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added to README files, added full dataset versions to data

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Franziska Marquis
2021-04-15 20:19:09 +02:00
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README.md
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@@ -3,4 +3,18 @@
German FoodBERT models for ingredient substitute recommendation
The 3 German FoodBERT versions can be found under https://cloud.marquis.site/s/ZUVIIIQv6yznBj6
The zip has to be unpacked in final_Versions/
The zip has to be unpacked in **final_Versions/**
More infos about each step can be found in other README files in each directory. The overall order is:
- crawl_recipes
- clean_dataset
- train_model
- evalutation
Dataset versions can be found in **data**.
## Run Configuartion
All python scripts should be run from the base directory (from here) using Python 3.9.
Example: python evaluation/final_eval.py

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data/README.md
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@@ -25,8 +25,6 @@ A full dataset contains the complete recipes and additional information. Each re
comments: List of user comments (Strings) about the recipe
**dataset_parts**: Directory contains the dataset parts that were pulled from chefkoch.
**dataset_fin.json**: Entire dataset (combined dataset parts) as retrieved from chefkoch. Instructions of each recipe are one String.
**dataset_test.json**: A test dataset that only contains a few recipes. This dataset was used to test code,
@@ -42,8 +40,14 @@ Instructions are separated into sentences (List of Strings).
**dataset_cleaned_steps_not_empty.json**: Entire dataset with cleaned ingredients and instructions. Instructions are
separated into sentences (List of Strings). Recipes without instructions removed manually by searching for recipes containing **instructions": []**.
**full_dataset.json**: Entire dataset with cleaned ingredients and instructions. Instructions are
separated into blocks of multiple sentences with up to 512 tokens. Recipes without instructions removed manually by searching for recipes containing **instructions": []**.
**full_dataset_vers1.json**: Entire dataset with cleaned ingredients and instructions. Instructions are
separated into blocks of multiple sentences with up to 512 tokens, separated by [SEP]. Recipes without instructions removed manually by searching for recipes containing **instructions": []**.
**full_dataset_vers2.json**: Entire dataset with cleaned ingredients and instructions. Instructions are
separated into blocks of multiple sentences with up to 512 tokens, not separated by [SEP]. Recipes without instructions removed manually by searching for recipes containing **instructions": []**.
**full_dataset_vers3.json**: Entire dataset with cleaned ingredients and instructions. Instructions are
separated into sentences. Recipes without instructions removed manually by searching for recipes containing **instructions": []**.
##Occurrances
### Cleaned Ingredients
@@ -64,7 +68,7 @@ Ingredients are sorted by number of occurrences.
ingredient occurrs in the cleaned steps. This is important for training the model later on,
as training will not be good for ingredients that only occur a few times in the steps.
## Ingredient sets
## food_categories directory
These json files contain all ingredients that could belong to the different categories.
These files are: **bread.json**, **fish.json**, **meats.json**, **pasta.json**, **rolls.json**, **sausage.json**
@@ -78,18 +82,7 @@ instructions is separated into its sentences. Each step is cleaned. The structur
**cleaned_sep_sentences_not_empty.json**: same as above, but removed recipes that don't have any instructions
**complete_dataset512.json**: sentences of instructions of a recipe are combined until token amount nears 512. Not [SEP]
tokens.
**complete_dataset_SEP512.json**: sentences of instructions of a recipe are combined until token amount nears 512. [SEP]
tokens included.
**model_datapoints.txt** and **model_datapoints_SEP.txt**: list of only the datapoints from **complete_dataset... .json**
**training_data.txt**: instruction datapoints from recipes set aside for training
**testing_data.txt**: instruction datapoints from recipes set aside for testing
## Other
**ground_truth.json**: ground truth used for evaluation
**synonyms.json**: synonyms of ingredients found in ground truth

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evaluation/README.md
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Some parameters (model version, etc.) need to be adjusted in all scripts.
## Generate Substitute Recommendations
**generate_substitutes.py** is used to generate the substitute recommendations for each model using various scoring thresholds. Model version and scoring threshold need to be specified.
