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app1.py

@@ -19,13 +19,30 @@ def load_model(config_path):
     return RetroReader.load(config_file=config_path)
 
 # Loading models
-model_base = load_model("configs/inference_en_electra_base.yaml")
-model_large = load_model("configs/inference_en_electra_large.yaml")
+model_electra_base = load_model("configs/inference_en_electra_base.yaml")
+model_electra_large = load_model("configs/inference_en_electra_large.yaml")
+model_roberta = load_model("configs/inference_en_roberta.yaml")
+model_distilbert = load_model("configs/inference_en_distilbert.yaml")
 
 def retro_reader_demo(query, context, model_choice):
-    model = model_base if model_choice == "Base" else model_large
+    # Choose the model based on the model_choice
+    if model_choice == "Electra Base":
+        model = model_electra_base
+    elif model_choice == "Electra Large":
+        model = model_electra_large
+    elif model_choice == "Roberta":
+        model = model_roberta
+    elif model_choice == "DistilBERT":
+        model = model_distilbert
+    else:
+        return "Invalid model choice"
+
+    # Generate outputs using the chosen model
     outputs = model(query=query, context=context, return_submodule_outputs=True)
+    
+    # Extract the answer
     answer = outputs[0]["id-01"] if outputs[0]["id-01"] else "No answer found"
+    
     return answer
 
 # Gradio app interface

configs/inference_en_distilbert.yaml

@@ -0,0 +1,43 @@
+RetroDataModelArguments:
+
+    # DataArguments
+    max_seq_length: 512
+    max_answer_length: 30
+    doc_stride: 128
+    return_token_type_ids: True
+    pad_to_max_length: True
+    preprocessing_num_workers: 5
+    overwrite_cache: False
+    version_2_with_negative: True
+    null_score_diff_threshold: 0.0
+    rear_threshold: 0.0
+    n_best_size: 20
+    use_choice_logits: False
+    start_n_top: -1
+    end_n_top: -1
+    beta1: 1
+    beta2: 1
+    best_cof: 1
+    
+    # ModelArguments
+    use_auth_token: False
+    
+    # SketchModelArguments
+    sketch_revision: en-distilbert-sketch
+    sketch_model_name: faori/retro_reeader
+    # sketch_model_mode: transfer
+    sketch_architectures: ElectraForSequenceClassification
+    
+    # IntensiveModelArguments
+    intensive_revision: en-distilbert-intensive1
+    intensive_model_name: faori/retro_reeader
+    # intensive_model_mode: transfer
+    intensive_architectures: ElectraForQuestionAnsweringAVPool
+    
+
+TrainingArguments:
+    output_dir: outputs
+    no_cuda: True # If you want to use cuda,
+                  # change `no_cuda` to False and `fp16` to True
+    per_device_train_batch_size: 1
+    per_device_eval_batch_size: 12

configs/inference_en_roberta.yaml

@@ -0,0 +1,43 @@
+RetroDataModelArguments:
+
+    # DataArguments
+    max_seq_length: 512
+    max_answer_length: 30
+    doc_stride: 128
+    return_token_type_ids: True
+    pad_to_max_length: True
+    preprocessing_num_workers: 5
+    overwrite_cache: False
+    version_2_with_negative: True
+    null_score_diff_threshold: 0.0
+    rear_threshold: 0.0
+    n_best_size: 20
+    use_choice_logits: False
+    start_n_top: -1
+    end_n_top: -1
+    beta1: 1
+    beta2: 1
+    best_cof: 1
+    
+    # ModelArguments
+    use_auth_token: False
+    
+    # SketchModelArguments
+    sketch_revision: en-roberta-sketch
+    sketch_model_name: faori/retro_reeader
+    # sketch_model_mode: transfer
+    sketch_architectures: ElectraForSequenceClassification
+    
+    # IntensiveModelArguments
+    intensive_revision: en-roberta-intensive
+    intensive_model_name: faori/retro_reeader
+    # intensive_model_mode: transfer
+    intensive_architectures: ElectraForQuestionAnsweringAVPool
+    
+
+TrainingArguments:
+    output_dir: outputs
+    no_cuda: True # If you want to use cuda,
+                  # change `no_cuda` to False and `fp16` to True
+    per_device_train_batch_size: 1
+    per_device_eval_batch_size: 12