analyze language identification models performance on

short ingredient texts with precision-recall evaluation (openfoodfacts#349)

language_identification/pyproject.toml

@@ -0,0 +1,30 @@
+[tool.poetry]
+name = "language_identification"
+version = "0.1.0"
+description = ""
+authors = ["Yulia Zhilyaeva"]
+readme = "./scripts/README.md"
+package-mode = false
+
+[tool.poetry.dependencies]
+python = "^3.12"
+pandas = "^2.2.3"
+tqdm = "^4.67.0"
+datasets = "^3.1.0"
+joblib = "^1.4.2"
+openfoodfacts = "^2.1.0"
+polyglot = "^16.7.4"
+numpy = "^2.1.3"
+langcodes = "^3.4.1"
+scikit-learn = "^1.5.2"
+fasttext = "^0.9.3"
+lingua-language-detector = "^2.0.2"
+huggingface-hub = "^0.26.2"
+pyicu = "^2.14"
+pycld2 = "^0.41"
+morfessor = "^2.0.6"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

language_identification/scripts/01_extract_data.py

@@ -0,0 +1,28 @@
+import os
+from pathlib import Path
+import pandas as pd
+from datasets import load_dataset
+
+
+def main():
+    # extracting all texts with their languages from huggingface dataset
+
+    # path where to save selected data
+    dataset_file = os.path.join(Path(__file__).parent, "texts_with_lang.csv")
+
+    hf_dataset = load_dataset("openfoodfacts/product-database", split=f"food")
+
+    data = set()
+    for entry, main_lang in zip(hf_dataset["ingredients_text"], hf_dataset["lang"]):  # iterate over products
+        for product_in_lang in entry:
+            if product_in_lang["text"]:
+                lang = main_lang if product_in_lang["lang"] == "main" else product_in_lang["lang"]
+                data.add((product_in_lang["text"], lang))
+
+    df = pd.DataFrame(data, columns=["ingredients_text", "lang"])
+    df.dropna(inplace=True)
+    df.to_csv(dataset_file, index=False)
+
+
+if __name__ == "__main__":
+    main()

language_identification/scripts/02_select_short_texts_with_known_ingredients.py

@@ -0,0 +1,85 @@
+import os
+from pathlib import Path
+import unicodedata
+import pandas as pd
+from tqdm import tqdm
+from openfoodfacts import API, APIVersion, Environment
+from polyglot.text import Text
+
+
+def is_punctuation(word: str) -> bool:
+    """
+    Check if the string `word` is a punctuation mark.
+    """
+    return all(unicodedata.category(char).startswith("P") for char in word)
+
+
+def select_texts_by_len(data: pd.DataFrame, min_len: int, max_len: int) -> pd.DataFrame:
+    """
+    Select rows from `data` where the number of words in `ingredients_text` 
+    is between `min_len` and `max_len` inclusively, excluding punctuation.
+
+    Args:
+        data: pandas.DataFrame with columns `ingredients_text`, `lang`
+        min_len: Minimum number of words.
+        max_len: Maximum number of words.
+
+    Returns:
+        A pandas DataFrame containing the rows from the `data` that satisfy the word count condition.
+    """
+    selected_rows = []
+
+    for _, row in tqdm(data.iterrows(), total=len(data)):
+        # the object that recognizes individual words in text
+        text = Text(row.ingredients_text, hint_language_code=row.lang)
+        # `Text` recognizes punctuation marks as words
+        words = [word for word in text.words if not is_punctuation(word)]
+        if min_len <= len(words) <= max_len:
+            selected_rows.append(row)
+
+    selected_df = pd.DataFrame(selected_rows)
+    return selected_df
+
+
+def main():
+    dataset_file = os.path.join(Path(__file__).parent, "texts_with_lang.csv")
+    all_data = pd.read_csv(dataset_file)
+
+    short_texts_df = select_texts_by_len(all_data, min_len=0, max_len=10)
+
+    # perform ingredient analysis
+    api = API(user_agent="langid",
+              version=APIVersion.v3,
+              environment=Environment.net)
+
+    threshold = 0.8
+    texts_with_known_ingredients = []
+
+    # select ingredients texts with the rate of known ingredients >= `threshold`
+    for i, row in tqdm(short_texts_df.iterrows(), total=len(short_texts_df)):
+        try:
+            ingredient_analysis_results = api.product.parse_ingredients(row.ingredients_text, lang=row.lang)
+        except RuntimeError:
+            continue
+
+        is_in_taxonomy = sum(dct.get("is_in_taxonomy", 0) for dct in ingredient_analysis_results)
+        is_in_taxonomy_rate = is_in_taxonomy / len(ingredient_analysis_results) \
+            if len(ingredient_analysis_results) > 0 else 0.
+
+        if is_in_taxonomy_rate >= threshold:
+            texts_with_known_ingredients.append(row)
+
+    texts_with_known_ingredients_df = pd.DataFrame(texts_with_known_ingredients)
+
+    # add short texts from manually checked data
+    manually_checked_data = pd.read_csv(os.path.join(Path(__file__).parent, "manually_checked_data.csv"))
+    short_texts_manual = select_texts_by_len(manually_checked_data, min_len=0, max_len=10)
+
+    # combine data and save
+    all_texts_under_10_words = pd.concat((short_texts_manual, texts_with_known_ingredients_df), ignore_index=True)
+    all_texts_under_10_words.drop_duplicates(inplace=True, ignore_index=True)
+    all_texts_under_10_words.to_csv(os.path.join(Path(__file__).parent, "texts_under_10_words.csv"), index=False)
+
+
+if __name__ == "__main__":
+    main()