## Prepare Data for Evaluation
**find_ground_truth_ingredients.py** was used to find "rare" and "frequent" ingredients for the ground truth.
Ingredients for which no substitute recommendations are found need to be added to the substitute-JSON file. This is done using **add_unused_ingredients.py**
## Evaluation
An intermediate evaluation was done using **stats_engl_substitutes_compare.py** to gain insight into the various versions of the substitute recommendations. However, this script is not used for the final evaluation.
The ingredient substitute recommendations made using each FoodBERT version can be evaluated using **final_eval.py**.
The version that is to be used has to be adjusted in the first line of the main().
Stats for the dataset and the ground truth can be found using **dataset_stats.py** and **ground_truth_stats.py**, respectively.

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evaluation/evaluate.py
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import json
import statistics
data_path = "data/"
occurances_path = "mult_ingredients_nice.json"
ground_truth_path = "ground_truth.json"
engl_data_path = "evaluation/engl_data/"
evaluation_path = "evaluation/"
synonyms_path = "synonyms.json"
found_substitutes_path = "final_Versions/models/vers2/eval/complete_substitute_pairs_50.json"
# model_name = "Versions/vers3/"
german_ground_truth = {
"Karotte": ["Pastinake", "Steckrübe", "Staudensellerie", "Kürbis", "Süßkartoffel", "Rettich", "Radieschen", "Kartoffel", "Paprika_rot", "Butternusskürbis", "Petersilienwurzel"],
"Kirsche": ["Aprikose", "Pflaume", "Nektarine", "Himbeeren", "Weintrauben", "Erdbeere", "Johannisbeeren", "Brombeeren", "Beeren_gemischte", "Pfirsich", "Cranberries", "Cranberries_getrocknet", "Blaubeeren", "Maraschino", "Beeren", "Trockenpflaumen"],
"Huhn": ["Truthahn", "Kaninchen", "Austernpilze", "Kalbfleisch", "Fisch", "Tofu", "Rindfleisch", "Tofu_fester", "Schweinefleisch", "Seitan", "Ente", "Lamm", "Pilze", "Shrimps", "Wachtel", "Gans", "Wildfleisch"],
"Petersilie": ["Kerbel", "Koriander", "Estragon", "Basilikum", "Oregano", "Liebstöckel", "Dill", "Koriandergrün", "Rosmarin", "Kapern", "Thymian", "Schnittlauch", "Minze", "Basilikum_getrockneter", "Oregano_getrocknet", "Thymian_getrocknet"],
"Schokolade": ["Nutella", "Kakaopulver_Instant", "Zucker", "Marmelade", "Marshmallow", "Kakao", "Süßigkeiten", "Erdnussbutter"],
"Frühstücksspeck": ["Pancetta", "Schinken_Prosciutto", "Speck", "Schinken_rohen", "Parmaschinken", "Schinken", "Salami", "Chorizo", "Wurst_Krakauer", "Schweineschwarte", "Schinkenwürfel", "Croûtons", "Speckwürfel", "Kochschinken", "Corned_Beef", "Wurst_Mortadella"],
"Grünkohl": ["Spinat", "Chinakohl", "Lauch", "Endiviensalat", "Mangold", "Wirsing", "Kohl", "Blumenkohl", "Brunnenkresse", "Rucola", "Blattspinat", "Kopfsalat", "Römersalat", "Babyspinat"],
"Zucker": ["Honig", "Stevia", "Süßstoff", "Stevia_flüssig", "Süßstoff_flüssigen", "Reissirup", "Ahornsirup", "Kondensmilch_gezuckerte", "Agavendicksaft", "Schokolade", "Vanille", "Melasse", "Zuckerrübensirup", "Sirup"],
"Brie": ["Camembert", "Gorgonzola", "Schmelzkäse", "Cheddarkäse", "Ziegenkäse", "Doppelrahmfrischkäse", "Blauschimmelkäse", "Roquefort", "Gouda", "Käse_Fontina", "Käse_Provolone", "Feta_Käse", "Scheiblettenkäse"],
"Truthahn": ["Huhn", "Kaninchen", "Ente", "Kochschinken", "Fasan", "Gans", "Rindfleisch", "Lammfleisch", "Schweinefleisch", "Roastbeef", "Kalbfleisch", "Geflügelfleisch", "Hähnchenfilet", "Hühnerkeule", "Wachtel", "schweinekotelett", "Wildfleisch"]
}
def no_synonyms(ground_truth_dict=None, found_substitutes_dict=None, get_occurrences=True, synonyms=True):
if get_occurrences:
with open(data_path + occurances_path, "r") as whole_json_file:
occurrences_dict = json.load(whole_json_file)
if not ground_truth_dict:
with open(data_path+ground_truth_path, "r") as whole_json_file:
ground_truth_dict = json.load(whole_json_file)
if synonyms:
with open(data_path + synonyms_path, "r") as whole_json_file:
synonyms_dict = json.load(whole_json_file)
else:
synonyms_dict = {}
if not found_substitutes_dict:
with open(found_substitutes_path, "r") as whole_json_file:
model_substitutes_dict = json.load(whole_json_file)
else:
model_substitutes_dict = found_substitutes_dict
found_ground_ingr = {}
correctly_found = 0
incorrectly_found = 0
average_precision = 0.0
average_recall = 0.0
number_correct_subs_found_overall = []
total_number_subs_found_overall = []
# base ingredient without synonyms, substitutes with synonyms
for base_ingred in ground_truth_dict.keys():
if get_occurrences:
occurrences = occurrences_dict[base_ingred]
found_substitutes = model_substitutes_dict[base_ingred].copy()
# if len(found_substitutes) > 30:
# found_substitutes = found_substitutes[:30]
found = []
# remove synonyms of base ingredient
new_found_substitutes = []
for subst in found_substitutes:
if base_ingred in synonyms_dict.keys():
if subst not in synonyms_dict[base_ingred]:
new_found_substitutes.append(subst)
else:
new_found_substitutes.append(subst)
found_substitutes = new_found_substitutes
# check which substitutes were found
for subst in ground_truth_dict[base_ingred]:
# only add substitute if not already added
if subst in found_substitutes and subst not in found:
found.append(subst)
found_substitutes.remove(subst)
# check if synonyms of substitute were found
# check if ingredient has synonyms
if subst in synonyms_dict.keys():
for synon in synonyms_dict[subst]:
if synon in found_substitutes:
if synon not in found and subst not in found:
found.append(subst)
found_substitutes.remove(synon)
# if base_ingred == "Erdbeere":
print(base_ingred + ": " + str(found_substitutes))
found_ground_ingr[base_ingred] = found
# print(base_ingred + ": ")
# if get_occurrences:
# print("occurrences in dataset: " + str(occurrences))
# print("number of found substitutes: " + str(len(found)) + "/" + str(len(ground_truth_dict[base_ingred])))
# print("correctly found substitutes: " + str(len(found)) + "/" + str(len(found) + len(found_substitutes)))
# print("correctly found substitutes: " + str(found))
# print("incorrectly found substitutes: " + str(found_substitutes))
# print("-----------------------------\n")
if len(found) > 0:
average_precision += len(found)/(len(found) + len(found_substitutes))
# print(len(found))
average_recall += len(found)/len(ground_truth_dict[base_ingred])
correctly_found += len(found)
incorrectly_found += len(found_substitutes)
number_correct_subs_found_overall.append(len(found))
total_number_subs_found_overall.append(len(found) + len(found_substitutes))
print("average precision: " + str(average_precision/40))
print("average recall: " + str(average_recall/40))
print("median number of correctly found subs: " + str(statistics.median(number_correct_subs_found_overall)))
print("median number of found subs overall: " + str(statistics.median(total_number_subs_found_overall)))
return found_ground_ingr
def merge_lists(all_lists):
max_len = 0
min_len = 99999
output = []
for curr_list in all_lists:
if len(curr_list) < min_len:
min_len = len(curr_list)
if len(curr_list) > max_len:
max_len = len(curr_list)
for index_counter in range(max_len):
for curr_list in all_lists:
if index_counter < len(curr_list):
if curr_list[index_counter] not in output:
output.append(curr_list[index_counter])
return output
def with_synonyms(ground_truth_dict=None, found_substitutes_dict=None, get_occurrences=True, synonyms=True):
