GPalomeque
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2a1ebf7
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Parent(s):
a3bfa46
Update app.py
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app.py
CHANGED
@@ -2,6 +2,8 @@ import gradio as gr
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from transformers import AutoModelForTokenClassification,AutoModelForSequenceClassification, AutoTokenizer, pipeline
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title = "Modelo Jurídico Mexicano"
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description = """
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@@ -100,22 +102,39 @@ def clasifica_conv_americana(example):
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return {i["label"]: float(i["score"]) for i in results}
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def process(example):
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entidades = get_entities(example)
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class_sistema_universal = clasifica_sistema_universal(example)
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class_conv_americana = clasifica_conv_americana(example)
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return entidades,class_sistema_universal, class_conv_americana
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input_sen = gr.inputs.Textbox(lines=10, label="Proporcione el texto a analizar:")
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output_lbl1= gr.outputs.Label(label="Clasificación modelo convención americana:")
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output_lbl2= gr.outputs.Label(label="Clasificación modelo sistema universal:")
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#iface = gr.Interface(fn=process, inputs=input_sen, outputs=["highlight","label","label"], examples=examples, title=title, description = description)
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iface = gr.Interface(fn=process, inputs=input_sen, outputs=["highlight",output_lbl2,output_lbl2], examples=examples, title=title, description = description)
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iface.launch(debug=True)
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from transformers import AutoModelForTokenClassification,AutoModelForSequenceClassification, AutoTokenizer, pipeline
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from sentence_transformers import SentenceTransformer, util
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title = "Modelo Jurídico Mexicano"
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description = """
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return {i["label"]: float(i["score"]) for i in results}
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def similitud(example,example2):
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#Compute embedding for both lists
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embeddings1 = model.encode(sentences1, convert_to_tensor=True)
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embeddings2 = model.encode(sentences2, convert_to_tensor=True)
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#Compute cosine-similarits
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cosine_scores = util.cos_sim(embeddings1, embeddings2)
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return float(cosine_scores[0])*100
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def process(example):
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entidades = get_entities(example)
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class_sistema_universal = clasifica_sistema_universal(example)
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class_conv_americana = clasifica_conv_americana(example)
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score_similitud = similitud(example,example2)
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return entidades,class_sistema_universal, class_conv_americana, score_similitud
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input_sen = gr.inputs.Textbox(lines=10, label="Proporcione el texto a analizar:")
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input_sen2 = gr.inputs.Textbox(lines=10, label="Proporcione el texto a comparar:")
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output_lbl1= gr.outputs.Label(label="Clasificación modelo convención americana:")
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output_lbl2= gr.outputs.Label(label="Clasificación modelo sistema universal:")
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output_txt= gr.outputs.Textbox(label="Porcentaje de similitud:")
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#iface = gr.Interface(fn=process, inputs=input_sen, outputs=["highlight","label","label"], examples=examples, title=title, description = description)
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iface = gr.Interface(fn=process, inputs=[input_sen, input_sen2], outputs=["highlight",output_lbl2,output_lbl2,output_txt], examples=examples, title=title, description = description)
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iface.launch(debug=True)
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