metadata
dataset_info:
- config_name: default
features:
- name: label
dtype:
class_label:
names:
'0': '"'
'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': Γ
'28': Δ
'29': Θ
'30': Λ
'31': Ξ
'32': Π
'33': Σ
'34': Υ
'35': Φ
'36': Ψ
'37': Ω
'38': α
'39': β
'40': γ
'41': δ
'42': ε
'43': ζ
'44': η
'45': θ
'46': ι
'47': κ
'48': λ
'49': μ
'50': ν
'51': ξ
'52': π
'53': ρ
'54': ς
'55': σ
'56': τ
'57': υ
'58': φ
'59': χ
'60': ψ
'61': ω
'62': ϑ
'63': ϕ
'64': ϖ
'65': ϰ
'66': ϱ
'67': ϵ
'68': א
'69': –
'70': —
'71': ‖
'72': “
'73': ”
'74': †
'75': ‡
'76': •
'77': …
'78': ‰
'79': ′
'80': ‵
'81': ‸
'82': ‹
'83': ›
'84': ※
'85': ₩
'86': ₫
'87': €
'88': ℂ
'89': ℍ
'90': ℑ
'91': ℓ
'92': ℕ
'93': ℗
'94': ℙ
'95': ℚ
'96': ℜ
'97': ℝ
'98': ™
'99': ℤ
'100': ℧
'101': ←
'102': ↑
'103': →
'104': ↓
'105': ↔
'106': ↕
'107': ↖
'108': ↗
'109': ↘
'110': ↙
'111': ↚
'112': ↛
'113': ↞
'114': ↠
'115': ↢
'116': ↣
'117': ↤
'118': ↦
'119': ↩
'120': ↪
'121': ↫
'122': ↬
'123': ↭
'124': ↮
'125': ↯
'126': ↶
'127': ↷
'128': ↺
'129': ↻
'130': ↼
'131': ↽
'132': ↾
'133': ↿
'134': ⇀
'135': ⇁
'136': ⇂
'137': ⇃
'138': ⇄
'139': ⇆
'140': ⇇
'141': ⇈
'142': ⇉
'143': ⇊
'144': ⇋
'145': ⇌
'146': ⇍
'147': ⇎
'148': ⇏
'149': ⇐
'150': ⇑
'151': ⇒
'152': ⇓
'153': ⇔
'154': ⇕
'155': ⇚
'156': ⇛
'157': ⇝
'158': ⇠
'159': ⇢
'160': ⇽
'161': ⇾
'162': ⇿
'163': ∀
'164': ∁
'165': ∂
'166': ∃
'167': ∄
'168': ∅
'169': ∇
'170': ∈
'171': ∉
'172': ∋
'173': ∎
'174': ∏
'175': ∐
'176': ∑
'177': ∓
'178': ∔
'179': ∖
'180': ∗
'181': ∘
'182': ∙
'183': ∝
'184': ∞
'185': ∠
'186': ∡
'187': ∢
'188': ∣
'189': ∤
'190': ∥
'191': ∦
'192': ∧
'193': ∨
'194': ∩
'195': ∪
'196': ∫
'197': ∬
'198': ∭
'199': ∮
'200': ∯
'201': ∲
'202': ∳
'203': ∴
'204': ∵
'205': ∼
'206': ∽
'207': ≀
'208': ≁
'209': ≃
'210': ≅
'211': ≇
'212': ≈
'213': ≊
'214': ≍
'215': ≒
'216': ≓
'217': ≜
'218': ≠
'219': ≡
'220': ≤
'221': ≥
'222': ≦
'223': ≧
'224': ≨
'225': ≩
'226': ≪
'227': ≫
'228': ≮
'229': ≯
'230': ≰
'231': ≱
'232': ≲
'233': ≳
'234': ≶
'235': ≷
'236': ≺
'237': ≻
'238': ≼
'239': ≽
'240': ≾
'241': ≿
'242': ⊀
'243': ⊁
'244': ⊂
'245': ⊃
'246': ⊆
'247': ⊇
'248': ⊈
'249': ⊉
'250': ⊊
'251': ⊋
'252': ⊎
'253': ⊏
'254': ⊐
'255': ⊑
'256': ⊒
'257': ⊓
'258': ⊔
'259': ⊕
'260': ⊖
'261': ⊗
'262': ⊘
'263': ⊙
'264': ⊚
'265': ⊛
'266': ⊝
'267': ⊞
'268': ⊟
'269': ⊠
'270': ⊡
'271': ⊢
'272': ⊣
'273': ⊤
'274': ⊥
'275': ⊧
'276': ⊨
'277': ⊩
'278': ⊬
'279': ⊭
'280': ⊲
'281': ⊳
'282': ⊴
'283': ⊵
'284': ⊸
'285': ⋀
'286': ⋁
'287': ⋂
'288': ⋃
'289': ⋅
'290': ⋆
'291': ⋇
'292': ⋉
'293': ⋊
'294': ⋋
'295': ⋌
'296': ⋍
'297': ⋎
'298': ⋏
'299': ⋐
'300': ⋑
'301': ⋒
'302': ⋓
'303': ⋖
'304': ⋗
'305': ⋘
'306': ⋙
'307': ⋚
'308': ⋛
'309': ⋞
'310': ⋟
'311': ⋦
'312': ⋧
'313': ⋨
'314': ⋩
'315': ⋬
'316': ⋭
'317': ⋮
'318': ⋱
'319': ⌀
'320': ⌈
'321': ⌉
'322': ⌊
'323': ⌋
'324': ⌢
'325': ⌣
'326': △
'327': ▴
'328': ▵
'329': ▶
'330': ▷
'331': ▽
'332': ▾
'333': ▿
'334': ◀
'335': ◁
'336': ◇
'337': ★
'338': ☉
'339': ♀
'340': ♂
