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chore: readme a bit

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2025-02-09 04:04:55 +06:00
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commit 94babaa7aa
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1
.gitignore vendored
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epubs/ epubs/
*.db *.db
*.db.bak
train.en train.en
train.zh train.zh

35
README.md
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# chinese -> english finetuning datasets # chinese -> english finetuning datasets
train.en and train.zh are from [here](https://www.dropbox.com/scl/fo/dtrf3pe1vfbo5nse16648/ANLqlv3ascANpkdnYF_w4Jk/V1/TRAIN?dl=0&rlkey=486vbn17qra1ez91btj0n4xu2&subfolder_nav_tracking=1) train.en and train.zh are from [here](https://www.dropbox.com/scl/fo/dtrf3pe1vfbo5nse16648/ANLqlv3ascANpkdnYF_w4Jk/V1/TRAIN?dl=0&rlkey=486vbn17qra1ez91btj0n4xu2&subfolder_nav_tracking=1)
TODO: mirror the [actual dataset and .sqlite file](https://mega.nz/folder/byoFHRST#Mcn6-mU5spHxPg0nMlRS3w)
It's missing the epubs dir I used for paragraph rebuilding... I accidentally deleted the dir, sorry :c
What I did was Google a sentence from the chapter 1 of a novel and just scrape 50-60 chapters from either Webnovel or some aggregator, then unzip it into epub with the directory name set to `book_id`.
GuoFeng dataset chapter spread:
```sql
select book_id, count(*) as chapter_count
from chapters
group by book_id
order by chapter_count desc;
```
| book_id | chapter_count |
| -------------- | ------------- |
| 45-jsys | 2262 |
| 93-yzsslfmmd | 1733 |
| 2-xzltq | 1718 |
| 19-ysmmjwn | 1546 |
| 52-mfwz | 1254 |
| 86-wzxajddyx | 1188 |
| 34-xwdrcsh | 1172 |
| 25-dgfsngm | 942 |
| 53-gmzz | 798 |
| 6-yh1frhjqjysy | 763 |
| 141-fyyysndy | 745 |
| 37-scrj | 539 |
| 95-cjjyyhy | 516 |
| 99-jjl | 220 |
There are 21 more with 60chs and the rest are 50 or less.
However, I didn't import many epubs, there are 153 books in the dataset in total and the most important part about [GuoFeng-Webnovel
](https://github.com/longyuewangdcu/GuoFeng-Webnovel) dataset is the Chinese raws and more or less _decent_ mapping between paragraphs (there are some mistakes which sucks). I used 19 epubs and not many of the paragraphs actually matched.

154
docs/guofeng_chapter_spread.csv Normal file
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book_id,chapter_count
45-jsys,2262
93-yzsslfmmd,1733
2-xzltq,1718
19-ysmmjwn,1546
52-mfwz,1254
86-wzxajddyx,1188
34-xwdrcsh,1172