language_identification/scripts/03_calculate_metrics.py

@@ -0,0 +1,149 @@
+import os
+from pathlib import Path
+import numpy as np
+import pandas as pd
+from tqdm import trange
+from collections.abc import Sequence
+
+import langcodes
+from sklearn.metrics import confusion_matrix
+import fasttext
+from lingua import LanguageDetectorBuilder
+from huggingface_hub import hf_hub_download
+
+
+def replace_lang_code(model_predictions: list[str], mapping: dict[str, str]) -> None:
+    """
+    Replace predicted language codes in the `model_predictions` list
+    using `mapping`, where:
+      - Keys represent the original language codes (predicted by the model)
+      - Values represent the target language codes to replace them with.
+
+    The purpose of this function is to standardize language codes
+    by combining multiple variants of the same language into a unified code
+    in order to match supported languages.
+    """
+    for i in trange(len(model_predictions)):
+        if model_predictions[i] in mapping:
+            model_predictions[i] = mapping[model_predictions[i]]
+
+
+def get_fasttext_predictions(texts: list[str]) -> list[str]:
+    """
+    Detect the languages of the given texts using FastText model.
+
+    Args:
+        texts: A sequence of texts for which the language detection
+               needs to be performed.
+
+    Returns:
+        A list of predicted ISO 639-1 language codes.
+    """
+    # texts preprocessing
+    table = str.maketrans({"\n": " ", "\r": " "})
+    texts = [text.translate(table).lower() for text in texts]
+
+    # load model
+    fasttext_model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
+    fasttext_model = fasttext.load_model(fasttext_model_path)
+
+    # get predictions
+    predictions, _ = fasttext_model.predict(texts)
+    predicted_lang = [predictions[i][0].removeprefix("__label__") for i in range(len(texts))]
+    # get iso 639-3
+    fasttext_preds = [predicted_lang[i].split("_")[0] for i in range(len(predicted_lang))]
+    # get iso 639-1
+    fasttext_preds = [langcodes.standardize_tag(lang_code, macro=True) for lang_code in fasttext_preds]
+    return fasttext_preds
+
+
+def get_lingua_predictions(texts: Sequence[str]) -> list[str]:
+    """
+    Detect the languages of the given texts using the Lingua language detector.
+
+    Args:
+        texts: A sequence of texts for which the language detection
+               needs to be performed.
+
+    Returns:
+        A list of predicted ISO 639-1 language codes.
+        If the model is uncertain about the language of a text, "xx" is returned
+        as a placeholder.
+    """
+    texts = [text.lower() for text in texts]
+    lingua_detector = LanguageDetectorBuilder.from_all_languages().build()
+
+    predictions = lingua_detector.detect_languages_in_parallel_of(texts)
+
+    lingua_preds = [prediction.iso_code_639_1.name.lower() if prediction is not None else "xx" 
