Violet-yo commited on
Commit
0c09f15
1 Parent(s): 9d89a46

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +17 -1
README.md CHANGED
@@ -15,9 +15,25 @@ tags:
15
  </p>
16
 
17
  ## Dataset Description
18
- <!-- 核心要点:原始数据(full tone)怎么做的,怎么去除音调的,现在的数据是保留了10%的音调; -->
 
 
 
 
 
 
 
 
 
19
 
20
  ## Dataset Statistics
 
 
 
 
 
 
 
21
 
22
  ## Resources
23
  - Homepage: [Vision-Braille](https://vision-braille.com/)
 
15
  </p>
16
 
17
  ## Dataset Description
18
+ The `Chinese-Braille-10per-Tone` dataset addresses the scarcity of publicly available Chinese Braille datasets. The original Chinese text data was sourced from the publicly available *Leipzig Corpora Collection*. This dataset consists of one million discrete sentences collected from news media between 2007 and 2009.
19
+
20
+ The Chinese characters from the Leipzig Corpora Collection were converted into "fully toned" Braille using tools from The Braille Online Platform of China. This tool labels the pronunciation of Chinese characters and converts them into Braille using rule-based appraoches. However, the tool sometimes returned misaligned data and garbled text. After cleaning the data and removing duplicates and misaligned sentences, the dataset size was reduced to about 600K sentences.
21
+
22
+ We randomly remove 90% tones from the `Fully Toned Braille Dataset`, resulting in the
23
+ `Chinese-Braille-10per-Tone Dataset`, as real-world usage of Braille omits about 90% of the tones accordindg to the research. To accurately remove tones from the Braille dataset without affecting numbers or punctuation marks, we followed the methoded described below:
24
+
25
+ Tones in Braille are represented as ⠁ (first tone), ⠂ (second tone), ⠄ (third tone), and ⠆ (fourth tone). Numbers are marked with ⠼ (e.g., 1 is ⠼⠁), and punctuation marks have specific patterns (e.g., period: ⠐⠆, question mark: ⠐⠄, exclamation mark: ⠰⠂). We split sentences into words using spaces. Because numbers start with ⠼ and punctuation marks have unique combinations, we could easily identify and exclude them from the removal process. This ensured that only the tone markers were removed.
26
+
27
+ All the source code for the pre-processing is available on the [Github repository](https://github.com/).
28
 
29
  ## Dataset Statistics
30
+ | | \# Sample | Ori. Braille Len. (Mean/Median) String | Ori. Braille Len. (Mean/Median) Token | Braille Len. (Mean/Median) String | Braille Len. (Mean/Median) Token | Chinese Len. (Mean/Median) String | Chinese Len. (Mean/Median) Token |
31
+ |-----------|-----------|--------------------------------------------|--------------------------------------------|----------------------------------|----------------------------------|-----------------------------------|----------------------------------|
32
+ | Training | 525072 | 186/144 | 190/147 | 145/112 | 149/115 | 74/64 | 59/51 |
33
+ | Validation| 65634 | 150/123 | 153/126 | 107/90 | 111/93 | 71/62 | 57/50 |
34
+ | Test | 65634 | 138/116 | 141/119 | 117/96 | 121/99 | 72/63 | 58/50 |
35
+
36
+ This table includes the original Braille lengths (mean and median) for both strings and tokens directly after the sample count.
37
 
38
  ## Resources
39
  - Homepage: [Vision-Braille](https://vision-braille.com/)