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import random |
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import autocuda |
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import gradio as gr |
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import pandas as pd |
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from pyabsa import ( |
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download_all_available_datasets, |
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TaskCodeOption, |
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available_checkpoints, |
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) |
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from pyabsa import ABSAInstruction |
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from pyabsa.utils.data_utils.dataset_manager import detect_infer_dataset |
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download_all_available_datasets() |
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def get_atepc_example(dataset): |
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task = TaskCodeOption.Aspect_Polarity_Classification |
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dataset_file = detect_infer_dataset(atepc_dataset_items[dataset], task) |
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for fname in dataset_file: |
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lines = [] |
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if isinstance(fname, str): |
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fname = [fname] |
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for f in fname: |
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print("loading: {}".format(f)) |
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fin = open(f, "r", encoding="utf-8") |
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lines.extend(fin.readlines()) |
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fin.close() |
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for i in range(len(lines)): |
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lines[i] = ( |
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lines[i][: lines[i].find("$LABEL$")] |
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.replace("[B-ASP]", "") |
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.replace("[E-ASP]", "") |
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.strip() |
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) |
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return sorted(set(lines), key=lines.index) |
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def get_aste_example(dataset): |
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task = TaskCodeOption.Aspect_Sentiment_Triplet_Extraction |
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dataset_file = detect_infer_dataset(aste_dataset_items[dataset], task) |
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for fname in dataset_file: |
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lines = [] |
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if isinstance(fname, str): |
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fname = [fname] |
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for f in fname: |
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print("loading: {}".format(f)) |
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fin = open(f, "r", encoding="utf-8") |
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lines.extend(fin.readlines()) |
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fin.close() |
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return sorted(set(lines), key=lines.index) |
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def get_acos_example(dataset): |
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task = 'ACOS' |
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dataset_file = detect_infer_dataset(acos_dataset_items[dataset], task) |
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for fname in dataset_file: |
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lines = [] |
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if isinstance(fname, str): |
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fname = [fname] |
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for f in fname: |
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print("loading: {}".format(f)) |
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fin = open(f, "r", encoding="utf-8") |
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lines.extend(fin.readlines()) |
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fin.close() |
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lines = [line.split('####')[0] for line in lines] |
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return sorted(set(lines), key=lines.index) |
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try: |
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from pyabsa import AspectTermExtraction as ATEPC |
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atepc_dataset_items = {dataset.name: dataset for dataset in ATEPC.ATEPCDatasetList()} |
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atepc_dataset_dict = { |
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dataset.name: get_atepc_example(dataset.name) |
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for dataset in ATEPC.ATEPCDatasetList() |
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} |
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aspect_extractor = ATEPC.AspectExtractor(checkpoint="multilingual") |
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except Exception as e: |
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print(e) |
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atepc_dataset_items = {} |
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atepc_dataset_dict = {} |
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aspect_extractor = None |
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try: |
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from pyabsa import AspectSentimentTripletExtraction as ASTE |
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aste_dataset_items = {dataset.name: dataset for dataset in ASTE.ASTEDatasetList()} |
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aste_dataset_dict = { |
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dataset.name: get_aste_example(dataset.name) for dataset in ASTE.ASTEDatasetList() |
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} |
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triplet_extractor = ASTE.AspectSentimentTripletExtractor(checkpoint="multilingual") |
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except Exception as e: |
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print(e) |
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aste_dataset_items = {} |
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aste_dataset_dict = {} |
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triplet_extractor = None |
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try: |
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from pyabsa import ABSAInstruction |
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acos_dataset_items = {dataset.name: dataset for dataset in ABSAInstruction.ACOSDatasetList()} |
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acos_dataset_dict = { |
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dataset.name: get_acos_example(dataset.name) for dataset in ABSAInstruction.ACOSDatasetList() |
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} |
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quadruple_extractor = ABSAInstruction.ABSAGenerator("multilingual") |
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except Exception as e: |
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print(e) |
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acos_dataset_items = {} |
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acos_dataset_dict = {} |
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quadruple_extractor = None |
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def perform_atepc_inference(text, dataset): |
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if not text: |
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text = atepc_dataset_dict[dataset][ |
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random.randint(0, len(atepc_dataset_dict[dataset]) - 1) |
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] |
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result = aspect_extractor.predict(text, pred_sentiment=True) |
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result = pd.DataFrame( |
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{ |
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"aspect": result["aspect"], |
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"sentiment": result["sentiment"], |
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"confidence": [round(x, 4) for x in result["confidence"]], |
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"position": result["position"], |