+                    for prediction in predictions]
+    return lingua_preds
+
+
+def calculate_metrics(cm: np.ndarray, labels: Sequence, model_name: str) -> pd.DataFrame:
+    """
+    Calculate precision, recall and f1-score.
+    Args:
+        cm: confusion matrix
+        labels: languages, for which the metrics need to be calculated
+        model_name: model name (needed for column names in DataFrame)
+
+    Returns: pandas.DataFrame with computed metrics for each language.
+    """
+    tp_and_fn = cm.sum(axis=1)
+    tp_and_fp = cm.sum(axis=0)
+    tp = cm.diagonal()
+
+    precision = np.divide(tp, tp_and_fp, out=np.zeros_like(tp, dtype=float), where=tp_and_fp > 0)
+    recall = np.divide(tp, tp_and_fn, out=np.zeros_like(tp, dtype=float), where=tp_and_fn > 0)
+    f1 = np.divide(2 * precision * recall, precision + recall, out=np.zeros_like(precision, dtype=float),
+                   where=(precision + recall) > 0)
+
+    df = pd.DataFrame({
+        "lang": labels,
+        f"{model_name}_precision": precision,
+        f"{model_name}_recall": recall,
+        f"{model_name}_f1": f1,
+    })
+
+    return df
+
+
+def main():
+    texts_under_10_words = pd.read_csv(os.path.join(Path(__file__).parent, "texts_under_10_words.csv"))
+    texts = texts_under_10_words.ingredients_text.tolist()
+    true_labels = texts_under_10_words.lang.tolist()
+    possible_class_labels = texts_under_10_words["lang"].value_counts().index.tolist()  # use value_counts in order to get sorted by frequency list
+
+    fasttext_preds = get_fasttext_predictions(texts)
+    lingua_preds = get_lingua_predictions(texts)
+
+    mapping = {"yue": "zh"}  # yue is a type of Chinese
+    replace_lang_code(fasttext_preds, mapping)
+    replace_lang_code(lingua_preds, mapping)
+
+    predictions = [fasttext_preds, lingua_preds]
+    model_names = ["fasttext", "lingua"]
+    metrics = []
+    for preds, model_name in zip(predictions, model_names):
+        cm = confusion_matrix(true_labels, preds, labels=possible_class_labels)
+        cm_df = pd.DataFrame(cm, index=possible_class_labels, columns=possible_class_labels)
+        cm_df.to_csv(os.path.join(Path(__file__).parent, f"{model_name}_confusion_matrix.csv"))
+
+        metrics_df = calculate_metrics(cm, possible_class_labels, model_name)
+        metrics_df.set_index("lang", inplace=True)
+        metrics.append(metrics_df)
+
+    # combine results
+    metrics_df = pd.DataFrame(texts_under_10_words.lang.value_counts())
+    metrics_df = pd.concat((metrics_df, *metrics), axis=1)
+
+    # change columns order
+    metrics_df = metrics_df[
+        ["count"] + [f"{model}_{metric}" for metric in ["precision", "recall", "f1"] for model in model_names]
+    ]
+
+    metrics_df.to_csv(os.path.join(Path(__file__).parent, "10_words_metrics.csv"))
+
+if __name__ == "__main__":
+    main()

