Initial dissertation evaluation results upload

ZambiaABSA_classification_report.txt

@@ -1,9 +1,9 @@
               precision    recall  f1-score   support
 
-    negative     0.3933    1.0000    0.5645      1183
-     neutral     0.0000    0.0000    0.0000       344
-    positive     0.0000    0.0000    0.0000      1481
+    negative     0.7939    0.8529    0.8223      1183
+     neutral     0.4444    0.2558    0.3247       344
+    positive     0.8324    0.8650    0.8483      1481
 
-    accuracy                         0.3933      3008
-   macro avg     0.1311    0.3333    0.1882      3008
-weighted avg     0.1547    0.3933    0.2220      3008
+    accuracy                         0.7906      3008
+   macro avg     0.6902    0.6579    0.6651      3008
+weighted avg     0.7729    0.7906    0.7782      3008

ZambiaABSA_metrics.txt

@@ -1 +1 @@
-{'accuracy': 0.3932845744680851, 'precision_macro': 0.13109485815602837, 'recall_macro': 0.3333333333333333, 'f1_macro': 0.18818102282669213}
+{'accuracy': 0.7905585106382979, 'precision_macro': 0.6902220729432346, 'recall_macro': 0.6578954595597124, 'f1_macro': 0.6651328354796525}

ZambiaABSA_training_logs.csv

@@ -1,48 +1,30 @@
 loss,grad_norm,learning_rate,epoch,step,eval_loss,eval_accuracy,eval_precision_macro,eval_recall_macro,eval_f1_macro,eval_runtime,eval_samples_per_second,eval_steps_per_second,train_runtime,train_samples_per_second,train_steps_per_second,total_flos,train_loss
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+,,,6.0,1104,,,,,,,,,373.8431,47.25,2.953,0.0,0.24579338014861435

baselineABSA_classification_report.txt

@@ -1,9 +1,9 @@
               precision    recall  f1-score   support
 
-    negative     0.8151    0.8462    0.8304      1183
-     neutral     0.4903    0.3692    0.4212       344
-    positive     0.8435    0.8663    0.8548      1481
+    negative     0.7784    0.8107    0.7942      1183
+     neutral     0.5948    0.2006    0.3000       344
+    positive     0.7916    0.8872    0.8367      1481
 
-    accuracy                         0.8015      3008
-   macro avg     0.7163    0.6939    0.7021      3008
-weighted avg     0.7920    0.8015    0.7956      3008
+    accuracy                         0.7786      3008
+   macro avg     0.7216    0.6328    0.6436      3008
+weighted avg     0.7639    0.7786    0.7586      3008

baselineABSA_metrics.txt

@@ -1 +1 @@
-{'accuracy': 0.801529255319149, 'precision_macro': 0.7163393558407339, 'recall_macro': 0.6938821474313679, 'f1_macro': 0.7021171781581792}
+{'accuracy': 0.7785904255319149, 'precision_macro': 0.7216009807254095, 'recall_macro': 0.6328235451291175, 'f1_macro': 0.6436263721052559}

