Train a language identifier model that works well on ingredient lists #349

raphael0202 · 2024-09-19T09:00:53Z

Problem

We're currently using fasttext for language identification.
This is useful especially to detect the language of an ingredient list extracted automatically using a ML model, or added by a contributor.

However, fasttext was trained on data that is quite different from ingredient lists (Wikipedia, Tatoeba and SETimes).

Sometimes the model fails for obvious cases, such as this one (french ingredient list):

text: fraise (12%), framboise (10%)

predictions:
en, confidence=0.4291181
it, confidence=0.13040087
fr, confidence=0.0435654
ro, confidence=0.026255628
no, confidence=0.019594753
de, confidence=0.017750196
es, confidence=0.01671417
tr, confidence=0.015862297
sco, confidence=0.01577331
ms, confidence=0.015433003

This behaviour is mostly present for short ingredient lists.

We should explore training a new model for language identification using Open Food Facts data (especially ingredient lists).

Requirements

Using fasttext is not a requirement. We can either train a new fasttext model, or train it with pytorch/tensorflow and export it to ONNX format.

The text was updated successfully, but these errors were encountered:

korablique · 2024-09-26T16:41:59Z

What I'm going to try (following the discussion on Slack):

select products from OFF with >80% of recognized ingredients
measure existing fasttext model quality on this data
fine tune the fasttext model
compare results

@raphael0202 If that's OK with you, could you please assign the issue to me?

raphael0202 · 2024-09-27T16:03:18Z

Yes that's a good plan to start 👍
I've assigned you on this issue

korablique · 2024-10-10T14:08:47Z

Here is the number of texts for each language

en    422020
fr    299681
de     89880
es     46255
it     31801
nl     19983
pl      8401
pt      8119
sv      6128
bg      4453
ro      3771
fi      3726
ru      3610
nb      3591
cs      3500
th      3157
da      2021
hr      2015
hu      1962
ar      1104
el       943
ja       912
ca       824
sr       735
sl       727
sk       606
tr       506
lt       453
zh       436
et       370
lv       333
xx       318
no       315
uk       274
id       262
he       209
vi       121
is       113
la        89
in        72
ko        71
sq        70
iw        59
ka        54
ms        52
bs        37
fa        35
bn        33
gl        32
kk        25
mk        23
nn        18
hi        18
aa        17
uz        17
so        15
af        12
eu        11
az         8
be         7
cy         7
hy         7
tt         6
ku         5
km         4
te         4
ky         4
ur         4
mg         3
ty         3
ta         3
tg         3
my         3
tl         3
mo         2
sc         2
ir         2
ne         2
tk         2
am         2
mn         2
co         2
se         2
si         2
fj         1
ch         1
ug         1
yi         1
to         1
fo         1
mt         1
ht         1
ak         1
jp         1
oc         1
lb         1
mi         1
as         1
yo         1
ga         1
gd         1
ba         1
zu         1
mr         1

baslia · 2024-10-11T05:01:12Z

Hey, is that a requirements that the models needs to run locally (not from a server) ?
It seems that LLMs are good with detecting languages.

korablique · 2024-10-14T17:48:12Z

Tried to use clustering to fix mislabeled data.

I took the languages for which there are at least 100 texts (37 languages). Then took 100 texts for each language and used them as a training dataset (wanted then to get predictions for the entire dataset).

The texts were converted to embeddings using fasttext (get_sentence_vector method), the dimension was reduced from 256 to 66 to preserve 95% variance using PCA.
Tried 2 methods: gaussian mixture and HDBSCAN.
Gaussian mixture divides the data into only 3 clusters. And HDBSCAN classifies all new data as noise. The picture below shows the result of HDBSCAN clustering of training data. The clusters are difficult to separate.

Either clustering is not suitable for this task, or I am doing something wrong.

Now I will try to use another text classification model: lingua https://github.com/pemistahl/lingua-py to compare the predictions and confidence of two models. Then I'll take data in which the predictions of the models coincide and they are both confident and fine-tune one of them on this data.

jeremyarancio · 2024-10-22T09:32:14Z

Here's a really nice article summarizing different approaches for language detection, from statistical to deep learning
https://medium.com/besedo-engineering/language-identification-for-very-short-texts-a-review-c9f2756773ad