language_identification/scripts/10_words_metrics.csv

@@ -0,0 +1,42 @@
+lang,count,fasttext_precision,lingua_precision,fasttext_recall,lingua_recall,fasttext_f1,lingua_f1
+en,36319,0.937642,0.987990,0.881476,0.934366,0.908692,0.960430
+fr,33759,0.980412,0.989449,0.828173,0.931849,0.897886,0.959786
+de,15540,0.971413,0.978929,0.834112,0.954839,0.897542,0.966734
+es,6010,0.843908,0.929216,0.855058,0.799721,0.849447,0.859619
+it,4098,0.665311,0.826041,0.878067,0.912855,0.757025,0.867281
+nl,3877,0.700655,0.905875,0.870591,0.925161,0.776434,0.915417
+pl,1350,0.807497,0.911485,0.962879,0.978790,0.878369,0.943939
+sv,1022,0.627437,0.833021,0.869000,0.949733,0.728721,0.887556
+pt,839,0.543636,0.426197,0.838710,0.905367,0.659680,0.579566
+fi,748,0.531029,0.565506,0.927298,0.982709,0.675325,0.717895
+bg,712,0.982072,0.986667,0.834179,0.951768,0.902104,0.968903
+hr,518,0.459313,0.883212,0.666667,0.889706,0.543897,0.886447
+nb,376,0.371212,0.196429,0.710145,0.416667,0.487562,0.266990
+ru,362,0.876877,0.974763,0.926984,0.916914,0.901235,0.944954
+hu,235,0.402367,0.682119,0.886957,0.911504,0.553596,0.780303
+cs,212,0.566038,0.811881,0.568720,0.828283,0.567376,0.820000
+ro,163,0.334239,0.275395,0.836735,0.853147,0.477670,0.416382
+da,115,0.211628,0.127152,0.834862,0.950495,0.337662,0.224299
+ca,90,0.052419,0.116883,0.822785,0.805970,0.098560,0.204159
+ja,60,1.000000,1.000000,0.305556,0.633333,0.468085,0.775510
+ar,44,0.854167,0.974359,0.953488,0.926829,0.901099,0.950000
+lv,34,0.198582,0.337079,0.848485,0.937500,0.321839,0.495868
+zh,25,0.063830,0.531915,0.187500,1.000000,0.095238,0.694444
+lt,23,0.068182,0.147887,0.954545,0.913043,0.127273,0.254545
+el,22,1.000000,1.000000,0.954545,1.000000,0.976744,1.000000
+tr,17,0.066667,0.121495,0.750000,0.928571,0.122449,0.214876
+sk,16,0.081250,0.130000,0.812500,0.812500,0.147727,0.224138
+uk,15,0.379310,0.423077,0.785714,0.846154,0.511628,0.564103
+th,14,1.000000,0.928571,0.857143,0.928571,0.923077,0.928571
+sl,14,0.062500,0.159420,0.833333,0.916667,0.116279,0.271605
+he,13,1.000000,1.000000,1.000000,1.000000,1.000000,1.000000
+bn,9,1.000000,1.000000,1.000000,1.000000,1.000000,1.000000
+is,8,0.160000,0.112903,1.000000,1.000000,0.275862,0.202899
+sr,8,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
+no,7,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
+et,5,0.005822,0.010471,0.800000,0.800000,0.011561,0.020672
+id,5,0.015228,0.000000,0.750000,0.000000,0.029851,0.000000
+ko,5,0.047059,1.000000,0.800000,1.000000,0.088889,1.000000
+sq,3,0.000000,0.015385,0.000000,1.000000,0.000000,0.030303
+ka,2,1.000000,1.000000,1.000000,1.000000,1.000000,1.000000
+vi,1,0.001050,0.200000,1.000000,1.000000,0.002099,0.333333

language_identification/scripts/README.md

@@ -0,0 +1,16 @@
+# Research of the quality of models on ingredient texts up to 10 words long.
+
+`01_extract_data.py`: extracts all texts with their languages from [huggingface dataset](https://huggingface.co/datasets/openfoodfacts/product-database).
+
+`02_select_short_texts_with_known_ingredients.py`: filters texts with length up to 10 words, performs ingredient analysis by OFF API, selects ingredient texts with at least 80% of known ingredients, adds short texts from manually checked data.
+
+What is manually checked data: \
+I created a validation dataset from texts from OFF (42 languages, 15-30 texts per language).
+I took 30 random texts in each language, obtained language predictions using the Deepl API and two other models ([language-detection-fine-tuned-on-xlm-roberta-base](https://huggingface.co/ivanlau/language-detection-fine-tuned-on-xlm-roberta-base) and [multilingual-e5-language-detection](https://huggingface.co/Mike0307/multilingual-e5-language-detection)). For languages they don’t support, I used Google Translate and ChatGPT for verification. (As a result, after correcting the labels, some languages have fewer than 30 texts).
+
+
+`03_calculate_metrics.py`: obtains predictions by [FastText](https://huggingface.co/facebook/fasttext-language-identification) and [lingua language detector](https://github.com/pemistahl/lingua-py) models for texts up to 10 words long, and calculates precision, recall and f1-score.
+
+Results are in files: [metrics](./10_words_metrics.csv), [FastText confusion matrix](./fasttext_confusion_matrix.csv), [lingua confusion matrix](./lingua_confusion_matrix.csv).
+
+It turned out that both models demonstrate low precision and high recall for some languages (indicating that the threshold might be too high and should be adjusted).

