first push

.gitattributes

@@ -51,3 +51,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text

create_multilingual_simCSE.py

@@ -0,0 +1,85 @@
+import json
+import jsonlines
+import argparse
+import pandas as pd
+import pprint
+from random import *
+
+def main(args):
+    mainDict = {}
+    with jsonlines.open(args.norwegian_input_file) as reader:
+        for obj in reader:
+            #Check if the value already exists
+            neutral = ""
+            entailment = ""
+            contradiction = ""
+            prompt = ""
+
+            if mainDict.get(obj['promptID'], None):
+                prompt = obj['sentence1']
+                entailment = mainDict[obj['promptID']].get('entailment','')
+                contradiction = mainDict[obj['promptID']].get('contradiction','')
+                neutral = mainDict[obj['promptID']].get('neutral','')
+
+            if obj['gold_label'] == "neutral":
+                neutral = obj['sentence2']
+            elif obj['gold_label'] == "contradiction":
+                contradiction =  obj['sentence2']
+            elif obj['gold_label'] == "entailment":
+                entailment = obj['sentence2']
+            
+            mainDict[obj['promptID']] = {'prompt': prompt, 'entailment' : entailment, 'neutral' : neutral, 'contradiction' : contradiction}
+    
+    myList = []
+    for promptID in mainDict:
+        myList.append({'sent0':mainDict[promptID]['prompt'], 'sent1':mainDict[promptID]['entailment'], 'hard_neg':mainDict[promptID]['contradiction']})
+    
+    df = pd.DataFrame.from_records(myList)        
+    
+    #Drop empty
+    df.replace("", float("NaN"), inplace=True)
+    df.dropna(subset = ["sent0","sent1","hard_neg"], inplace=True)
+    
+    #Get the english file
+    english = pd.read_csv(args.english_input_file)
+
+    #Get the multilingual lookup file
+    multi = pd.read_csv(args.multilingual_input_file, sep='\t')
+    multi.columns=['id','english','norwegian']
+    multi['english'] = multi['english'].str.strip()
+    multi['norwegian'] = multi['norwegian'].str.strip()
+    
+    #Create dictionary
+    mydict = {}
+    for index,row in multi.iterrows():
+        mydict[row['norwegian']] = row['english']
+
+    merged = df.copy()
+    for index, row in merged.iterrows():
+        flip = randint(1,6)
+        if flip == 3 or flip == 4 or flip == 5:
+            merged.at[index, 'sent0'] = mydict.get(row['sent0'],row['sent0'])
+        
+        if flip == 2 or flip == 4 or flip == 6:
+            merged.at[index, 'sent1'] = mydict.get(row['sent1'],row['sent1'])
+        
+        if flip == 1 or flip == 5 or flip == 6:
+            merged.at[index,'hard_neg'] = mydict.get(row['hard_neg'],row['hard_neg'])
+        
+    
+    combined = pd.concat([df, english, merged])
+    
+    combined = combined.sample(frac=1).reset_index(drop=True)
+
+    #Save csv
+    combined.to_csv(args.output_file, index=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--norwegian_input_file', help="Norwegian json file.", required=True)
+    parser.add_argument('--english_input_file', help="English csv file.", required=True)
+    parser.add_argument('--multilingual_input_file', help="Multilingual tsv file.", required=True)
+    parser.add_argument('--output_file', help="Output file.", required=True)
+    
+    args = parser.parse_args()
+    main(args)

