalapane
/
En-Nso_update

Translation
Transformers
PyTorch
TensorBoard
marian
text2text-generation
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
Kabelo Malapane commited on
Commit
347016c
•
1 Parent(s): b6132c9

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +159 -0
README.md ADDED
@@ -0,0 +1,159 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - translation
5
+ - generated_from_trainer
6
+ metrics:
7
+ - bleu
8
+ model-index:
9
+ - name: En-Nso_update
10
+ results: []
11
+ ---
12
+
13
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
+ should probably proofread and complete it, then remove this comment. -->
15
+
16
+ # En-Nso_update
17
+
18
+ This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset.
19
+ It achieves the following results on the evaluation set:
20
+ - Loss: 2.8782
21
+ - Bleu: 31.2967
22
+
23
+ ## Model description
24
+
25
+ More information needed
26
+
27
+ ## Intended uses & limitations
28
+
29
+ More information needed
30
+
31
+ ## Training and evaluation data
32
+
33
+ More information needed
34
+
35
+ ## Training procedure
36
+
37
+ ### Training hyperparameters
38
+
39
+ The following hyperparameters were used during training:
40
+ - learning_rate: 2e-05
41
+ - train_batch_size: 16
42
+ - eval_batch_size: 32
43
+ - seed: 42
44
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
45
+ - lr_scheduler_type: linear
46
+ - num_epochs: 100
47
+
48
+ ### Training results
49
+
50
+ | Training Loss | Epoch | Step | Validation Loss | Bleu |
51
+ |:-------------:|:-----:|:----:|:---------------:|:-------:|
52
+ | No log | 1.0 | 4 | 7.2950 | 0.0088 |
53
+ | No log | 2.0 | 8 | 5.9614 | 0.6848 |
54
+ | No log | 3.0 | 12 | 5.0695 | 4.9050 |
55
+ | No log | 4.0 | 16 | 4.5523 | 9.1757 |
56
+ | No log | 5.0 | 20 | 4.2355 | 10.4744 |
57
+ | No log | 6.0 | 24 | 4.0106 | 14.6163 |
58
+ | No log | 7.0 | 28 | 3.8427 | 15.8379 |
59
+ | No log | 8.0 | 32 | 3.7264 | 15.6158 |
60
+ | No log | 9.0 | 36 | 3.6338 | 16.3562 |
61
+ | No log | 10.0 | 40 | 3.5555 | 21.1011 |
62
+ | No log | 11.0 | 44 | 3.4839 | 21.5754 |
63
+ | No log | 12.0 | 48 | 3.4180 | 22.7155 |
64
+ | No log | 13.0 | 52 | 3.3620 | 23.1592 |
65
+ | No log | 14.0 | 56 | 3.3115 | 24.3886 |
66
+ | No log | 15.0 | 60 | 3.2676 | 24.1278 |
67
+ | No log | 16.0 | 64 | 3.2285 | 24.2245 |
68
+ | No log | 17.0 | 68 | 3.1974 | 23.9716 |
69
+ | No log | 18.0 | 72 | 3.1695 | 24.2395 |
70
+ | No log | 19.0 | 76 | 3.1441 | 23.3442 |
71
+ | No log | 20.0 | 80 | 3.1235 | 21.3332 |
72
+ | No log | 21.0 | 84 | 3.1029 | 21.8410 |
73
+ | No log | 22.0 | 88 | 3.0849 | 22.4065 |
74
+ | No log | 23.0 | 92 | 3.0666 | 22.3016 |
75
+ | No log | 24.0 | 96 | 3.0534 | 22.9616 |
76
+ | No log | 25.0 | 100 | 3.0423 | 23.3971 |
77
+ | No log | 26.0 | 104 | 3.0306 | 23.5443 |
78
+ | No log | 27.0 | 108 | 3.0183 | 23.3348 |
79
+ | No log | 28.0 | 112 | 3.0051 | 23.4077 |
80
+ | No log | 29.0 | 116 | 2.9947 | 24.1791 |
81
+ | No log | 30.0 | 120 | 2.9855 | 24.1265 |
82
+ | No log | 31.0 | 124 | 2.9777 | 23.9860 |
83
+ | No log | 32.0 | 128 | 2.9691 | 24.7301 |
84
+ | No log | 33.0 | 132 | 2.9597 | 25.1896 |
85
+ | No log | 34.0 | 136 | 2.9521 | 24.5893 |
86
+ | No log | 35.0 | 140 | 2.9457 | 24.5229 |
87
+ | No log | 36.0 | 144 | 2.9409 | 24.6232 |
88