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} |
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) |
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return result, "{}".format(text) |
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def perform_aste_inference(text, dataset): |
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if not text: |
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text = aste_dataset_dict[dataset][ |
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random.randint(0, len(aste_dataset_dict[dataset]) - 1) |
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] |
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result = triplet_extractor.predict(text) |
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pred_triplets = pd.DataFrame(result["Triplets"]) |
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true_triplets = pd.DataFrame(result["True Triplets"]) |
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return pred_triplets, true_triplets, "{}".format(text) |
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def perform_acos_inference(text, dataset): |
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if not text: |
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text = acos_dataset_dict[dataset][ |
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random.randint(0, len(acos_dataset_dict[dataset]) - 1) |
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] |
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raw_output = quadruple_extractor.predict(text.split('####')[0], max_length=128) |
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result = raw_output['Quadruples'] |
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result = pd.DataFrame(result) |
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return result, text |
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demo = gr.Blocks() |
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with demo: |
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with gr.Row(): |
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if quadruple_extractor: |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("# <p align='center'> ABSA Quadruple Extraction (Experimental) </p>") |
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acos_input_sentence = gr.Textbox( |
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placeholder="Leave this box blank and choose a dataset will give you a random example...", |
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label="Example:", |
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) |
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acos_dataset_ids = gr.Radio( |
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choices=[dataset.name for dataset in ABSAInstruction.ACOSDatasetList()], |
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value="Laptop14", |
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label="Datasets", |
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) |
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acos_inference_button = gr.Button("Let's go!") |
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acos_output_text = gr.TextArea(label="Example:") |
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acos_output_pred_df = gr.DataFrame(label="Predicted Triplets:") |
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acos_inference_button.click( |
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fn=perform_acos_inference, |
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inputs=[acos_input_sentence, acos_dataset_ids], |
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outputs=[acos_output_pred_df, acos_output_text], |
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) |
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with gr.Row(): |
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if triplet_extractor: |
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with gr.Column(): |
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gr.Markdown("# <p align='center'>Aspect Sentiment Triplet Extraction !</p>") |
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with gr.Row(): |
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with gr.Column(): |
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aste_input_sentence = gr.Textbox( |
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placeholder="Leave this box blank and choose a dataset will give you a random example...", |
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label="Example:", |
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) |
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gr.Markdown( |
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"You can find code and dataset at [ASTE examples](https://github.com/yangheng95/PyABSA/tree/v2/examples-v2/aspect_sentiment_triplet_extration)" |
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) |
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aste_dataset_ids = gr.Radio( |
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choices=[dataset.name for dataset in ASTE.ASTEDatasetList()[:-1]], |
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value="Restaurant14", |
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label="Datasets", |
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) |
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aste_inference_button = gr.Button("Let's go!") |
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aste_output_text = gr.TextArea(label="Example:") |
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aste_output_pred_df = gr.DataFrame(label="Predicted Triplets:") |
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aste_output_true_df = gr.DataFrame(label="Original Triplets:") |
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aste_inference_button.click( |
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fn=perform_aste_inference, |
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inputs=[aste_input_sentence, aste_dataset_ids], |
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outputs=[aste_output_pred_df, aste_output_true_df, aste_output_text], |
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) |
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if aspect_extractor: |
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with gr.Column(): |
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gr.Markdown( |
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"# <p align='center'>Multilingual Aspect-based Sentiment Analysis !</p>" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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atepc_input_sentence = gr.Textbox( |
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placeholder="Leave this box blank and choose a dataset will give you a random example...", |
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label="Example:", |
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) |
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gr.Markdown( |
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"You can find the datasets at [github.com/yangheng95/ABSADatasets](https://github.com/yangheng95/ABSADatasets/tree/v1.2/datasets/text_classification)" |
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) |
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atepc_dataset_ids = gr.Radio( |
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choices=[dataset.name for dataset in ATEPC.ATEPCDatasetList()[:-1]], |
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value="Laptop14", |
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label="Datasets", |
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) |
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atepc_inference_button = gr.Button("Let's go!") |
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atepc_output_text = gr.TextArea(label="Example:") |
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atepc_output_df = gr.DataFrame(label="Prediction Results:") |
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atepc_inference_button.click( |
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fn=perform_atepc_inference, |
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inputs=[atepc_input_sentence, atepc_dataset_ids], |
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outputs=[atepc_output_df, atepc_output_text], |
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) |
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gr.Markdown( |
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"""### GitHub Repo: [PyABSA V2](https://github.com/yangheng95/PyABSA) |
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### Author: [Heng Yang](https://github.com/yangheng95) (ζ¨ζ) |
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[](https://pepy.tech/project/pyabsa) |
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[](https://pepy.tech/project/pyabsa) |
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""" |
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) |
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demo.launch() |
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