chore: analyze language identification models performance on short ingredient texts with precision-recall evaluation (#349) #365
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short ingredient texts with precision-recall evaluation (openfoodfacts#349)
baslia
reviewed
Nov 25, 2024
korablique
changed the title
|
@baslia Could you please help me with this error? https://github.com/openfoodfacts/openfoodfacts-ai/actions/runs/12106320879/job/33751933052?pr=365 |
Hey, I thought this was just a check about the PR label attribute, I just attached the label "enhancement". |
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This is the research conducted in the next issue: #349
01_extract_data.py: extracts all texts with their languages from huggingface dataset.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 and 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 and lingua language detector models for texts up to 10 words long, and calculates precision, recall and f1-score.Results are in files: 10_words_metrics.csv, fasttext_confusion_matrix.csv, 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).