if get_occurrences:
with open(data_path + occurances_path, "r") as whole_json_file:
occurrences_dict = json.load(whole_json_file)
if not ground_truth_dict:
with open(data_path+ground_truth_path, "r") as whole_json_file:
ground_truth_dict = json.load(whole_json_file)
if synonyms:
with open(data_path + synonyms_path, "r") as whole_json_file:
synonyms_dict = json.load(whole_json_file)
else:
synonyms_dict = {}
if not found_substitutes_dict:
with open(found_substitutes_path, "r") as whole_json_file:
model_substitutes_dict = json.load(whole_json_file)
else:
model_substitutes_dict = found_substitutes_dict
correctly_found = 0
incorrectly_found = 0
average_precision = 0.0
average_recall = 0.0
number_correct_subs_found_overall = []
total_number_subs_found_overall = []
found_ground_ingr = {}
# base ingredient with synonyms, substitutes with synonyms
for base_ingred in ground_truth_dict.keys():
base_synonyms = [base_ingred]
if get_occurrences:
occurrences = 0
# get list of all synonyms of base ingredient
if base_ingred in synonyms_dict.keys():
synonyms = synonyms_dict[base_ingred]
base_synonyms = base_synonyms + synonyms
found_substitutes = []
all_substitutes = []
# get top 30 substitutes of each base synonym
for synon in base_synonyms:
if get_occurrences:
occurrences += occurrences_dict[synon]
all_substitutes.append(model_substitutes_dict[synon].copy())
# synon_subs = model_substitutes_dict[synon].copy()
# if len(synon_subs) > 30:
# synon_subs = synon_subs[:30]
# for sub in synon_subs:
# if sub not in found_substitutes:
# found_substitutes.append(sub)
found_substitutes = merge_lists(all_substitutes)
else:
found_substitutes = model_substitutes_dict[base_ingred].copy()
if len(found_substitutes) > 30:
found_substitutes = found_substitutes[:30]
found = []
# remove all base synonyms from found substitutes
new_found_substitutes = []
for subst in found_substitutes:
if subst not in base_synonyms:
new_found_substitutes.append(subst)
found_substitutes = new_found_substitutes
# check which substitutes were found
for subst in ground_truth_dict[base_ingred]:
# only add substitute if not already added
if subst in found_substitutes and subst not in found:
found.append(subst)
found_substitutes.remove(subst)
# check if synonyms of substitute were found
# check if ingredient has synonyms
if subst in synonyms_dict.keys():
for synon in synonyms_dict[subst]:
if synon in found_substitutes:
if synon not in found and subst not in found:
found.append(subst)
found_substitutes.remove(synon)
found_ground_ingr[base_ingred] = found
# print(base_ingred + ": ")
# if get_occurrences:
# print("occurrences in dataset: " + str(occurrences))
# print("number of synonyms incl. original word: " + str(len(base_synonyms)))
# print("number of found substitutes: " + str(len(found)) + "/" + str(len(ground_truth_dict[base_ingred])))
# print("correctly found substitutes: " + str(len(found)) + "/" + str(len(found) + len(found_substitutes)))
# print("correctly found substitutes: " + str(found))
# print("incorrectly found substitutes: " + str(found_substitutes))
# print("-----------------------------\n")
if len(found) > 0:
average_precision += len(found) / (len(found) + len(found_substitutes))
average_recall += len(found) / len(ground_truth_dict[base_ingred])
correctly_found += len(found)
incorrectly_found += len(found_substitutes)
number_correct_subs_found_overall.append(len(found))
total_number_subs_found_overall.append(len(found) + len(found_substitutes))
print("average precision: " + str(average_precision / 40))
print("average recall: " + str(average_recall / 40))
print("median number of correctly found subs: " + str(statistics.median(number_correct_subs_found_overall)))