Would be great to have a validation dataset to estimate the performance of any solution.
This dataset can be manually annotated using https://languagetool.org/

korablique · 2024-10-22T10:02:26Z

How I got the distribution of texts languages:

selected ingredients_text_{LANG} field names from mongo DB:

docker exec -i mongodb-container mongo mydatabase --quiet --eval '
var cursor = db.mycollection.aggregate([
  { "$project": {
      "fields": { "$objectToArray": "$$ROOT" }
  }},
  { "$unwind": "$fields" },
  { "$match": { "fields.k": /^ingredients_text_/ }},
  { "$group": {
      "_id": null,
      "all_fields": { "$addToSet": "$fields.k" }
  }},
  { "$limit": 20 }
]);
if (cursor.hasNext()) {
  printjson(cursor.next().all_fields);
}' > field_names.json

then selected field values:

FIELDS=$(jq -r '.[]' field_names.json | paste -sd "," -)
docker exec -i mongodb-container mongo mydatabase --quiet --eval '
var fields = "'$FIELDS'".split(",");
var projection = {};
fields.forEach(function(field) { projection[field] = 1; });

var cursor = db.mycollection.find({}, projection).forEach(function(doc) {
    var cleanedDoc = {};
    fields.forEach(function(field) {
        if (doc[field] && doc[field] !== "") {
            cleanedDoc[field] = doc[field];
        }
    });
    if (Object.keys(cleanedDoc).length > 0) {
        printjson(cleanedDoc);
    }
});' > filtered_extracted_values.json

(but after that there are still some extra fields left, e.x. ingredients_text_with_allergens)

then I made a dictionary in which text is the key, language is the value:

ingredients_text_lang_dct = dict()

with open(os.path.join(data_dir, 'filtered_extracted_values.json'), 'r') as data_file:
    for dct in ijson.items(data_file, 'item'):
        for k, v in dct.items():
            if k == 'ingredients_text_with_allergens':
                continue
            lang = k[k.rfind('_') + 1:]

            # if the field is `ingredients_text_{LANG}_imported`
            if lang == 'imported':
                start = k[:k.rfind('_')].rfind('_') + 1
                end = k.rfind('_')
                lang = k[start:end]
            ingredients_text_lang_dct.update({v: lang})

@raphael0202

korablique · 2024-10-24T10:35:15Z

Would be great to have a validation dataset to estimate the performance of any solution. This dataset can be manually annotated using https://languagetool.org/

How many samples should it contain? Should I select an equal number of samples for each language or just random?
@jeremyarancio

jeremyarancio · 2024-10-24T10:56:01Z

Roughly 30 labels per language to start with I would say.
It's just to have an idea about the performances

Would be great to have a validation dataset to estimate the performance of any solution. This dataset can be manually annotated using https://languagetool.org/

How many samples should it contain? Should I select an equal number of samples for each language or just random?
@jeremyarancio

baslia · 2024-10-26T04:47:13Z

Here is the number of texts for each language

en    422020
fr    299681
de     89880
es     46255
it     31801
nl     19983
pl      8401
pt      8119
sv      6128
bg      4453
ro      3771
fi      3726
ru      3610
nb      3591
cs      3500
th      3157
da      2021
hr      2015
hu      1962
ar      1104
el       943
ja       912
ca       824
sr       735
sl       727
sk       606
tr       506
lt       453
zh       436
et       370
lv       333
xx       318
no       315
uk       274
id       262
he       209
vi       121
is       113
la        89
in        72
ko        71
sq        70
iw        59
ka        54
ms        52
bs        37
fa        35
bn        33
gl        32
kk        25
mk        23
nn        18
hi        18
aa        17
uz        17
so        15
af        12
eu        11
az         8
be         7
cy         7
hy         7
tt         6
ku         5
km         4
te         4
ky         4
ur         4
mg         3
ty         3
ta         3
tg         3
my         3
tl         3
mo         2
sc         2
ir         2
ne         2
tk         2
am         2
mn         2
co         2
se         2
si         2
fj         1
ch         1
ug         1
yi         1
to         1
fo         1
mt         1
ht         1
ak         1
jp         1
oc         1
lb         1
mi         1
as         1
yo         1
ga         1
gd         1
ba         1
zu         1
mr         1

Would it be possible to share the link to this original data set ? I am curious to have a look to it as well.
Thanks!

korablique · 2024-10-28T11:58:47Z

Would it be possible to share the link to this original data set ? I am curious to have a look to it as well.
Thanks!

I used the MongoDB dump. I described above how I retrieved the data from it. However, there might be an error in my script because some languages have fewer texts than expected (e.g. I got 912 samples of Japanese texts, but on https://jp-en.openfoodfacts.org/ there are around 16,000).