baselineABSA_training_logs.csv

@@ -1,48 +1,30 @@
 loss,grad_norm,learning_rate,epoch,step,eval_loss,eval_accuracy,eval_precision_macro,eval_recall_macro,eval_f1_macro,eval_runtime,eval_samples_per_second,eval_steps_per_second,train_runtime,train_samples_per_second,train_steps_per_second,total_flos,train_loss
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+0.1413,2.2179958820343018,7.318840579710146e-06,3.8043478260869565,700,,,,,,,,,,,,,
+,,,4.0,736,0.46895599365234375,0.899390243902439,0.7886295386295386,0.7541782633379027,0.7690249853936831,1.5784,207.804,13.305,,,,,
+0.095,0.2704041004180908,6.41304347826087e-06,4.076086956521739,750,,,,,,,,,,,,,
+0.0983,0.09123282134532928,5.507246376811595e-06,4.3478260869565215,800,,,,,,,,,,,,,
+0.091,3.4786059856414795,4.601449275362319e-06,4.619565217391305,850,,,,,,,,,,,,,
+0.1316,0.0942794531583786,3.6956521739130436e-06,4.891304347826087,900,,,,,,,,,,,,,
+,,,5.0,920,0.5067058801651001,0.9024390243902439,0.8301012115839242,0.7986264002547806,0.8129218692717431,1.5647,209.622,13.421,,,,,
+0.0714,33.246986389160156,2.7898550724637686e-06,5.163043478260869,950,,,,,,,,,,,,,
+0.0543,32.3036003112793,1.884057971014493e-06,5.434782608695652,1000,,,,,,,,,,,,,
+0.0499,50.19234085083008,9.782608695652175e-07,5.706521739130435,1050,,,,,,,,,,,,,
+0.065,13.91033935546875,7.246376811594204e-08,5.978260869565218,1100,,,,,,,,,,,,,
+,,,6.0,1104,0.5527961254119873,0.8902439024390244,0.7915659208024198,0.7925892078261952,0.7920516465292584,1.8008,182.138,11.661,,,,,
+,,,6.0,1104,,,,,,,,,377.0434,46.849,2.928,0.0,0.25469970597170183

final_comparison_metrics.csv

@@ -1,3 +1,3 @@
 Model,Accuracy,F1 (Macro),Precision (Macro),Recall (Macro)
-baselineABSA,0.801529255319149,0.7021171781581792,0.7163393558407339,0.6938821474313679
-ZambiaABSA,0.3932845744680851,0.18818102282669213,0.13109485815602837,0.3333333333333333
+baselineABSA,0.7785904255319149,0.6436263721052559,0.7216009807254095,0.6328235451291175
+ZambiaABSA,0.7905585106382979,0.6651328354796525,0.6902220729432346,0.6578954595597124

final_per_language_metrics.csv

@@ -1,5 +1,5 @@
 language,baselineABSA_accuracy,ZambiaABSA_accuracy
-lusaka_slang,0.8461538461538461,0.6410256410256411
-bemba_cibemba,0.865546218487395,0.5462184873949579
-english,0.8119914346895075,0.3957173447537473
-nyanja_cinyanja,0.7359223300970874,0.32815533980582523
+lusaka_slang,0.8461538461538461,0.8205128205128205
+bemba_cibemba,0.8319327731092437,0.8151260504201681
+english,0.782441113490364,0.7961456102783726
+nyanja_cinyanja,0.7436893203883496,0.7572815533980582

inference_demo_results.csv

@@ -1,6 +1,6 @@
 text,aspect,baseline_prediction,zambia_prediction
-Yango drivers are rude but prices are good,driver_behavior,positive,negative
-Yango drivers are rude but prices are good,pricing,positive,negative
+Yango drivers are rude but prices are good,driver_behavior,neutral,negative
+Yango drivers are rude but prices are good,pricing,neutral,negative
 The app keeps crashing and payment failed twice,app_performance,negative,negative
 The app keeps crashing and payment failed twice,payment,negative,negative
 Ride was smooth but waiting time was too long,ride_quality,positive,negative
@@ -9,17 +9,17 @@ Yango driver alitumpa but price yali bwino,driver_behavior,positive,negative
 Yango driver alitumpa but price yali bwino,pricing,positive,negative
 App yakweba sana today,app_performance,negative,negative
 Nalolela sana motoka ukufika,waiting_time,negative,negative
-Driver wa Yango anali rude koma mtengo unali bwino,driver_behavior,positive,negative
-Driver wa Yango anali rude koma mtengo unali bwino,pricing,positive,negative
+Driver wa Yango anali rude koma mtengo unali bwino,driver_behavior,negative,negative
+Driver wa Yango anali rude koma mtengo unali bwino,pricing,negative,negative
 Kudikira galimoto kwatenga nthawi yayitali,waiting_time,negative,negative
 Ulendo unali wabwino kwambiri,ride_quality,positive,negative
 That driver performed butah,driver_behavior,negative,negative
-Service ya Yango ni fire,service_quality,positive,negative
+Service ya Yango ni fire,service_quality,neutral,negative
 Prices za Yango ni stress these days,pricing,negative,negative
 Pickup yadelaya crazy,waiting_time,negative,negative
-"App ya Yango ikunyoza today, payment yachita bounce",app_performance,negative,negative
-"App ya Yango ikunyoza today, payment yachita bounce",payment,negative,negative
-Prices za Yango zili on point but drivers ni stress,pricing,negative,negative
-Prices za Yango zili on point but drivers ni stress,driver_behavior,positive,negative
-Ride ilikuwa smooth sana but app yakweba pa booking,ride_quality,negative,negative
-Ride ilikuwa smooth sana but app yakweba pa booking,app_performance,negative,negative
+"App ya Yango ikunyoza today, payment yachita bounce",app_performance,positive,negative
+"App ya Yango ikunyoza today, payment yachita bounce",payment,positive,negative
+Prices za Yango zili on point but drivers ni stress,pricing,neutral,negative
+Prices za Yango zili on point but drivers ni stress,driver_behavior,neutral,negative
+Ride ilikuwa smooth sana but app yakweba pa booking,ride_quality,neutral,negative
+Ride ilikuwa smooth sana but app yakweba pa booking,app_performance,neutral,negative