language_identification/scripts/fasttext_confusion_matrix.csv

@@ -0,0 +1,42 @@
+,en,fr,de,es,it,nl,pl,sv,pt,fi,bg,hr,nb,ru,hu,cs,ro,da,ca,ja,ar,lv,zh,lt,el,tr,sk,uk,th,sl,he,bn,is,sr,no,et,id,ko,sq,ka,vi
+en,31050,308,79,414,1092,353,119,161,23,188,0,51,72,0,67,19,116,77,305,0,0,25,25,22,0,47,2,0,0,5,0,0,11,0,0,248,29,45,8,0,264
+fr,1406,25627,120,246,361,402,77,80,266,211,0,167,175,0,53,33,82,30,678,0,1,37,13,184,0,48,26,0,0,9,0,0,6,0,0,116,104,19,4,0,363
+de,284,40,12505,30,170,575,29,219,46,110,0,52,92,0,127,4,5,153,42,0,0,22,3,14,0,19,9,0,0,48,0,0,8,0,0,252,29,11,4,0,90
+es,112,56,15,4336,70,11,28,9,113,27,0,4,7,0,7,11,5,0,79,0,0,8,0,32,0,15,21,0,0,17,0,0,6,0,0,3,6,1,0,0,72
+it,71,19,48,42,3435,9,12,10,42,22,0,2,10,0,3,7,30,8,37,0,0,4,0,6,0,0,2,0,0,5,0,0,0,0,0,3,8,3,0,0,74
+nl,85,70,74,19,8,3209,16,11,4,19,0,1,23,0,7,7,2,26,11,0,0,6,0,8,0,8,2,0,0,2,0,0,2,0,0,35,3,0,1,0,27
+pl,9,0,2,1,0,0,1271,1,1,1,0,3,0,0,12,0,0,0,2,0,0,4,0,0,0,10,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1
+sv,17,6,3,0,2,3,3,869,1,9,0,7,11,0,15,0,2,7,1,0,0,3,0,2,0,6,0,0,0,1,0,0,5,0,0,9,0,0,5,0,13
+pt,12,6,7,37,4,1,1,0,598,2,0,0,3,0,0,2,3,0,13,0,0,0,0,3,0,3,4,0,0,4,0,0,0,0,0,0,0,0,0,0,10
+fi,0,0,1,1,4,4,0,1,1,676,0,7,1,0,3,0,0,5,0,0,0,2,0,0,0,3,1,0,0,1,0,0,0,0,0,14,1,0,0,0,3
+bg,15,0,3,0,0,0,0,4,0,0,493,0,0,41,0,2,0,0,0,0,1,0,0,0,0,0,0,10,0,0,0,0,0,19,0,0,0,2,0,0,1
+hr,8,0,2,6,5,1,9,6,0,3,0,254,4,0,0,1,0,0,2,0,0,1,0,12,0,0,2,0,0,49,0,0,1,0,0,2,5,0,1,0,7
+nb,11,5,3,0,5,7,1,7,2,2,0,3,245,0,3,0,0,29,0,0,0,0,0,1,0,6,0,0,0,1,0,0,3,0,0,1,6,0,0,0,4
+ru,2,0,0,0,0,0,0,0,0,0,8,0,1,292,0,0,0,0,0,0,1,0,0,0,0,0,0,8,0,0,0,0,0,2,0,0,0,0,0,0,1
+hu,3,1,0,1,0,1,3,0,1,0,0,1,0,0,204,4,0,0,1,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0
+cs,1,0,3,0,0,2,2,0,1,0,0,1,0,0,4,120,0,1,0,0,0,0,0,1,0,1,68,0,0,5,0,0,0,0,0,0,0,0,0,0,1
+ro,3,1,2,0,3,1,0,1,1,1,0,0,1,0,0,0,123,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2
+da,2,0,1,0,1,0,0,2,0,0,0,0,9,0,0,0,0,91,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+ca,3,0,0,2,3,0,1,1,0,2,0,0,0,0,0,0,0,0,65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2
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