removed sarcasm

will later be added into it's own space

app.py

@@ -8,7 +8,6 @@ import backend.home
 import backend.processor
 import backend.sa
 import backend.qa
-import backend.sarcasm
 
 st.set_page_config(
     page_title="TEST", page_icon="📖", initial_sidebar_state="expanded", layout="wide"

backend/home.py

@@ -16,7 +16,6 @@ def write():
     - Arabic Text Preprocessor: Test how text imput is treated by our preprocessor
     - Arabic Language Generation: Generate Arabic text using our AraGPT2 language models
     - Arabic Sentiment Analysis: Test the senitment analysis model that won the [Arabic Senitment Analysis competition @ KAUST](https://www.kaggle.com/c/arabic-sentiment-analysis-2021-kaust)
-    - Arabic Sarcasm Detection: Test MARBERT trained for sarcasm detection
     - Arabic Question Answering: Test our AraELECTRA QA capabilities
     """
     )

backend/services.py

@@ -216,7 +216,7 @@ class SentimentAnalyzer:
             # "sa_no_aoa_in_neutral": NewArabicPreprocessorBalanced(model_name='UBC-NLP/MARBERT'),
             # "sa_cnnbert": CNNMarbertArabicPreprocessor(model_name='UBC-NLP/MARBERT'),
             # "sa_sarcasm": SarcasmArabicPreprocessor(model_name='UBC-NLP/MARBERT'),
-            "sar_trial10": SarcasmArabicPreprocessor(model_name='UBC-NLP/MARBERT'),
+            # "sar_trial10": SarcasmArabicPreprocessor(model_name='UBC-NLP/MARBERT'),
             # "sa_no_AOA": NewArabicPreprocessorBalanced(model_name='UBC-NLP/MARBERT'),
         }
 
@@ -224,7 +224,7 @@ class SentimentAnalyzer:
             "sa_trial5_1": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_trial5_1",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_trial5_1")],
             # "sa_no_aoa_in_neutral": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_no_aoa_in_neutral",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_no_aoa_in_neutral")],
             # "sa_cnnbert": [CNNTextClassificationPipeline("{}/train_{}/best_model".format("sa_cnnbert",i), device=-1, return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_cnnbert")],
-            "sa_sarcasm": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_sarcasm",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_sarcasm")],
+            # "sa_sarcasm": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_sarcasm",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_sarcasm")],
             # "sar_trial10": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sar_trial10",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sar_trial10")],
             # "sa_no_AOA": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_no_AOA",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_no_AOA")],
         }