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import json |
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import logging |
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import os.path |
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import tempfile |
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from functools import partial |
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from typing import List, Optional, Tuple |
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import gradio as gr |
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import pandas as pd |
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from document_store import DocumentStore, get_annotation_from_document |
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from model_utils import create_and_annotate_document, load_models |
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from pie_modules.taskmodules import PointerNetworkTaskModuleForEnd2EndRE |
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from pytorch_ie import Pipeline |
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from pytorch_ie.documents import TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions |
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from rendering_utils import render_displacy, render_pretty_table |
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from transformers import PreTrainedModel, PreTrainedTokenizer |
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logger = logging.getLogger(__name__) |
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RENDER_WITH_DISPLACY = "displaCy + highlighted arguments" |
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RENDER_WITH_PRETTY_TABLE = "Pretty Table" |
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DEFAULT_MODEL_NAME = "ArneBinder/sam-pointer-bart-base-v0.3" |
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DEFAULT_MODEL_REVISION = "76300f8e534e2fcf695f00cb49bba166739b8d8a" |
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DEFAULT_EMBEDDING_MODEL_NAME = "allenai/scibert_scivocab_uncased" |
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def render_annotated_document( |
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document: TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions, |
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render_with: str, |
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render_kwargs_json: str, |
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) -> str: |
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render_kwargs = json.loads(render_kwargs_json) |
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if render_with == RENDER_WITH_PRETTY_TABLE: |
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html = render_pretty_table(document, **render_kwargs) |
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elif render_with == RENDER_WITH_DISPLACY: |
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html = render_displacy(document, **render_kwargs) |
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else: |
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raise ValueError(f"Unknown render_with value: {render_with}") |
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return html |
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def wrapped_process_text( |
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text: str, |
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doc_id: str, |
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models: Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]], |
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document_store: DocumentStore, |
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) -> Tuple[dict, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions]: |
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document = create_and_annotate_document( |
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text=text, |
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doc_id=doc_id, |
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models=models, |
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) |
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document_store.add_document(document) |
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return document.asdict(), document |
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def process_uploaded_files( |
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file_names: List[str], |
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models: Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]], |
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document_store: DocumentStore, |
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) -> pd.DataFrame: |
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try: |
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new_documents = [] |
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for file_name in file_names: |
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if file_name.lower().endswith(".txt"): |
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with open(file_name, "r", encoding="utf-8") as f: |
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text = f.read() |
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base_file_name = os.path.basename(file_name) |
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gr.Info(f"Processing file '{base_file_name}' ...") |
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new_documents.append(create_and_annotate_document(text, base_file_name, models)) |
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else: |
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raise gr.Error(f"Unsupported file format: {file_name}") |
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document_store.add_documents(new_documents) |
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except Exception as e: |
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raise gr.Error(f"Failed to process uploaded files: {e}") |
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return document_store.overview() |
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def open_accordion(): |
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return gr.Accordion(open=True) |
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def close_accordion(): |
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return gr.Accordion(open=False) |
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def select_processed_document( |
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evt: gr.SelectData, |
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processed_documents_df: pd.DataFrame, |
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document_store: DocumentStore, |
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) -> TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions: |
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row_idx, col_idx = evt.index |
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doc_id = processed_documents_df.iloc[row_idx]["doc_id"] |
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doc = document_store.get_document(doc_id) |
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return doc |
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def set_relation_types( |
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models: Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]], |
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default: Optional[List[str]] = None, |
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) -> gr.Dropdown: |
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arg_pipeline = models[0] |
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if isinstance(arg_pipeline.taskmodule, PointerNetworkTaskModuleForEnd2EndRE): |
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relation_types = arg_pipeline.taskmodule.labels_per_layer["binary_relations"] |
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else: |
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raise gr.Error("Unsupported taskmodule for relation types") |
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return gr.Dropdown( |
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choices=relation_types, |
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label="Relation Types", |
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value=default, |
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multiselect=True, |
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) |
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def download_processed_documents( |
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document_store: DocumentStore, |
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file_name: str = "processed_documents.json", |
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) -> str: |
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file_path = os.path.join(tempfile.gettempdir(), file_name) |
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document_store.save_to_json(file_path, indent=2) |
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return file_path |
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def upload_processed_documents( |
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file_name: str, |
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document_store: DocumentStore, |
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) -> pd.DataFrame: |
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document_store.add_from_json(file_name) |
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return document_store.overview() |
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def main(): |
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example_text = "Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent." |
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print("Loading models ...") |
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argumentation_model, embedding_model, embedding_tokenizer = load_models( |
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model_name=DEFAULT_MODEL_NAME, |
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revision=DEFAULT_MODEL_REVISION, |
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embedding_model_name=DEFAULT_EMBEDDING_MODEL_NAME, |
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) |
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default_render_kwargs = { |
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"entity_options": { |
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"colors": { |
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"own_claim".upper(): "#009933", |
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"background_claim".upper(): "#99ccff", |
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"data".upper(): "#993399", |
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} |
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}, |
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"colors_hover": { |
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"selected": "#ffa", |
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"tail": { |
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"supports": "#9f9", |
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"contradicts": "#f99", |