'341': ♠
'342': ♡
'343': ♢
'344': ♣
'345': ♭
'346': ♮
'347': ♯
'348': ✓
'349': ✠
'350': ⟂
'351': ⟅
'352': ⟆
'353': ⟨
'354': ⟩
'355': ⟵
'356': ⟶
'357': ⟷
'358': ⟸
'359': ⟹
'360': ⟺
'361': ⟻
'362': ⟼
'363': ⟽
'364': ⟾
'365': ⤆
'366': ⤇
'367': ⦇
'368': ⦈
'369': ⨀
'370': ⨁
'371': ⨂
'372': ⨄
'373': ⨆
'374': ⨌
'375': ⨏
'376': ⨖
'377': ⨝
'378': ⩽
'379': ⩾
'380': ⪅
'381': ⪆
'382': ⪇
'383': ⪈
'384': ⪉
'385': ⪊
'386': ⪯
'387': ⪰
'388': ⪷
'389': ⪸
'390': ⪹
'391': ⪺
'392': 𝔸
'393': 𝔹
'394': 𝔻
'395': 𝔼
'396': 𝔽
'397': 𝔾
'398': 𝕀
'399': 𝕁
'400': 𝕂
'401': 𝕃
'402': 𝕄
'403': 𝕆
'404': 𝕊
'405': 𝕋
'406': 𝕌
'407': 𝕍
'408': 𝕎
'409': 𝕏
'410': 𝕐
'411': 🜨
- name: strokes
large_list:
large_list:
list: float32
- name: source
dtype: string
splits:
- name: train
num_bytes: 226957986
num_examples: 126926
- name: test
num_bytes: 28418312
num_examples: 15869
- name: val
num_bytes: 27987581
num_examples: 15664
download_size: 196202009
dataset_size: 283363879
- config_name: raw
features:
- name: latex_label
dtype: string
- name: symbol
list:
list:
list: float32
length: 2
- name: source
dtype: string
splits:
- name: data
num_bytes: 272423878
num_examples: 216877
download_size: 129534286
dataset_size: 272423878
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
- config_name: raw
data_files:
- split: data
path: raw/data-*
Training Datasets for Detypify
Main Repository: detypify
This repo contains how to preprocess the raw dataset and the processed data for use with detypify.
The Samples are store in type Strokes shown below:
type Point = tuple[float, float]
type Stroke = list[Point]
type Strokes = list[Strokes]
To preprocess the data
First clone main repo, and follow the instructions below.
Do make sure the data is in the right path defined in python/proc_data.py's constants.
Change the constants as you may want to.
Then, run uv run --extra data python/proc_data.py to preprocess the data.
To get the needed raw dataset
Detexify:
Contains training dataset from detexify.
Source: https://github.com/kirel/detexify-data
Download Link: https://drive.google.com/drive/folders/0ByuYordD0JBRWXUxRlhEMGtfVVE?resourcekey=0-rYksj_Cs11AMqko9p5swdg&usp=sharing
Look for detexify.sql.gz and symbols.json.
detexify.json obtained by
# TODO: Download detexify.sql.gz.
gunzip -c detexify.sql.gz | psql detypify
psql detypify -qAtXc 'SELECT json_agg(json_build_array(key, strokes)) FROM samples' -o detexify.json
rm detexify.sql.gz
# moving data into `build/raw_data`
mv symbols.json build/raw_data/detexify
mv detexify.json build/raw_data/detexify
MathWriting 2024
Description: https://github.com/google-research/google-research/tree/master/mathwriting
Download Link: https://storage.googleapis.com/mathwriting_data/mathwriting-2024.tgz
Contains extracted data from math MathWriting dataset
# only symbols folder are needed
tar xzvf mathwriting-2024.tgz mathwriting-2024/symbols
mv mathwriting-2024/symbols build/raw_data/mathwriting