This blog post is a tutorial on creating a CSV file to import into Anki using Tatoeba sentences, a frequency list, and pre-downloaded Wiktionary entries.

You can find the frequency dictionary file (freqdict.csv) here and the Tatoeba sentences directly from their website.

Even though this tutorial is specifically aimed at Russian, you can easily adapt it to any other language with sufficient Tatoeba sentences, or any bilingual dataset of sufficient quality.

If you need any help in adapting this to your language pair, ask us on the Matrix chatroom linked on the side bar.

Formatting explanation

Code looks like this
Output looks like this. (Tables are hand-formatted specially)

Note: Progress bars don’t display properly here, so they show as 0% even though they are all complete.

Note 2: The definition section in tables should be in HTML, but it is not displayed here.

Import common libraries

We are disabling warnings so that pandas would not complain about using slice operators (the warned problem do not pose an issue to this use case, and fixing them will make code more verbose)

import warnings
import pandas as pd
import pymorphy2
from tqdm.notebook import tqdm

Lemmatization setup

Here we are using PyMorphy2, which works for Russian and experimentally Ukrainian. For other languages, you can look into SpaCy. If your language does not have inflection, simply replace the code under the function lem_text with return text.split()

morph = pymorphy2.MorphAnalyzer(lang="ru")
def lemmatize(word):
    "Lemmatize a word"
    return morph.parse(word)[0].normal_form

def lem_text(text):
    "Lemmatize a sentence and return a list"
    return [lemmatize(word) for word in text.split() if word]

lem_text("Я продолжаю получать спам по электронной почте.")
['я', 'продолжать', 'получать', 'спам', 'по', 'электронный', 'почте.']

Load CSV file from Tatoeba

IMPORTANT: You need to check if they are in the right order. You may have to swap the order of “en” and “tl” tags.

d = pd.read_csv('../langdata/sentences/en_ru.tsv', sep='\t', header=None, names=['id1', 'ru', 'id2', 'en'])
d = d[['ru', 'en']]
sample = d.sample(1000)
# Make sure this looks right. If not, swap the order in the second last
# cell, be sure to leave the double brackets as is.
ru en
327924 Тебе не нужно извиняться перед Томом. You don’t need to apologize to Tom.
481741 Есть ли у тебя вспышка, которую ты можешь мне одолжить? Do you have a flashlight I could borrow?
564452 Том не может это есть. Tom can’t eat that.
289114 Она умерла в своей постели в возрасте 96 лет. She died in her bed at the age of 96.
192477 Три в кубе будет двадцать семь. Three cubed is twenty-seven.
def remove_punctuations(l):
    return [item for item in l if item.isalpha()]
def getword(freq):
    return fl.loc[freq]['Lemma']

Reading the frequency dictionary

This must be processed and be fully lemmatized, or else the result might be poor.

This list must not contain any duplicates.

Note that the definition column is in HTML, but they cannot be displayed here.

fd = pd.read_csv("freqdict.csv", index_col="Unnamed: 0")[:20000]
fd2 = fd.reset_index().set_index("Lemma")
# Make sure this looks right
Lemma index definition
булыжник 12817 Noun1. cobble, cobblestone
легонько 12925 Adverb1. lightly2. easily, facilely, slightly3. luckilyAdjective1. lightly2. easily, facilely, slightly3. luckily
акционер 3059 Noun1. shareholder (one who owns shares of stock)
заведомо 7356 Adverb1. deliberately, intentionally, knowingly2. obviously
неуловимый 10027 Adjective1. elusive, difficult to catch2. imperceptible, subtle
напасть 15351 Noun1. misfortune, bad luck, troubleVerb1. misfortune, bad luck, trouble
погибший 7044 Verb1. to perish, to die of unnatural causes, to be killed
накинуть 8507 Verb1. to throw on, to throw over2. to add
привлекательность 9626 Noun1. attractiveness, appeal
столовый 6111 Adjective1. table2. dinner

Dictionary lookup function

We are using a pre-downloaded version of formatted English Wiktionary entries.

You can also use other dictionary formats, like a plain json or another CSV. If you choose to do so, you must reimplement the function below. The return value should be a string, either plaintext or with HTML tags.

def get_def(word):
        return fd2.loc[word]['definition']
    except Exception:
        return None
'<i>Verb</i><br>1. to throw on, to throw over<br>2. to add'

Coverage testing functions

The first function tests the overall coverage, i.e. how much of the vocabulary does the entire set of sentences cover. The second function tests word coverage, i.e., how many words have their own cards.

The overall coverage is displayed as a percentage of the frequency list up to the specific level, whereas the word coverage is displayed as a raw number of words.

def test_coverage(data, upto=2000, downto=200):
    vocab = set.union(*data['lemmas'].apply(set).tolist())
    words = set(fl.iloc[downto:upto]['Lemma'])
    inter = vocab.intersection(words)
    covered_ratio = len(inter) / (upto - downto)
    return covered_ratio
def test_word_coverage(data, upto=2000, downto=200):
    vocab = set(data[data.difficulty < upto][data.difficulty > downto]['target'].tolist())
    words = set(fl['Lemma'])
    inter = vocab.intersection(words)
    return len(inter)
def test_cov_levels(data):
    a = [2000, 5000, 10000, 15000]
    b = [format(100 * test_coverage(data, level), "2.1f") + "%" for level in a]
    c = [format(test_word_coverage(data, level)) for level in a]
    return pd.DataFrame({"Level": a, "Words": c, "Coverage%": b}).set_index("Level")

Getting missing words

def get_missings(data, upto=5000, downto=200):
    vocab = set.union(*data['lemmas'].apply(set).tolist())
    words = set(fl.iloc[downto:upto]['Lemma'])
    inter = vocab.intersection(words)
    return words - inter
def format_lemmas(lemmas):
    return " · ".join(lemmas)

