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Update app.py
Browse files
app.py
CHANGED
@@ -113,7 +113,7 @@ def get_splade_cocondenser_representation(text):
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sorted_representation = sorted(meaningful_tokens.items(), key=lambda item: item[1], reverse=True)
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formatted_output = "SPLADE-cocondenser-distil
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if not sorted_representation:
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formatted_output += "No significant terms found for this input.\n"
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else:
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@@ -237,9 +237,9 @@ def get_splade_doc_representation(text):
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def predict_representation_explorer(model_choice, text):
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if model_choice == "SPLADE-cocondenser-distil (weighting and expansion)":
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return get_splade_cocondenser_representation(text)
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elif model_choice == "SPLADE-v3-
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return get_splade_lexical_representation(text)
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elif model_choice == "
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return get_splade_doc_representation(text)
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else:
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return "Please select a model."
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@@ -357,12 +357,12 @@ def format_sparse_vector_output(splade_vector, tokenizer, is_binary=False):
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# --- NEW/MODIFIED: Helper to get the correct vector function, tokenizer, and binary flag ---
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def get_model_assets(model_choice_str):
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if model_choice_str == "SPLADE-cocondenser-distil
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return get_splade_cocondenser_vector, tokenizer_splade, False, "SPLADE-cocondenser-distil
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elif model_choice_str == "SPLADE-v3-
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return get_splade_lexical_vector, tokenizer_splade_lexical, False, "SPLADE-v3-
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elif model_choice_str == "
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return get_splade_doc_vector, tokenizer_splade_doc, True, "
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else:
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return None, None, False, "Unknown Model"
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@@ -418,7 +418,7 @@ with gr.Blocks(title="SPLADE Demos") as demo:
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"Binary Encoder"
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],
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label="Choose Sparse Encoder",
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value="SPLADE-cocondenser-distil
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),
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gr.Textbox(
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lines=5,
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@@ -437,9 +437,9 @@ with gr.Blocks(title="SPLADE Demos") as demo:
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# Define the common model choices for cleaner code
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model_choices = [
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"SPLADE-cocondenser-distil
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"SPLADE-v3-
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"
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]
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gr.Interface(
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@@ -448,12 +448,12 @@ with gr.Blocks(title="SPLADE Demos") as demo:
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gr.Radio(
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model_choices,
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label="Choose Query Encoding Model",
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value="SPLADE-cocondenser-distil
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),
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gr.Radio(
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model_choices,
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label="Choose Document Encoding Model",
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value="SPLADE-cocondenser-distil
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),
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gr.Textbox(
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lines=3,
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sorted_representation = sorted(meaningful_tokens.items(), key=lambda item: item[1], reverse=True)
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formatted_output = "MLM encoder (SPLADE-cocondenser-distil):\n"
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if not sorted_representation:
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formatted_output += "No significant terms found for this input.\n"
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else:
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def predict_representation_explorer(model_choice, text):
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if model_choice == "SPLADE-cocondenser-distil (weighting and expansion)":
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return get_splade_cocondenser_representation(text)
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elif model_choice == "MLP encoder (SPLADE-v3-lexical)":
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return get_splade_lexical_representation(text)
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elif model_choice == "Binary encoder":
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return get_splade_doc_representation(text)
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else:
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return "Please select a model."
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# --- NEW/MODIFIED: Helper to get the correct vector function, tokenizer, and binary flag ---
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def get_model_assets(model_choice_str):
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if model_choice_str == "MLM encoder (SPLADE-cocondenser-distil)":
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return get_splade_cocondenser_vector, tokenizer_splade, False, "MLM encoder (SPLADE-cocondenser-distil)"
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elif model_choice_str == "MLP encoder (SPLADE-v3-lexical)":
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return get_splade_lexical_vector, tokenizer_splade_lexical, False, "MLP encoder (SPLADE-v3-lexical)"
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elif model_choice_str == "Binary encoder":
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return get_splade_doc_vector, tokenizer_splade_doc, True, "Binary encoder"
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else:
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return None, None, False, "Unknown Model"
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"Binary Encoder"
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],
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label="Choose Sparse Encoder",
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value="MLM encoder (SPLADE-cocondenser-distil)"
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),
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gr.Textbox(
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lines=5,
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# Define the common model choices for cleaner code
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model_choices = [
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"MLM encoder (SPLADE-cocondenser-distil)",
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"MLP encoder (SPLADE-v3-lexical)",
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"Binary encoder"
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]
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gr.Interface(
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gr.Radio(
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model_choices,
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label="Choose Query Encoding Model",
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value="MLM encoder (SPLADE-cocondenser-distil)" # Default value
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),
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gr.Radio(
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model_choices,
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label="Choose Document Encoding Model",
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value="MLM encoder (SPLADE-cocondenser-distil)" # Default value
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),
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gr.Textbox(
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lines=3,
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