25-dgfsngm,942
53-gmzz,798
6-yh1frhjqjysy,763
141-fyyysndy,745
37-scrj,539
95-cjjyyhy,516
99-jjl,220
100-jdxx,100
149-ajnszwj9csan,100
151-gfsy,100
152-dwyx,100
153-ldyb98k,100
154-nsxhn,100
155-dlgl,60
157-bfrgbfn,60
158-myqc,60
159-mrzj,60
160-dsgyzdn,60
161-nssjwydgm,60
162-wajxs,60
163-sjsdhgwz,60
164-gxswa,60
165-ysxw,60
167-wdbz,60
169-wgdd,60
170-fqmy,60
171-cshtclwrm,60
172-ymyx,60
173-cssyxtq,60
174-nswjbddxd,60
175-frnmjydl,60
176-jtxbygy,60
177-syfjytlnl,60
178-cszknxs,60
179-jxdn,60
1-jlws,50
10-ssdyzsy,50
101-wclxzsz,50
104-msdhl,50
11-tdtsg,50
113-dzysxt,50
114-wyzjzfs,50
115-kjqdhgst,50
116-yhzx,50
121-dfwxqwdlgsmw,50
124-jdltxs！,50
125-nndjjgjjlzt,50
127-dylr,50
13-sfydxl,50
130-zshwclqcdzzj,50
132-gfyxjdcz！,50
133-sjsdyx,50
136-wyzsjlys,50
137-zd,50
139-cfdw,50
140-yjqzyds,50
145-wyzqmlz,50
147-wnyxs,50
16-cjsjy,50
18-jdj,50
20-zqdxt,50
21-zzjh,50
24-xtdhkjwb,50
29-lwdnx,50
3-srj,50
33-yxnq,50
35-kbds,50
36-wdyzhgj,50
39-dwrm,50
4-mrly,50
40-jbw,50
41-sjgl,50
42-fxsppxhn,50
46-yghknct,50
48-jcsw,50
50-nswdllbw,50
51-zlhz,50
54-cywlznrbhd,50
55-dxcwj,50
56-nbdyjct,50
59-ywxcbjn,50
61-htzb,50
64-sh,50
65-ywsczwn,50
69-txdj,50
7-sm,50
70-wdlyzclp,50
71-hmjqzsxysn,50
72-bslrnz,50
75-sghndhm,50
76-dyxl,50
77-ndjdxbl,50
78-ddz,50
8-csjms,50
80-ayjy,50
87-wdyty4xs,50
88-wyhdqql,50
89-wddshss,50
9-msgys,50
91-wxdknsjx,50
94-yssjdst,50
102-bgwzsl,49
103-kc1s,49
106-wdzsmjyx,49
109-ljbyg,49
118-wdzhwkyxjn,49
119-fszl,49
122-yh1fzzsxbns,49
17-xzfb,49
27-gmlgdhj,49
31-yxxgzyscks,49
43-lnyhs,49
67-scjh,49
68-mcxw,49
96-dqrsztxml,49
98-wyctUp,49
111-xldg,48
12-wskj,48
134-kcsfcyxks,48
135-czyxsj,48
22-ltzz,48
28-xjnsswb,48
30-kt,48
47-gjjf,48
58-xbdhkjxt,48
73-ylwsyed,48
74-ajnsnxb！,48
81-tywgjqc,48
129-gwltq,47
148-hlwzw,47
97-jmtj,47
38-wbxttgl,46
1v1h,44
85-xsjqlwq,43
92-nxsmmxhn,43
146-zlgk,41
120-yzjd,40
117-sgjy,39
123-whrqysj,39
131-cyyh,39
142-wyzwss,39
143-qnyxsjs,39
84-cqsyjgxgtaj,39
138-zhyd,37
14-ftjq,11
126-mwbbbdd,4
1 book_id chapter_count
2 45-jsys 2262
3 93-yzsslfmmd 1733
4 2-xzltq 1718
5 19-ysmmjwn 1546
6 52-mfwz 1254
7 86-wzxajddyx 1188
8 34-xwdrcsh 1172
9 25-dgfsngm 942
10 53-gmzz 798
11 6-yh1frhjqjysy 763
12 141-fyyysndy 745
13 37-scrj 539
14 95-cjjyyhy 516
15 99-jjl 220
16 100-jdxx 100
17 149-ajnszwj9csan 100
18 151-gfsy 100
19 152-dwyx 100
20 153-ldyb98k 100
21 154-nsxhn 100
22 155-dlgl 60
23 157-bfrgbfn 60
24 158-myqc 60
25 159-mrzj 60
26 160-dsgyzdn 60
27 161-nssjwydgm 60
28 162-wajxs 60
29 163-sjsdhgwz 60
30 164-gxswa 60
31 165-ysxw 60
32 167-wdbz 60