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"parts_of_same": None, |
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}, |
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"head": None, |
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"other": None, |
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}, |
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} |
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with gr.Blocks() as demo: |
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document_store_state = gr.State(DocumentStore()) |
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models_state = gr.State((argumentation_model, embedding_model, embedding_tokenizer)) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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doc_id = gr.Textbox( |
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label="Document ID", |
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value="user_input", |
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) |
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doc_text = gr.Textbox( |
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label="Text", |
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lines=20, |
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value=example_text, |
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) |
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with gr.Accordion("Model Configuration", open=False): |
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model_name = gr.Textbox( |
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label="Model Name", |
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value=DEFAULT_MODEL_NAME, |
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) |
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model_revision = gr.Textbox( |
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label="Model Revision", |
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value=DEFAULT_MODEL_REVISION, |
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) |
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embedding_model_name = gr.Textbox( |
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label=f"Embedding Model Name (e.g. {DEFAULT_EMBEDDING_MODEL_NAME})", |
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value=DEFAULT_EMBEDDING_MODEL_NAME, |
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) |
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load_models_btn = gr.Button("Load Models") |
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load_models_btn.click( |
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fn=load_models, |
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inputs=[model_name, model_revision, embedding_model_name], |
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outputs=models_state, |
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) |
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predict_btn = gr.Button("Analyse") |
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document_state = gr.State() |
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with gr.Column(scale=1): |
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with gr.Accordion("See plain result ...", open=False) as output_accordion: |
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document_json = gr.JSON(label="Model Output") |
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with gr.Accordion("Render Options", open=False): |
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render_as = gr.Dropdown( |
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label="Render with", |
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choices=[RENDER_WITH_PRETTY_TABLE, RENDER_WITH_DISPLACY], |
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value=RENDER_WITH_DISPLACY, |
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) |
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render_kwargs = gr.Textbox( |
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label="Render Arguments", |
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lines=5, |
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value=json.dumps(default_render_kwargs, indent=2), |
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) |
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render_btn = gr.Button("Re-render") |
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rendered_output = gr.HTML(label="Rendered Output") |
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upload_btn = gr.UploadButton( |
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"Upload & Analyse Documents", file_types=["text"], file_count="multiple" |
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) |
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with gr.Column(scale=1): |
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with gr.Accordion( |
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"Indexed Documents", open=False |
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) as processed_documents_accordion: |
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processed_documents_df = gr.DataFrame( |
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headers=["id", "num_adus", "num_relations"], |
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interactive=False, |
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) |
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with gr.Row(): |
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download_processed_documents_btn = gr.DownloadButton("Download") |
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upload_processed_documents_btn = gr.UploadButton( |
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"Upload", file_types=["json"] |
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) |
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with gr.Accordion("Selected ADU", open=False): |
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selected_adu_id = gr.Textbox(label="ID", elem_id="selected_adu_id") |
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selected_adu_text = gr.Textbox(label="Text") |
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with gr.Accordion("Retrieval Configuration", open=False): |
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min_similarity = gr.Slider( |
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label="Minimum Similarity", |
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minimum=0.0, |
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maximum=1.0, |
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step=0.01, |
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value=0.8, |
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) |
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top_k = gr.Slider( |
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label="Top K", |
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minimum=2, |
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maximum=50, |
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step=1, |
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value=20, |
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) |
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retrieve_similar_adus_btn = gr.Button("Retrieve similar ADUs") |
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similar_adus = gr.DataFrame(headers=["doc_id", "adu_id", "score", "text"]) |
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relation_types = set_relation_types( |
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models_state.value, default=["supports", "contradicts"] |
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) |
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relevant_adus = gr.DataFrame( |
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label="Relevant ADUs from other documents", |
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headers=[ |
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"relation", |
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"adu", |
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"reference_adu", |
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"doc_id", |
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"sim_score", |
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"rel_score", |
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], |
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interactive=False, |
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) |
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render_event_kwargs = dict( |
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fn=render_annotated_document, |
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inputs=[document_state, render_as, render_kwargs], |
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outputs=rendered_output, |
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) |
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predict_btn.click(fn=open_accordion, inputs=[], outputs=[output_accordion]).then( |
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fn=wrapped_process_text, |
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inputs=[doc_text, doc_id, models_state, document_store_state], |
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outputs=[document_json, document_state], |
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api_name="predict", |
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).success( |
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fn=lambda document_store: document_store.overview(), |
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inputs=[document_store_state], |
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outputs=[processed_documents_df], |
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) |
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render_btn.click(**render_event_kwargs, api_name="render") |
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document_state.change( |
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fn=lambda doc: doc.asdict(), |
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inputs=[document_state], |
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outputs=[document_json], |
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).success(close_accordion, inputs=[], outputs=[output_accordion]).then( |
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**render_event_kwargs |
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) |
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upload_btn.upload( |
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fn=open_accordion, inputs=[], outputs=[processed_documents_accordion] |
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).then( |
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fn=process_uploaded_files, |
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inputs=[upload_btn, models_state, document_store_state], |
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outputs=[processed_documents_df], |
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) |
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processed_documents_df.select( |
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select_processed_document, |
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inputs=[processed_documents_df, document_store_state], |
