LMing-Rules baseline¶

Using language modeling.
Using rules, extracted from the training data.

To get the data.

In [ ]:

%%capture
!pip install aicrowd-cli
%load_ext aicrowd.magic

In [2]:

%aicrowd login

Please login here: https://api.aicrowd.com/auth/ZRK_SW7MLRKWe-kcH8erAnLc3_CSYNb2He_hcLv9HdU
API Key valid
Gitlab access token valid
Saved details successfully!

In [3]:

!rm -rf data
!mkdir data
%aicrowd ds dl -c htrec-2022 -o data

In [ ]:

%%capture
!pip install pywer
import pywer
import pandas as pd
import numpy as np
import os

In [12]:

train = pd.read_csv("data/train.csv")
test = pd.read_csv("data/test.csv")
print(f"{train.shape[0]} train and {test.shape[0]} instances"); train.sample()

1875 train and 338 instances

Out[12]:

	HUMAN_TRANSCRIPTION	SYSTEM_TRANSCRIPTION	CENTURY	IMAGE_PATH	TEXT_LINE_NUM
246	οιονει και την υλην την κοσμουσαν και αυτην	διόν εικαὶ τὴν χην την κοσμουσαν και αυτη	11	9 Bodleian-Library-MS-Barocci-102_00165_fol-79...	8

LMing¶

Using the human-transcribed data to train a statistical character language model.

In [15]:

!git clone https://github.com/ipavlopoulos/lm.git
from lm.markov.models import LM
lm = LM(gram="CHAR").train(train.HUMAN_TRANSCRIPTION.sum()); #cslm.generate_text()
lm.generate_text()

Out[15]:

'ἐγγινομενου σρς ἐχθρούς. Εἰ πάντας φιλοπαίγμονα των αὐτῷ, ὡς τῶν λόγῳἔστ᾽, ἐπεὶ νεκρόν· οὐκ ἴσχυσαν ἅπαντός ὀνόματος και αλλωπισμου πόδακαί ὑπό τόν Θεόνὄντα δυναστενόν δέ μοι το αιδοτριβενταυθα μὲν περιεργοῖς, οὓς ἔκτειν᾽ ἀπεικαὶ τύχας σριαςυποθεν ην παρ᾽ οὐδὲν τῶν φράσονται. Τουτέστι, προσεται, καὶ ὑμεῖς πῶς λέβητα ὑποκαιόμενον τό δέξασθαι δύ᾽ ἢ τῆς ἐργάσεται εφοβουαφαιρεσεως πότεπρουθέμην βασιλειαν εμε μιμουμένῃδεδωκοτας ἕλκει· εἶτ᾽ ἀνὰ λειμῶναΧορεύσω και σάρκα κατὰ τοῦ παλαιαὶ Πριαμιδῶν και ουκ ανποτε πέλαβερᾶς και εναντιας του γλωττηςΒακχε,νυκτερηθήσοντα ποιησεν Ἰλιάδ᾽ ἄν τι;δεινόν μ᾽ ἀπειλε Σαοὺλ, καὶ τοῦ ΝαυῆἸησοῦς ἔνεστανττω και προς και παντας ἐλάβοι πλησας ὧν σε ἔκβαλεῖς κόρησεμνον, καθως εἰς τόν σ᾽ ἀνθεμώδεις, ἤνεγκαναὐτὸς ὑμὶν ἐξ Αἰγύπτου και το ποιος και εκαστον αὐτοῖσι τραποίμην προσθοῦ. Εὐφραίνειν ει γαρ ετι τα ιματια υμωντου φανες ησπαζοντες ολην αυτάὁ Ξανθίαν, βοήθησομένην, καὶ σὺ, Κε.Οὐδὲν μακάριος Δαβὶδ, πρόσθε ον φίλταται· ἐγὼπάλαινα μαθηται· ἐγὼμόνος ἄκανθανάτῳ οὐκ '

Frequently mistaken tokens¶

In [13]:

LEX = train.HUMAN_TRANSCRIPTION.sum().split()

In [18]:

from collections import Counter
broken_words = [w for w in train.SYSTEM_TRANSCRIPTION.sum().split() if w not in set(LEX)]
x,y = zip(*Counter(broken_words).most_common(10))
pd.DataFrame({"mistaken":x, "occurrences":y}).plot.barh(x="mistaken");

Picking one, one that can be fixed without much ambiguity.