Sentence difficulty function

This function takes a list of words and returns the index of the word ranked the highest on the frequency list (“the hardest word”)

This ideally would guarantee that you would never study a word before you know all the other ones, but in practice does not work perfectly, though you should still have single-unknown sentences for the most part if you study them in order.

def get_dif(l):
    if len(res:=fl2.reindex(l)['index'].fillna(0)) != 0:
        return int(max(res))
        return 0

Processing function

This does the first two steps, which are lemmatization and calculating difficulty.

def process_sentences(data):
    print("STEP 1: Lemmatization")
    data.loc[:, "lemmas"] = data['ru'].progress_apply(lem_text)
    data.loc[:, 'lemmas'] = data['lemmas'].apply(remove_punctuations)
    print("STEP 2: Calculating frequency scores")
    data.loc[:, 'difficulty'] = data['lemmas'].progress_apply(get_dif)
    data.loc[:, 'lemmas_formatted'] = data['lemmas'].apply(format_lemmas)
    data = data[data['difficulty'] < 15000]
    return data.sort_values('difficulty')

Sentence complement

The downside of taking sentences solely by the most difficult word is that it will miss a lot of useful sentences, especially those with an unknown word, but with a more difficult word somewhere in it.

We first deduplicate the dataset, so that each word only has one sentence for it. Then, for each missing word, we try to find the easiest sentence that contains it, and we modify the difficulty of that sentence by +0.5 so that it will be studied after the sentence that targets the highest-ranked word is studied, ensuring as many cards as possible are single-unknown. We add these sentences back to the list.

def get_complemented(processed):
    First deduplicate the sentences, and then add sentences back to enhance the range
    The easiest sentence that has a missing word is picked, and it's difficulty is 0.5 plus the difficulty of that word
    so that it will be studied only after the highest ranked word is already studied.
    print("STEP 4: Deduplication")
    print("  Size of processed data: ", len(processed))
    norepeat = processed.drop_duplicates("difficulty")
    print("  Size of deduplicated data: ", len(norepeat))
    print("  Coverage of deduplicated data:")
    missings = get_missings(norepeat) - get_missings(processed)
    result = pd.DataFrame()
    print("STEP 5: Finding complement")
    for word in missings:
        row = processed[processed.lemmas_formatted.str.contains(word)].reset_index(drop=True).reindex([0]).loc[0]
        row['target'] = word
        row['difficulty'] = row['difficulty'] + 0.5
        result = result.append(row)
    result = result[["en", "ru", "lemmas", "difficulty", "lemmas_formatted", "target"]]
    complement = result.dropna()
    enhanced = norepeat.append(complement)
    print("\tSize of enhanced data: ", len(enhanced))
    return enhanced.sort_values('difficulty').reset_index(drop=True)
def add_targets(data):
    print("STEP 3: Add targets to sentences")
    data = data[data.difficulty > 200]
    data['target'] = data["difficulty"].apply(getword)
    return data
def lookup_defs(data):
    print("STEP 6: Look up definitions")
    data.loc[:, "definition"] =
    return data
def full_processing(data):
    output = process_sentences(data)
    output = add_targets(output)
    output = get_complemented(output)
    output = lookup_defs(output)
    n_def = len(output.definition.notna())
    print(n_def, " definitions found.")
    output = output.drop("lemmas", axis=1)
    return output


Now all the relevant functions have been defined. We can start with a small sample to check for errors before running it on the full list.

#Calculate a sample first to check for errors. If this runs successfully, you may use the full list.
sample = d.sample(100000)
sm = full_processing(sample)
STEP 1: Lemmatization

  0%|          | 0/100000 [00:00<?, ?it/s]

STEP 2: Calculating frequency scores

  0%|          | 0/100000 [00:00<?, ?it/s]

STEP 3: Add targets to sentences
Level Words Coverage%
2000 1339 85.5%
5000 3090 73.7%
10000 4824 55.3%
15000 5871 43.8%
STEP 4: Deduplication
  Size of processed data:  62499
  Size of deduplicated data:  5871
  Coverage of deduplicated data:
Level Words Coverage%
2000 1339 81.4%
5000 3090 69.9%
10000 4824 52.8%
15000 5871 42.2%
STEP 5: Finding complement
	Size of enhanced data:  6054
Level Words Coverage%
2000 1373 85.2%
5000 3186 73.3%
10000 4989 54.6%
15000 6054 43.3%
STEP 6: Look up definitions

  0%|          | 0/6054 [00:00<?, ?it/s]

6054  definitions found.
ru en difficulty lemmas_formatted target definition
0 Я дал Тому возможность это сделать. I gave Tom a chance to do that. 201 я · дать · тот · возможность · это возможность Noun1. opportunity, chance, potential (chance for advancement, progress or profit)2. possibility
1 Каких результатов ты ждёшь? What results do you anticipate? 202 какой · результат · ты результат Noun1. effect, result, return, outcome
2 Ночь была холодной. It was a cold night. 203 ночь · быть ночь Noun1. night
3 Стол в комнате. The table is in the room. 204 стол · в стол Noun1. table2. board, fare, cuisine3. department, section, office, bureau4. throne
4 Ты никогда её не найдёшь. You’ll never find her. 205 ты · никогда · её · не никогда Adverb1. never, nevermore (entails the meaning “always” or “for now on”)

Full processing

This will take quite some time, depending on the power of your computer. If you have a much bigger database, you might want to consider using multiprocessing for the get_dif and lemmatize functions. The library pandarallel is a good choice for this.

full = full_processing(d)