print("median number of found subs overall: " + str(statistics.median(total_number_subs_found_overall)))
return found_ground_ingr
def translate_engl_ground_truth(ground_truth, ger_transl):
new_ground_truth = {}
for base_ingr in ground_truth.keys():
new_ground_truth[ger_transl[base_ingr]] = []
for subst in ground_truth[base_ingr]:
if subst in ger_transl.keys():
new_ground_truth[ger_transl[base_ingr]].append(ger_transl[subst])
return new_ground_truth
def with_base_synonyms(ground_truth_dict=None, found_substitutes_dict=None, get_occurrences=True, synonyms=True):
if get_occurrences:
with open(data_path + occurances_path, "r") as whole_json_file:
occurrences_dict = json.load(whole_json_file)
if not ground_truth_dict:
with open(data_path+ground_truth_path, "r") as whole_json_file:
ground_truth_dict = json.load(whole_json_file)
if synonyms:
with open(data_path + synonyms_path, "r") as whole_json_file:
synonyms_dict = json.load(whole_json_file)
else:
synonyms_dict = {}
if not found_substitutes_dict:
with open(found_substitutes_path, "r") as whole_json_file:
model_substitutes_dict = json.load(whole_json_file)
else:
model_substitutes_dict = found_substitutes_dict
found_ground_ingr = {}
# base ingredient with synonyms, substitutes with synonyms
for base_ingred in ground_truth_dict.keys():
base_synonyms = [base_ingred]
if get_occurrences:
occurrences = 0
# get list of all synonyms of base ingredient
if base_ingred in synonyms_dict.keys():
synonyms = synonyms_dict[base_ingred]
base_synonyms = base_synonyms + synonyms
found_substitutes = []
all_substitutes = []
# get top 30 substitutes of each base synonym
for synon in base_synonyms:
if get_occurrences:
occurrences += occurrences_dict[synon]
all_substitutes.append(model_substitutes_dict[synon].copy())
found_substitutes = merge_lists(all_substitutes)
else:
found_substitutes = model_substitutes_dict[base_ingred].copy()
if len(found_substitutes) > 30:
found_substitutes = found_substitutes[:30]
found = []
# remove all base synonyms from found substitutes
new_found_substitutes = []
for subst in found_substitutes:
if subst not in base_synonyms:
new_found_substitutes.append(subst)
found_substitutes = new_found_substitutes
# check which substitutes were found
for subst in ground_truth_dict[base_ingred]:
# only add substitute if not already added
if subst in found_substitutes and subst not in found:
found.append(subst)
found_substitutes.remove(subst)
# check if synonyms of substitute were found
# check if ingredient has synonyms
# if subst in synonyms_dict.keys():
# for synon in synonyms_dict[subst]:
# if synon in found_substitutes:
# if synon not in found and subst not in found:
# found.append(subst)
# found_substitutes.remove(synon)
found_ground_ingr[base_ingred] = found
print(base_ingred + ": ")
if get_occurrences:
print("occurrences in dataset: " + str(occurrences))
print("number of synonyms incl. original word: " + str(len(base_synonyms)))
print("number of found substitutes: " + str(len(found)) + "/" + str(len(ground_truth_dict[base_ingred])))
print("correctly found substitutes: " + str(len(found)) + "/" + str(len(found) + len(found_substitutes)))
print("correctly found substitutes: " + str(found))
print("incorrectly found substitutes: " + str(found_substitutes))
print("-----------------------------\n")
return found_ground_ingr
def engl_compare():
# with open(data_path + occurances_path, "r") as whole_json_file:
# occurrences_dict = json.load(whole_json_file)
with open(engl_data_path + "translation.json", "r") as whole_json_file:
ger_transl = json.load(whole_json_file)
# with open(data_path + synonyms_path, "r") as whole_json_file:
# synonyms_dict = json.load(whole_json_file)
with open(found_substitutes_path, "r") as whole_json_file:
model_substitutes_dict = json.load(whole_json_file)
with open(engl_data_path + "substitute_pairs_foodbert_text.json", "r") as whole_json_file:
engl_list = json.load(whole_json_file)
with open(engl_data_path + "engl_ground_truth.json", "r") as whole_json_file:
engl_ground_truth = json.load(whole_json_file)
engl_dict = {}
for foo in engl_list:
if foo[0] in engl_dict.keys():
engl_dict[foo[0]].append(foo[1])
else:
engl_dict[foo[0]] = [foo[1]]
translated_ground_truth = translate_engl_ground_truth(engl_ground_truth, ger_transl)
# without any synonyms
print("Engl compare without any synonyms:")
engl_replacements = {}
# ger_replacements = {}
for ingred in engl_ground_truth.keys():
found = []
incorr = []
found_ger = []
incorr_ger = []
engl_replacements[ingred] = {}
engl_replacements[ingred]["engl"] = 0
engl_replacements[ingred]["ger"] = 0
# ger_replacements[ingred] = 0
if ingred in engl_dict.keys():
for sub in engl_ground_truth[ingred]:
if sub in engl_dict[ingred]:
engl_replacements[ingred]["engl"] += 1
found.append(sub)
if ger_transl[ingred] in model_substitutes_dict.keys():
for sub in german_ground_truth[ger_transl[ingred]]:
if sub in model_substitutes_dict[ger_transl[ingred]]:
engl_replacements[ingred]["ger"] += 1
found_ger.append(sub)
# ger_replacements[ingred] += 1
for found_sub in engl_dict[ingred]:
if found_sub not in engl_ground_truth[ingred]:
incorr.append(found_sub)
for found_sub in model_substitutes_dict[ger_transl[ingred]]:
if found_sub not in translated_ground_truth[ger_transl[ingred]]:
incorr_ger.append(found_sub)
print(ger_transl[ingred] + ": ")
print("number of found substitutes: " + str(len(found_ger)) + "/" + str(len(translated_ground_truth[ger_transl[ingred]])))
print("correctly found substitutes: " + str(len(found_ger)) + "/" + str(len(found_ger) + len(incorr_ger)))
print("correctly found substitutes: " + str(found_ger))
print("incorrectly found substitutes: " + str(incorr_ger))
print("-----------------------------\n")
print(ingred + ": ")
print("number of found substitutes: " + str(len(found)) + "/" + str(len(engl_ground_truth[ingred])))
print("correctly found substitutes: " + str(len(found)) + "/" + str(len(found) + len(incorr)))
print("correctly found substitutes: " + str(found))
print("incorrectly found substitutes: " + str(incorr))
print("-----------------------------\n")
with open(evaluation_path + "engl_comparison_results/engl_no_syn.json", 'w') as f:
json.dump(engl_replacements, f, ensure_ascii=False, indent=4)
# with synonyms of substitutes
print("Engl compare with synonyms of substitutes only:")
# german
new_german_result = no_synonyms(ground_truth_dict=translated_ground_truth, get_occurrences=False)
#engl
new_engl_result = no_synonyms(ground_truth_dict=engl_ground_truth, found_substitutes_dict=engl_dict, get_occurrences=False, synonyms=False)
engl_replacements = {}
for ingred in engl_ground_truth.keys():
engl_replacements[ingred] = {}
engl_replacements[ingred]["engl"] = 0
engl_replacements[ingred]["ger"] = 0
if ingred in new_engl_result.keys():
for sub in engl_ground_truth[ingred]:
if sub in new_engl_result[ingred]:
engl_replacements[ingred]["engl"] += 1
if ger_transl[ingred] in new_german_result.keys():
for sub in german_ground_truth[ger_transl[ingred]]:
if sub in new_german_result[ger_transl[ingred]]:
engl_replacements[ingred]["ger"] += 1
with open(evaluation_path + "engl_comparison_results/engl_sub_syn.json", 'w') as f:
json.dump(engl_replacements, f, ensure_ascii=False, indent=4)
# with synonyms for substitutes and base words
print("Engl compare with synonyms of both:")
# german
new_german_result = with_synonyms(ground_truth_dict=translated_ground_truth, get_occurrences=False)