In [19]:

train[train.SYSTEM_TRANSCRIPTION.str.contains(" ς ")].sample(2)

Out[19]:

	HUMAN_TRANSCRIPTION	SYSTEM_TRANSCRIPTION	CENTURY	IMAGE_PATH	TEXT_LINE_NUM
595	μηρ δ᾽ ἐν οἴκοις· ἣν σὺ μὴ δείσῃς ποθ᾽ ὡς	ξυ ρομι ς ὡς υτῦ δήσεινταρχας γ	16	44 Bodleian-Library-MS-Barocci-66_00318_fol-15...	7
1156	ως· ὡς αὐτοῦ ὄντος τοῦ Χῦ και ῥήμα	ως ω ς αὐτοῦ ον πος τοῦ χυ καιρήε,	15	77 Bodleian-Library-MS-Barocci-59_00082_fol-45...	7

The method¶

Exploring the mistakes, two are the easier fixes: merge with the previous word (i.e., this is a final character, which makes sense) or delete. To pick out of the two, we ask the LM.

In [22]:

def lmr(text, word=" ς ", replacements=["ς ", " "], lm=lm):
    scores = []
    for the_candidate in replacements:
        scores.append(lm.cross_entropy(text.replace(word, the_candidate)))
    text_out = text.replace(word, replacements[scores.index(min(scores))])
    return text_out
    
lmr("ως ω ς αὐτοῦ ον πος τοῦ χυ καιρήε,")

Out[22]:

'ως ως αὐτοῦ ον πος τοῦ χυ καιρήε,'

Predict and submit¶

In [23]:

R1 = test.SYSTEM_TRANSCRIPTION.apply(lmr)

In [27]:

submission = pd.DataFrame(zip(test.IMAGE_PATH, R1), columns=["ImageID", "Transcriptions"])
submission.sample()

Out[27]:

	ImageID	Transcriptions
2	66 Bodleian-Library-MS-Barocci-127_00136_fol-66v	συ σ κατεχισωμε ἐπιτελωτι

In [25]:

submission.to_csv("submission.csv", index=False)

In [26]:

%aicrowd submission create -c htrec-2022 -f submission.csv

                                    ╭─────────────────────────╮                                     
                                    │ Successfully submitted! │                                     
                                    ╰─────────────────────────╯

                                          Important links                                           
┌──────────────────┬───────────────────────────────────────────────────────────────────────────────┐
│  This submission │ https://www.aicrowd.com/challenges/htrec-2022/submissions/187737              │
│                  │                                                                               │
│  All submissions │ https://www.aicrowd.com/challenges/htrec-2022/submissions?my_submissions=true │
│                  │                                                                               │
│      Leaderboard │ https://www.aicrowd.com/challenges/htrec-2022/leaderboards                    │
│                  │                                                                               │
│ Discussion forum │ https://discourse.aicrowd.com/c/htrec-2022                                    │
│                  │                                                                               │
│   Challenge page │ https://www.aicrowd.com/challenges/htrec-2022                                 │
└──────────────────┴───────────────────────────────────────────────────────────────────────────────┘

{'submission_id': 187737, 'created_at': '2022-06-06T11:41:37.986Z'}

In [ ]:

HTREC 2022

LMing/Rules

LMing-Rules baseline¶

LMing¶

Frequently mistaken tokens¶

The method¶

Predict and submit¶

Content

Comments

HTREC 2022

LMing/Rules

LMing-Rules baseline¶

Sign in¶

LMing¶

Frequently mistaken tokens¶

The method¶

Predict and submit¶

Content