Please keep me posted if you’re planning to work on this task, as I’m actively working on it. You can find me on OFF slack (Yulia Zhilyaeva)

jeremyarancio · 2024-10-28T21:41:13Z

If this can help, there's now a Parquet dump in Hugging Face, which is the JSONL processed and cleaned from irrelevant features:

https://huggingface.co/datasets/openfoodfacts/product-database

korablique · 2024-10-31T15:39:24Z

If this can help, there's now a Parquet dump in Hugging Face, which is the JSONL processed and cleaned from irrelevant features:

https://huggingface.co/datasets/openfoodfacts/product-database

Tried to retrieve data from huggingface dataset, but still get ~900 samples of Japanese texts, and ~996,000 texts in total. Am I doing something wrong? Or is it because at the moment the hf dataset stores text only in the original language?

My code here

other_lang_columns = [
    'ingredients_text_fr',
    'ingredients_text_en',
    ...
] 
dataset_file = os.path.join(data_dir, 'data_from_hf.csv')

for start, stop in tqdm(zip(range(0, 91, 10), range(10, 101, 10))):  
    # read 10% of the dataset
    hf_dataset = load_dataset('openfoodfacts/product-database', split=f'main[{start}%:{stop}%]')
    
    # retrieve ingredients_text and lang
    ingredients_texts = hf_dataset['ingredients_text']
    langs = hf_dataset['lang']
    
    df = pd.DataFrame({'ingredients_text': ingredients_texts, 'lang': langs})
    df.dropna(inplace=True)
    
    # retrieve ingredients_text_{LANG}
    for other_lang_col in other_lang_columns:
        lang = other_lang_col[-2:]
        other_lang_texts = hf_dataset[other_lang_col]
        other_lang_texts = [text for text in other_lang_texts if text is not None and len(text) > 0]
        
        new_rows = pd.DataFrame({'ingredients_text': other_lang_texts, 'lang': [lang] * len(other_lang_texts)})
        df = pd.concat((df, new_rows), ignore_index=True)
        
    # save
    df.to_csv(dataset_file, mode='a', header=start == 0, index=False)

@jeremyarancio

jeremyarancio · 2024-10-31T16:13:13Z

If this can help, there's now a Parquet dump in Hugging Face, which is the JSONL processed and cleaned from irrelevant features:
https://huggingface.co/datasets/openfoodfacts/product-database

Tried to retrieve data from huggingface dataset, but still get ~900 samples of Japanese texts, and ~996,000 texts in total. Am I doing something wrong? Or is it because at the moment the hf dataset stores text only in the original language?

My code here

other_lang_columns = [
    'ingredients_text_fr',
    'ingredients_text_en',
    ...
] 
dataset_file = os.path.join(data_dir, 'data_from_hf.csv')

for start, stop in tqdm(zip(range(0, 91, 10), range(10, 101, 10))):  
    # read 10% of the dataset
    hf_dataset = load_dataset('openfoodfacts/product-database', split=f'main[{start}%:{stop}%]')
    
    # retrieve ingredients_text and lang
    ingredients_texts = hf_dataset['ingredients_text']
    langs = hf_dataset['lang']
    
    df = pd.DataFrame({'ingredients_text': ingredients_texts, 'lang': langs})
    df.dropna(inplace=True)
    
    # retrieve ingredients_text_{LANG}
    for other_lang_col in other_lang_columns:
        lang = other_lang_col[-2:]
        other_lang_texts = hf_dataset[other_lang_col]
        other_lang_texts = [text for text in other_lang_texts if text is not None and len(text) > 0]
        
        new_rows = pd.DataFrame({'ingredients_text': other_lang_texts, 'lang': [lang] * len(other_lang_texts)})
        df = pd.concat((df, new_rows), ignore_index=True)
        
    # save
    df.to_csv(dataset_file, mode='a', header=start == 0, index=False)

@jeremyarancio

The Parquet contains the same information as the JSONL file, so it's not surprising.
You also have the text in all languages as ingredients_text and ingredients_text_{lang}

korablique · 2024-10-31T18:59:47Z

The Parquet contains the same information as the JSONL file, so it's not surprising.
You also have the text in all languages as ingredients_text and ingredients_text_{lang}

I see. I mean I don't understand why there https://jp-en.openfoodfacts.org/ are 16,000 products, and I have only 900
@jeremyarancio

korablique · 2024-10-31T19:04:25Z

Oh, seems that just not all of them have ingredients list in Japanese

korablique · 2024-11-05T18:57:44Z

I created a validation dataset from texts from OFF off_validation_dataset.csv (42 languages, 15-30 texts per language) and validated FastText and lingua models.

I took 30 random texts in each language, obtained language predictions using the Deepl API and two other models (this and this). 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).