+ | No log | 37.0 | 148 | 2.9354 | 24.2830 |
89
+ | No log | 38.0 | 152 | 2.9322 | 26.1404 |
90
+ | No log | 39.0 | 156 | 2.9306 | 25.9425 |
91
+ | No log | 40.0 | 160 | 2.9288 | 30.5432 |
92
+ | No log | 41.0 | 164 | 2.9261 | 29.4635 |
93
+ | No log | 42.0 | 168 | 2.9215 | 28.4787 |
94
+ | No log | 43.0 | 172 | 2.9182 | 28.9082 |
95
+ | No log | 44.0 | 176 | 2.9151 | 29.3171 |
96
+ | No log | 45.0 | 180 | 2.9132 | 28.3602 |
97
+ | No log | 46.0 | 184 | 2.9126 | 28.9583 |
98
+ | No log | 47.0 | 188 | 2.9104 | 26.0269 |
99
+ | No log | 48.0 | 192 | 2.9086 | 29.6904 |
100
+ | No log | 49.0 | 196 | 2.9052 | 29.2881 |
101
+ | No log | 50.0 | 200 | 2.9020 | 29.6063 |
102
+ | No log | 51.0 | 204 | 2.8994 | 29.5224 |
103
+ | No log | 52.0 | 208 | 2.8960 | 29.3913 |
104
+ | No log | 53.0 | 212 | 2.8930 | 30.5451 |
105
+ | No log | 54.0 | 216 | 2.8889 | 32.1862 |
106
+ | No log | 55.0 | 220 | 2.8869 | 31.9423 |
107
+ | No log | 56.0 | 224 | 2.8859 | 30.7244 |
108
+ | No log | 57.0 | 228 | 2.8846 | 30.8172 |
109
+ | No log | 58.0 | 232 | 2.8837 | 30.5376 |
110
+ | No log | 59.0 | 236 | 2.8826 | 31.1454 |
111
+ | No log | 60.0 | 240 | 2.8813 | 30.9049 |
112
+ | No log | 61.0 | 244 | 2.8802 | 30.6363 |
113
+ | No log | 62.0 | 248 | 2.8802 | 31.3739 |
114
+ | No log | 63.0 | 252 | 2.8799 | 30.9776 |
115
+ | No log | 64.0 | 256 | 2.8793 | 29.8283 |
116
+ | No log | 65.0 | 260 | 2.8795 | 29.6912 |
117
+ | No log | 66.0 | 264 | 2.8804 | 29.7654 |
118
+ | No log | 67.0 | 268 | 2.8810 | 29.1586 |
119
+ | No log | 68.0 | 272 | 2.8822 | 28.8888 |
120
+ | No log | 69.0 | 276 | 2.8819 | 29.7222 |
121
+ | No log | 70.0 | 280 | 2.8810 | 29.9932 |
122
+ | No log | 71.0 | 284 | 2.8811 | 30.2492 |
123
+ | No log | 72.0 | 288 | 2.8802 | 29.9644 |
124
+ | No log | 73.0 | 292 | 2.8791 | 30.3378 |
125
+ | No log | 74.0 | 296 | 2.8790 | 29.8055 |
126
+ | No log | 75.0 | 300 | 2.8794 | 29.0100 |
127
+ | No log | 76.0 | 304 | 2.8795 | 30.7968 |
128
+ | No log | 77.0 | 308 | 2.8790 | 31.5414 |
129
+ | No log | 78.0 | 312 | 2.8783 | 31.5060 |
130
+ | No log | 79.0 | 316 | 2.8775 | 31.4376 |
131
+ | No log | 80.0 | 320 | 2.8766 | 31.6005 |
132
+ | No log | 81.0 | 324 | 2.8767 | 31.3697 |
133
+ | No log | 82.0 | 328 | 2.8769 | 31.6108 |
134
+ | No log | 83.0 | 332 | 2.8770 | 31.4214 |
135
+ | No log | 84.0 | 336 | 2.8772 | 31.6039 |
136
+ | No log | 85.0 | 340 | 2.8776 | 32.0254 |
137
+ | No log | 86.0 | 344 | 2.8779 | 31.4024 |
138
+ | No log | 87.0 | 348 | 2.8783 | 32.0279 |
139
+ | No log | 88.0 | 352 | 2.8786 | 31.8914 |
140
+ | No log | 89.0 | 356 | 2.8788 | 31.6500 |
141
+ | No log | 90.0 | 360 | 2.8791 | 31.7698 |
142
+ | No log | 91.0 | 364 | 2.8793 | 31.6137 |
143
+ | No log | 92.0 | 368 | 2.8793 | 31.8244 |
144
+ | No log | 93.0 | 372 | 2.8790 | 31.5626 |
145
+ | No log | 94.0 | 376 | 2.8786 | 31.3743 |
146
+ | No log | 95.0 | 380 | 2.8785 | 31.4160 |
147
+ | No log | 96.0 | 384 | 2.8784 | 31.6682 |
148
+ | No log | 97.0 | 388 | 2.8782 | 31.8335 |
149
+ | No log | 98.0 | 392 | 2.8782 | 31.7143 |
150
+ | No log | 99.0 | 396 | 2.8782 | 31.7143 |
151
+ | No log | 100.0 | 400 | 2.8782 | 31.7143 |
152
+
153
+
154
+ ### Framework versions
155
+
156
+ - Transformers 4.20.1
157
+ - Pytorch 1.12.0+cu113
158
+ - Datasets 2.3.2
159
+ - Tokenizers 0.12.1