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outputs=[document_state], |
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) |
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download_processed_documents_btn.click( |
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fn=download_processed_documents, |
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inputs=[document_store_state], |
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outputs=[download_processed_documents_btn], |
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) |
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upload_processed_documents_btn.upload( |
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fn=upload_processed_documents, |
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inputs=[upload_processed_documents_btn, document_store_state], |
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outputs=[processed_documents_df], |
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) |
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retrieve_relevant_adus_event_kwargs = dict( |
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fn=partial(DocumentStore.get_relevant_adus_df, columns=relevant_adus.headers), |
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inputs=[ |
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document_store_state, |
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selected_adu_id, |
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document_state, |
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min_similarity, |
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top_k, |
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relation_types, |
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], |
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outputs=[relevant_adus], |
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) |
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selected_adu_id.change( |
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fn=partial(get_annotation_from_document, annotation_layer="labeled_spans"), |
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inputs=[document_state, selected_adu_id], |
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outputs=[selected_adu_text], |
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).success(**retrieve_relevant_adus_event_kwargs) |
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retrieve_similar_adus_btn.click( |
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fn=DocumentStore.get_similar_adus_df, |
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inputs=[ |
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document_store_state, |
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selected_adu_id, |
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document_state, |
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min_similarity, |
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top_k, |
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], |
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outputs=[similar_adus], |
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) |
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models_state.change( |
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fn=set_relation_types, |
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inputs=[models_state], |
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outputs=[relation_types], |
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) |
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js = """ |
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() => { |
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function maybeSetColor(entity, colorAttributeKey, colorDictKey) { |
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var color = entity.getAttribute('data-color-' + colorAttributeKey); |
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// if color is a json string, parse it and use the value at colorDictKey |
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try { |
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const colors = JSON.parse(color); |
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color = colors[colorDictKey]; |
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} catch (e) {} |
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if (color) { |
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entity.style.backgroundColor = color; |
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entity.style.color = '#000'; |
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} |
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} |
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function highlightRelationArguments(entityId) { |
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const entities = document.querySelectorAll('.entity'); |
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// reset all entities |
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entities.forEach(entity => { |
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const color = entity.getAttribute('data-color-original'); |
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entity.style.backgroundColor = color; |
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entity.style.color = ''; |
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}); |
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if (entityId !== null) { |
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var visitedEntities = new Set(); |
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// highlight selected entity |
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const selectedEntity = document.getElementById(entityId); |
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if (selectedEntity) { |
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const label = selectedEntity.getAttribute('data-label'); |
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maybeSetColor(selectedEntity, 'selected', label); |
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visitedEntities.add(selectedEntity); |
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} |
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// highlight tails |
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const relationTailsAndLabels = JSON.parse(selectedEntity.getAttribute('data-relation-tails')); |
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relationTailsAndLabels.forEach(relationTail => { |
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const tailEntity = document.getElementById(relationTail['entity-id']); |
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if (tailEntity) { |
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const label = relationTail['label']; |
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maybeSetColor(tailEntity, 'tail', label); |
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visitedEntities.add(tailEntity); |
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} |
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}); |
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// highlight heads |
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const relationHeadsAndLabels = JSON.parse(selectedEntity.getAttribute('data-relation-heads')); |
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relationHeadsAndLabels.forEach(relationHead => { |
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const headEntity = document.getElementById(relationHead['entity-id']); |
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if (headEntity) { |
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const label = relationHead['label']; |
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maybeSetColor(headEntity, 'head', label); |
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visitedEntities.add(headEntity); |
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} |
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}); |
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// highlight other entities |
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entities.forEach(entity => { |
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if (!visitedEntities.has(entity)) { |
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const label = entity.getAttribute('data-label'); |
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maybeSetColor(entity, 'other', label); |
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} |
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}); |
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} |
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} |
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function setReferenceAduId(entityId) { |
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// get the textarea element that holds the reference adu id |
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let referenceAduIdDiv = document.querySelector('#selected_adu_id textarea'); |
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// set the value of the input field |
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referenceAduIdDiv.value = entityId; |
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// trigger an input event to update the state |
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var event = new Event('input'); |
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referenceAduIdDiv.dispatchEvent(event); |
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} |
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const entities = document.querySelectorAll('.entity'); |
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entities.forEach(entity => { |
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const alreadyHasListener = entity.getAttribute('data-has-listener'); |
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if (alreadyHasListener) { |
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return; |
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} |
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entity.addEventListener('mouseover', () => { |
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highlightRelationArguments(entity.id); |
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setReferenceAduId(entity.id); |
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}); |
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entity.addEventListener('mouseout', () => { |
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highlightRelationArguments(null); |
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}); |
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entity.setAttribute('data-has-listener', 'true'); |
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}); |
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} |
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""" |
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rendered_output.change(fn=None, js=js, inputs=[], outputs=[]) |
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demo.launch() |
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if __name__ == "__main__": |
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logging.basicConfig() |
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main() |
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