create_multilingual_small_simCSE.py

@@ -0,0 +1,84 @@
+import json
+import jsonlines
+import argparse
+import pandas as pd
+import pprint
+from random import *
+
+def main(args):
+    mainDict = {}
+    with jsonlines.open(args.norwegian_input_file) as reader:
+        for obj in reader:
+            #Check if the value already exists
+            neutral = ""
+            entailment = ""
+            contradiction = ""
+            prompt = ""
+
+            if mainDict.get(obj['promptID'], None):
+                prompt = obj['sentence1']
+                entailment = mainDict[obj['promptID']].get('entailment','')
+                contradiction = mainDict[obj['promptID']].get('contradiction','')
+                neutral = mainDict[obj['promptID']].get('neutral','')
+
+            if obj['gold_label'] == "neutral":
+                neutral = obj['sentence2']
+            elif obj['gold_label'] == "contradiction":
+                contradiction =  obj['sentence2']
+            elif obj['gold_label'] == "entailment":
+                entailment = obj['sentence2']
+            
+            mainDict[obj['promptID']] = {'prompt': prompt, 'entailment' : entailment, 'neutral' : neutral, 'contradiction' : contradiction}
+    
+    myList = []
+    for promptID in mainDict:
+        myList.append({'sent0':mainDict[promptID]['prompt'], 'sent1':mainDict[promptID]['entailment'], 'hard_neg':mainDict[promptID]['contradiction']})
+    
+    df = pd.DataFrame.from_records(myList)        
+    
+    #Drop empty
+    df.replace("", float("NaN"), inplace=True)
+    df.dropna(subset = ["sent0","sent1","hard_neg"], inplace=True)
+    
+    #Get the english file
+    english = pd.read_csv(args.english_input_file)
+
+    #Get the multilingual lookup file
+    #multi = pd.read_csv(args.multilingual_input_file, sep='\t')
+    #multi.columns=['id','english','norwegian']
+    #multi['english'] = multi['english'].str.strip()
+    #multi['norwegian'] = multi['norwegian'].str.strip()
+    
+    #Create dictionary
+    #mydict = {}
+    #for index,row in multi.iterrows():
+    #    mydict[row['norwegian']] = row['english']
+
+    #merged = df.copy()
+    #for index, row in merged.iterrows():
+    #    flip = randint(1,6)
+    #    if flip == 3 or flip == 4 or flip == 5:
+    #        merged.at[index, 'sent0'] = mydict.get(row['sent0'],row['sent0'])
+    #    
+    #    if flip == 2 or flip == 4 or flip == 6:
+    #        merged.at[index, 'sent1'] = mydict.get(row['sent1'],row['sent1'])
+    #    
+    #    if flip == 1 or flip == 5 or flip == 6:
+    #        merged.at[index,'hard_neg'] = mydict.get(row['hard_neg'],row['hard_neg'])
+        
+    
+    combined = pd.concat([df, english])
+    
+    combined = combined.sample(frac=1).reset_index(drop=True)
+
+    #Save csv
+    combined.to_csv(args.output_file, index=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--norwegian_input_file', help="Norwegian json file.", required=True)
+    parser.add_argument('--english_input_file', help="English csv file.", required=True)
+    parser.add_argument('--output_file', help="Output file.", required=True)
+    
+    args = parser.parse_args()
+    main(args)

create_simCSE.py

@@ -0,0 +1,51 @@
+import json
+import jsonlines
+import argparse
+import pandas as pd
+import pprint
+
+def main(args):
+    mainDict = {}
+    with jsonlines.open(args.input_file) as reader:
+        for obj in reader:
+            #Check if the value already exists
+            neutral = ""
+            entailment = ""
+            contradiction = ""
+            prompt = ""
+
+            if mainDict.get(obj['promptID'], None):
+                prompt = obj['sentence1']
+                entailment = mainDict[obj['promptID']].get('entailment','')
+                contradiction = mainDict[obj['promptID']].get('contradiction','')
+                neutral = mainDict[obj['promptID']].get('neutral','')
+
+            if obj['gold_label'] == "neutral":
+                neutral = obj['sentence2']
+            elif obj['gold_label'] == "contradiction":
+                contradiction =  obj['sentence2']
+            elif obj['gold_label'] == "entailment":
+                entailment = obj['sentence2']
+            
+            mainDict[obj['promptID']] = {'prompt': prompt, 'entailment' : entailment, 'neutral' : neutral, 'contradiction' : contradiction}
+    
+    myList = []
+    for promptID in mainDict:
+        myList.append({'sent0':mainDict[promptID]['prompt'], 'sent1':mainDict[promptID]['entailment'], 'hard_neg':mainDict[promptID]['contradiction']})
+    
+    df = pd.DataFrame.from_records(myList)        
+    
+    #Drop empty
+    df.replace("", float("NaN"), inplace=True)
+    df.dropna(subset = ["sent0","sent1","hard_neg"], inplace=True)
+    
+    #Save csv
+    df.to_csv(args.output_file, index=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_file', help="Input file.", required=True)
+    parser.add_argument('--output_file', help="Output file.", required=True)
+    
+    args = parser.parse_args()
+    main(args)

mnli_no_en_for_simcse.csv

@@ -0,0 +1,3 @@
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+size 106691470

mnli_no_en_small_for_simcse.csv

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+oid sha256:1f99e56cc162bdd808e619ac6d73e6441f47eb848973d685d360bb4c99000806
+size 77804651

mnli_no_for_simcse.csv

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+oid sha256:d3a3d7fb9905ec57c1bc62a578c88002dc9a20f3ebcbd2c675c1a549172aadf8
+size 28826475

multinli_1.0_dev_matched_no_mt.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f0e9f90a0967a2d5e650db95a16cb7b0fa8f9c8822eae4a30c9c7baf8119bf4a
+size 4755492

multinli_1.0_dev_mismatched_no_mt.jsonl

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+size 4856954

multinli_1.0_train_no_mt.jsonl

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+oid sha256:d333b35562b2293ad9917a108968a0a6d0ceca27098376bfe32fd6879d8285cf
+size 187700447

nli_for_simcse.csv

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+oid sha256:0747687ec3594fa449d2004fd3757a56c24bf5f7428976fb5b67176775a68d48
+size 48978197

xnli_dev_no_mt.jsonl

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+oid sha256:46ea82fab04f63b8e783f7a55a165c9181e63d8c0c098018ed01f39d87dfeab4
+size 1193913

xnli_test_no_mt.jsonl

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+oid sha256:815aba6a5b452cd9a82bc8c037e124ab5cd46548ebca1df74665dd622aef74ca
+size 2402537