model_comparison_metrics.csv

@@ -1,3 +1,3 @@
 Model,Accuracy,F1 (Macro),Precision (Macro),Recall (Macro)
-baselineABSA,0.801529255319149,0.7021171781581792,0.7163393558407339,0.6938821474313679
-ZambiaABSA,0.3932845744680851,0.18818102282669213,0.13109485815602837,0.3333333333333333
+baselineABSA,0.7785904255319149,0.6436263721052559,0.7216009807254095,0.6328235451291175
+ZambiaABSA,0.7905585106382979,0.6651328354796525,0.6902220729432346,0.6578954595597124

model_complexity_analysis.csv

@@ -1,3 +1,3 @@
 Model,Total Parameters,Trainable Parameters,Non-Trainable Parameters
 baselineABSA,177855747,177855747,0
-ZambiaABSA,306941955,306941955,0
+ZambiaABSA,177855747,177855747,0

model_memory_footprint.csv

@@ -1,3 +1,3 @@
 Model,Approx_Memory_MB
 baselineABSA,678.47
-ZambiaABSA,1170.89
+ZambiaABSA,678.47

per_language_metrics.csv

@@ -1,5 +1,5 @@
 language,baselineABSA_accuracy,ZambiaABSA_accuracy
-lusaka_slang,0.8461538461538461,0.6410256410256411
-bemba_cibemba,0.865546218487395,0.5462184873949579
-english,0.8119914346895075,0.3957173447537473
-nyanja_cinyanja,0.7359223300970874,0.32815533980582523
+lusaka_slang,0.8461538461538461,0.8205128205128205
+bemba_cibemba,0.8319327731092437,0.8151260504201681
+english,0.782441113490364,0.7961456102783726
+nyanja_cinyanja,0.7436893203883496,0.7572815533980582

statistical_significance_results.csv

@@ -1,2 +1,2 @@
 Statistic,P_Value,Significant
-945.6840452261307,1.1506090005204204e-207,True
+5.518018018018018,0.01882157163366623,True

training_dynamics_comparison.csv

@@ -1,11 +1,7 @@
 epoch,baselineABSA_f1,ZambiaABSA_f1
-1.0,0.5483122196868361,0.19233550253073028
-2.0,0.763840151047071,0.19233550253073028
-3.0,0.7730524266444166,0.19233550253073028
-4.0,0.7915135915135915,0.19233550253073028
-5.0,0.838416198599265,0.19233550253073028
-6.0,0.8158214672627061,0.19233550253073028
-7.0,0.8450703840371737,0.19233550253073028
-8.0,0.8307478088631303,0.19233550253073028
-9.0,0.8245274767855414,0.19233550253073028
-10.0,0.8579209138569869,0.19233550253073028
+1.0,0.5551485551485551,0.5532358169433887
+2.0,0.7924165252663192,0.7805411363710947
+3.0,0.7527839389541517,0.79439571273288
+4.0,0.7690249853936831,0.8091445224654642
+5.0,0.8129218692717431,0.7856705049022152
+6.0,0.7920516465292584,0.8270513352705132