Accuracy of the models:
fasttext: 92.94%
lingua: 93.79%
(I used only these models, because according to some articles (this and this) comparing language identification models these's almost nothing better than fasttext)

Should I compare their accuracy on only short texts, or should I try to retrain fasttext?
@raphael0202 @jeremyarancio

raphael0202 · 2024-11-06T10:06:24Z

Hello @korablique, thank you for the analysis!

So if I understood correctly, the lang field was obtained by querying Deepl and two other models, or checking manually?

And can you provide the metrics for each language?

For reference, using duckdb, I computed the number of items for each language:
┌─────────┬───────┐
│ lang │ count │
│ varchar │ int64 │
├─────────┼───────┤
│ fi │ 30 │
│ nl │ 30 │
│ pl │ 30 │
│ hr │ 30 │
│ pt │ 30 │
│ es │ 30 │
│ en │ 30 │
│ de │ 30 │
│ fr │ 30 │
│ it │ 30 │
│ cs │ 30 │
│ sv │ 29 │
│ da │ 29 │
│ he │ 29 │
│ nb │ 29 │
│ sl │ 28 │
│ et │ 28 │
│ lv │ 28 │
│ bg │ 28 │
│ ja │ 28 │
│ tr │ 27 │
│ hu │ 27 │
│ ru │ 26 │
│ vi │ 26 │
│ zh │ 25 │
│ is │ 25 │
│ th │ 24 │
│ no │ 24 │
│ ro │ 24 │
│ sr │ 24 │
│ uk │ 23 │
│ ko │ 22 │
│ ar │ 22 │
│ sk │ 22 │
│ lt │ 21 │
│ ka │ 17 │
│ el │ 17 │
│ bn │ 17 │
│ ca │ 17 │
│ bs │ 16 │
│ sq │ 15 │
│ id │ 15 │
├─────────┴───────┤
│ 42 rows │
└─────────────────┘

raphael0202 · 2024-11-06T11:05:36Z

I've just added to the Python SDK a new method to analyze the ingredients in a given language:
https://openfoodfacts.github.io/openfoodfacts-python/usage/#perform-ingredient-analysis

Using the is_in_taxonomy field for each detected ingredient, you can compute easily the number of ingredients recognized or not, and spot ingredient lists that are not in the right language. It can help you detect errors in your validation set or increase its size.

edit: you need the latest version of the SDK for it to work, openfoodfacts==2.1.0

jeremyarancio · 2024-11-06T11:34:02Z

Gj @korablique
Since the distribution is not uniform, it would be preferable to compute the Precision & Recall for each lang to have a better understanding of which languages the models struggle with.
Also, based on the initial issue description, it seems the language prediction is often wrong when the text is quite short. Having Precision and Recall depending on the text length (<10 words, 10 - 20 words, >20 words, for example) could be insightful.

korablique · 2024-11-06T17:16:23Z

So if I understood correctly, the lang field was obtained by querying Deepl and two other models, or checking manually?

Yes

And can you provide the metrics for each language?

lang	count	fasttext_precision	fasttext_recall	lingua_precision	lingua_recall
no	53	0.980392	0.943396	0.961538	0.943396
en	30	0.933333	0.933333	0.947368	0.600000
nl	30	0.937500	1.000000	0.937500	1.000000
pl	30	1.000000	1.000000	0.967742	1.000000
it	30	0.966667	0.966667	1.000000	0.966667
pt	30	1.000000	0.900000	1.000000	0.900000
hr	30	0.689655	0.666667	0.531915	0.833333
fr	30	1.000000	0.933333	1.000000	0.966667
es	30	0.931034	0.900000	1.000000	0.900000
fi	30	0.931034	0.900000	1.000000	0.933333
de	30	1.000000	0.966667	0.937500	1.000000
cs	30	0.964286	0.900000	0.937500	1.000000
he	29	1.000000	1.000000	1.000000	1.000000
da	29	0.965517	0.965517	0.928571	0.896552
sv	29	0.966667	1.000000	0.966667	1.000000
sl	28	0.931034	0.964286	0.928571	0.928571
et	28	0.965517	1.000000	0.965517	1.000000
lv	28	1.000000	0.928571	1.000000	0.892857
bg	28	1.000000	0.892857	1.000000	0.964286
ja	28	0.833333	0.357143	1.000000	0.892857
hu	27	0.928571	0.962963	1.000000	0.962963
tr	27	0.962963	0.962963	1.000000	0.925926
ru	26	1.000000	0.961538	0.962963	1.000000
vi	26	0.928571	1.000000	1.000000	0.923077
zh	25	0.517241	0.600000	0.892857	1.000000
is	25	1.000000	1.000000	1.000000	1.000000
ro	24	1.000000	0.916667	1.000000	0.833333
sr	24	0.000000	0.000000	0.000000	0.000000
th	24	1.000000	1.000000	1.000000	1.000000
uk	23	1.000000	1.000000	1.000000	0.913043
ko	22	1.000000	0.909091	1.000000	1.000000
sk	22	0.846154	1.000000	1.000000	0.863636
ar	22	0.916667	1.000000	1.000000	0.954545
lt	21	1.000000	1.000000	0.913043	1.000000
ka	17	1.000000	1.000000	1.000000	1.000000
el	17	1.000000	1.000000	1.000000	1.000000
ca	17	0.882353	0.882353	0.937500	0.882353
bn	17	1.000000	0.764706	1.000000	1.000000
bs	16	0.300000	0.750000	0.235294	0.250000
id	15	1.000000	0.933333	1.000000	0.800000
sq	15	1.000000	0.866667	0.933333	0.933333

