Update app.py
Browse files
app.py
CHANGED
@@ -8,7 +8,7 @@ from sentence_transformers import SentenceTransformer
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def load_model(model_name):
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if model_name == "GLuCoSE-base-ja-v2":
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return SentenceTransformer("pkshatech/GLuCoSE-base-ja-v2")
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elif model_name == "RoSEtta-base
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return SentenceTransformer("pkshatech/RoSEtta-base", trust_remote_code=True)
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elif model_name == "ruri-large":
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return SentenceTransformer("cl-nagoya/ruri-large")
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@@ -17,20 +17,21 @@ def load_model(model_name):
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def get_similarities(model_name, sentences):
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model = load_model(model_name)
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if model_name
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sentences = [
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"クエリ: " + s if i % 2 == 0 else "文章: " + s
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for i, s in enumerate(sentences)
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]
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embeddings = model.encode(sentences, convert_to_tensor=True)
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else: # ruri-large
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similarities = F.cosine_similarity(
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embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2
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)
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return similarities.cpu().numpy()
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@@ -45,7 +46,7 @@ def process_input(model_name, input_text):
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return format_similarities(similarities)
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models = ["GLuCoSE-base-ja-v2", "RoSEtta-base
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with gr.Blocks() as demo:
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gr.Markdown("# Sentence Similarity Demo")
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@@ -55,7 +56,14 @@ with gr.Blocks() as demo:
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model_dropdown = gr.Dropdown(
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choices=models, label="Select Model", value=models[0]
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)
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input_text = gr.Textbox(
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submit_btn = gr.Button(value="Calculate Similarities")
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with gr.Column():
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@@ -69,11 +77,11 @@ with gr.Blocks() as demo:
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examples=[
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[
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"GLuCoSE-base-ja-v2",
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"
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],
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[
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"RoSEtta-base
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"
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],
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[
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"ruri-large",
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def load_model(model_name):
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if model_name == "GLuCoSE-base-ja-v2":
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return SentenceTransformer("pkshatech/GLuCoSE-base-ja-v2")
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elif model_name == "RoSEtta-base":
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return SentenceTransformer("pkshatech/RoSEtta-base", trust_remote_code=True)
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elif model_name == "ruri-large":
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return SentenceTransformer("cl-nagoya/ruri-large")
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def get_similarities(model_name, sentences):
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model = load_model(model_name)
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if model_name in ["GLuCoSE-base-ja-v2", "RoSEtta-base"]:
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sentences = [
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"query: " + s if i % 2 == 0 else "passage: " + s
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for i, s in enumerate(sentences)
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]
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elif model_name == "ruri-large":
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sentences = [
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"クエリ: " + s if i % 2 == 0 else "文章: " + s
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for i, s in enumerate(sentences)
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]
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embeddings = model.encode(sentences, convert_to_tensor=True)
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similarities = F.cosine_similarity(
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embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2
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)
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return similarities.cpu().numpy()
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return format_similarities(similarities)
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models = ["GLuCoSE-base-ja-v2", "RoSEtta-base", "ruri-large"]
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with gr.Blocks() as demo:
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gr.Markdown("# Sentence Similarity Demo")
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model_dropdown = gr.Dropdown(
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choices=models, label="Select Model", value=models[0]
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)
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input_text = gr.Textbox(
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lines=5,
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label="Input Sentences (one per line)",
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placeholder="Enter query and passage pairs, alternating lines.",
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)
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gr.Markdown("""
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**Note:** Prefixes ('query:' / 'passage:' or 'クエリ:' / '文章:') are added automatically. Just input your sentences.
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""")
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submit_btn = gr.Button(value="Calculate Similarities")
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with gr.Column():
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examples=[
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[
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"GLuCoSE-base-ja-v2",
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"PKSHAはどんな会社ですか?\n研究開発したアルゴリズムを、多くの企業のソフトウエア・オペレーションに導入しています。",
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],
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[
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"RoSEtta-base",
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"PKSHAはどんな会社ですか?\n研究開発したアルゴリズムを、多くの企業のソフトウエア・オペレーションに導入しています。",
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],
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[
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"ruri-large",
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