# engl
new_engl_result = with_synonyms(ground_truth_dict=engl_ground_truth, found_substitutes_dict=engl_dict, get_occurrences=False, synonyms=False)
engl_replacements = {}
for ingred in engl_ground_truth.keys():
engl_replacements[ingred] = {}
engl_replacements[ingred]["engl"] = 0
engl_replacements[ingred]["ger"] = 0
if ingred in new_engl_result.keys():
for sub in engl_ground_truth[ingred]:
if sub in new_engl_result[ingred]:
engl_replacements[ingred]["engl"] += 1
if ger_transl[ingred] in new_german_result.keys():
for sub in german_ground_truth[ger_transl[ingred]]:
if sub in new_german_result[ger_transl[ingred]]:
engl_replacements[ingred]["ger"] += 1
with open(evaluation_path + "engl_comparison_results/engl_all_syn.json", 'w') as f:
json.dump(engl_replacements, f, ensure_ascii=False, indent=4)
# with synonyms for base words
print("Engl compare with synonyms of base words only:")
# german
new_german_result = with_base_synonyms(ground_truth_dict=translated_ground_truth, get_occurrences=False)
# engl
new_engl_result = with_base_synonyms(ground_truth_dict=engl_ground_truth, found_substitutes_dict=engl_dict,
get_occurrences=False, synonyms=False)
engl_replacements = {}
for ingred in engl_ground_truth.keys():
engl_replacements[ingred] = {}
engl_replacements[ingred]["engl"] = 0
engl_replacements[ingred]["ger"] = 0
if ingred in new_engl_result.keys():
for sub in engl_ground_truth[ingred]:
if sub in new_engl_result[ingred]:
engl_replacements[ingred]["engl"] += 1
if ger_transl[ingred] in new_german_result.keys():
for sub in german_ground_truth[ger_transl[ingred]]:
if sub in new_german_result[ger_transl[ingred]]:
engl_replacements[ingred]["ger"] += 1
with open(evaluation_path + "engl_comparison_results/engl_base_syn.json", 'w') as f:
json.dump(engl_replacements, f, ensure_ascii=False, indent=4)
print("test")
def main():
# compare english and german results
# engl_compare()
print("--------------------------------------------------------")
print("--------------------------------------------------------")
print("--------------------------------------------------------\n")
# get results, synonyms only used in substitutes
no_synonyms()
print("--------------------------------------------------------")
print("--------------------------------------------------------")
print("--------------------------------------------------------\n")
# get results, synonyms used in substitutes and base ingredients
with_synonyms()
main()

16
final_Versions/README.md
View File

@@ -1,3 +1,19 @@
## German FoodBERT Models
Unzip German FoodBERT models here!
They can be found under https://cloud.marquis.site/s/ZUVIIIQv6yznBj6
## Datasets
Each model has a folder "dataset" with the following files:
**full_dataset.json**: Entire dataset with cleaned ingredients and instructions. This is the same file as found for each version in the main data directory.
**complete_dataset.json**: dataset containing only URLs and instructions, separated depending on the version
**model_datapoints.txt**: list of only the instruction datapoints from **complete_dataset.json**
**training_data.txt**: instruction datapoints from recipes set aside for training
**testing_data.txt**: instruction datapoints from recipes set aside for testing

4
train_model/README.md
View File

@@ -1,4 +1,4 @@
#Vocab
# Vocab
To create vocab.txt file, run **make_new_vocab.py**
# Prep dataset
@@ -8,7 +8,7 @@ To create vocab.txt file, run **make_new_vocab.py**
**language_modeling**
#Vocab Files:
# Vocab Files:
**bert-base-german-cased_tokenizer.json**: original bert-base-german-cased tokenizer file
**bert_vocab.txt**: original bert-base-german-cased vocab
**used_ingredients**: all ingredients in dataset