Serbian (sr), Bosnian (bs) and Croatian (hr) are very similar, so models confuse them. I talked to a friend from Serbia and he said that basically they are the same language with only tiny variations.

Also, I considered the variants of Norwegian as one language.

Sorry, I didn't think to filter only short texts from the beginning. I'll calculate the metrics again after I improve the dataset

baslia · 2024-11-11T15:56:03Z

It seems like good results, congrats!
If I can suggest some way of improvement:

You can compute the AUC ROC, there are different way to do it as it is a multi classification problem. But this can be relevant to see how sensitive the model is to the threshold
You can weight the loss function, to give more importance to certain language, or to balance minority samples.

raphael0202 · 2024-11-12T08:34:41Z

I would suggest also adding f1-score as a metric!

korablique · 2024-11-12T09:47:06Z

Recalculated metrics on only short texts (no more than 10 words). 30 texts per language.

lang	fasttext_precision	lingua_precision	fasttext_recall	lingua_recall	fasttext_f1	lingua_f1
ar	0.964286	1.000000	0.900000	0.866667	0.931035	0.928572
bg	1.000000	0.965517	0.633333	0.933333	0.775510	0.949152
ca	0.769231	0.913043	0.666667	0.700000	0.714286	0.792453
cs	0.941176	1.000000	0.533333	0.833333	0.680851	0.909091
da	0.800000	0.818182	0.800000	0.900000	0.800000	0.857143
de	0.717949	0.906250	0.933333	0.966667	0.811594	0.935484
en	0.571429	0.896552	0.800000	0.866667	0.666667	0.881356
es	0.807692	0.941176	0.700000	0.533333	0.750000	0.680851
fi	0.903226	0.933333	0.933333	0.933333	0.918033	0.933333
fr	0.842105	0.888889	0.533333	0.800000	0.653061	0.842105
hr	1.000000	0.952381	0.400000	0.666667	0.571429	0.784314
hu	0.964286	1.000000	0.900000	0.866667	0.931035	0.928572
it	1.000000	0.960000	0.900000	0.800000	0.947368	0.872727
ja	1.000000	1.000000	0.233333	0.700000	0.378378	0.823529
lv	1.000000	1.000000	0.766667	0.866667	0.867925	0.928572
no	0.720000	0.800000	0.600000	0.666667	0.654545	0.727273
nl	0.880000	0.833333	0.733333	0.666667	0.800000	0.740741
pl	0.966667	1.000000	0.966667	1.000000	0.966667	1.000000
pt	0.944444	0.696970	0.566667	0.766667	0.708333	0.730159
ro	0.956522	0.961538	0.733333	0.833333	0.830189	0.892857
ru	0.961538	1.000000	0.833333	0.833333	0.892857	0.909091
sv	0.958333	0.961538	0.766667	0.833333	0.851852	0.892857

raphael0202 added the ✨ enhancement New feature or request label Sep 19, 2024

teolemon added this to 🤖 Artificial Intelligence @ Open Food Facts Sep 19, 2024

github-project-automation bot moved this to Todo in 🤖 Artificial Intelligence @ Open Food Facts Sep 19, 2024

raphael0202 assigned korablique Sep 27, 2024

teolemon removed the ✨ enhancement New feature or request label Oct 19, 2024

teolemon added ingredients Multilingual products labels Oct 29, 2024

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Train a language identifier model that works well on ingredient lists #349

Train a language identifier model that works well on ingredient lists #349

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Train a language identifier model that works well on ingredient lists #349

Train a language identifier model that works well on ingredient lists #349

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raphael0202 commented Sep 19, 2024

Problem

Requirements

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raphael0202 commented Nov 6, 2024 •

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