33 169-wgdd 60
34 170-fqmy 60
35 171-cshtclwrm 60
36 172-ymyx 60
37 173-cssyxtq 60
38 174-nswjbddxd 60
39 175-frnmjydl 60
40 176-jtxbygy 60
41 177-syfjytlnl 60
42 178-cszknxs 60
43 179-jxdn 60
44 1-jlws 50
45 10-ssdyzsy 50
46 101-wclxzsz 50
47 104-msdhl 50
48 11-tdtsg 50
49 113-dzysxt 50
50 114-wyzjzfs 50
51 115-kjqdhgst 50
52 116-yhzx 50
53 121-dfwxqwdlgsmw 50
54 124-jdltxs！ 50
55 125-nndjjgjjlzt 50
56 127-dylr 50
57 13-sfydxl 50
58 130-zshwclqcdzzj 50
59 132-gfyxjdcz！ 50
60 133-sjsdyx 50
61 136-wyzsjlys 50
62 137-zd 50
63 139-cfdw 50
64 140-yjqzyds 50
65 145-wyzqmlz 50
66 147-wnyxs 50
67 16-cjsjy 50
68 18-jdj 50
69 20-zqdxt 50
70 21-zzjh 50
71 24-xtdhkjwb 50
72 29-lwdnx 50
73 3-srj 50
74 33-yxnq 50
75 35-kbds 50
76 36-wdyzhgj 50
77 39-dwrm 50
78 4-mrly 50
79 40-jbw 50
80 41-sjgl 50
81 42-fxsppxhn 50
82 46-yghknct 50
83 48-jcsw 50
84 50-nswdllbw 50
85 51-zlhz 50
86 54-cywlznrbhd 50
87 55-dxcwj 50
88 56-nbdyjct 50
89 59-ywxcbjn 50
90 61-htzb 50
91 64-sh 50
92 65-ywsczwn 50
93 69-txdj 50
94 7-sm 50
95 70-wdlyzclp 50
96 71-hmjqzsxysn 50
97 72-bslrnz 50
98 75-sghndhm 50
99 76-dyxl 50
100 77-ndjdxbl 50
101 78-ddz 50
102 8-csjms 50
103 80-ayjy 50
104 87-wdyty4xs 50
105 88-wyhdqql 50
106 89-wddshss 50
107 9-msgys 50
108 91-wxdknsjx 50
109 94-yssjdst 50
110 102-bgwzsl 49
111 103-kc1s 49
112 106-wdzsmjyx 49
113 109-ljbyg 49
114 118-wdzhwkyxjn 49
115 119-fszl 49
116 122-yh1fzzsxbns 49
117 17-xzfb 49
118 27-gmlgdhj 49
119 31-yxxgzyscks 49
120 43-lnyhs 49
121 67-scjh 49
122 68-mcxw 49
123 96-dqrsztxml 49
124 98-wyctUp 49
125 111-xldg 48
126 12-wskj 48
127 134-kcsfcyxks 48
128 135-czyxsj 48
129 22-ltzz 48
130 28-xjnsswb 48
131 30-kt 48
132 47-gjjf 48
133 58-xbdhkjxt 48
134 73-ylwsyed 48
135 74-ajnsnxb！ 48
136 81-tywgjqc 48
137 129-gwltq 47
138 148-hlwzw 47
139 97-jmtj 47
140 38-wbxttgl 46
141 1v1h 44
142 85-xsjqlwq 43
143 92-nxsmmxhn 43
144 146-zlgk 41
145 120-yzjd 40
146 117-sgjy 39
147 123-whrqysj 39
148 131-cyyh 39
149 142-wyzwss 39
150 143-qnyxsjs 39
151 84-cqsyjgxgtaj 39
152 138-zhyd 37
153 14-ftjq 11
154 126-mwbbbdd 4

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paragraph_split_custom_zh.py
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def extract_zh_paragraphs(soup: BeautifulSoup) -> List[str]: def extract_zh_paragraphs(soup: BeautifulSoup) -> List[str]:
"""extract paragraphs from zh HTML""" """
most chinese raws are split with 2 br tags rather than
by <p> elements so.. yeah
"""
if h1_tag := soup.find("h1"): if h1_tag := soup.find("h1"):
h1_tag.decompose() h1_tag.decompose()