Spaces:
Running
Running
update
Browse files- app.py +160 -62
- checkpoints-v2.0.json +12 -0
- checkpoints.json +1 -0
- checkpoints/Multilingual/ACOS/multilingual-acos.zip +3 -0
app.py
CHANGED
@@ -8,23 +8,20 @@
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# Copyright (C) 2023. All Rights Reserved.
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import random
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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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-
AspectTermExtraction as ATEPC,
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TaskCodeOption,
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available_checkpoints,
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)
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from pyabsa import
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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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atepc_dataset_items = {dataset.name: dataset for dataset in ATEPC.ATEPCDatasetList()}
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aste_dataset_items = {dataset.name: dataset for dataset in ASTE.ASTEDatasetList()}
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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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return sorted(set(lines), key=lines.index)
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aspect_extractor = ATEPC.AspectExtractor(checkpoint="multilingual")
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def perform_atepc_inference(text, dataset):
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return pred_triplets, true_triplets, "{}".format(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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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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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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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.
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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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fn=
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inputs=[
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outputs=[
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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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# Copyright (C) 2023. All Rights Reserved.
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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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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()[:-1]}
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acos_dataset_dict = {
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dataset.name: get_acos_example(dataset.name) for dataset in ABSAInstruction.ACOSDatasetList()[:-1]
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}
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quadruple_extractor = ABSAInstruction.ABSAGenerator(checkpoint="multilingual", device=autocuda.auto_cuda())
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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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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)
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outputs = raw_output[0].strip().split(', ')
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data = {}
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for output in outputs:
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for sub_output in output.split('|'):
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if 'aspect' in sub_output:
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data['aspect'] = sub_output.split(':')[1]
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elif 'opinion' in sub_output:
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data['opinion'] = sub_output.split(':')[1]
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elif 'sentiment' in sub_output:
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data['sentiment'] = sub_output.split(':')[1]
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elif 'polarity' in sub_output:
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data['polarity'] = sub_output.split(':')[1]
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elif 'category' in sub_output:
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try:
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data['category'] = sub_output.split(':')[1]
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except:
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data['category'] = ''
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result = pd.DataFrame.from_dict(data, orient='index').T
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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 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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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'>Aspect Category Opinion Sentiment Extraction !</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="Restaurant16",
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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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gr.Markdown(
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"""### GitHub Repo: [PyABSA V2](https://github.com/yangheng95/PyABSA)
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checkpoints-v2.0.json
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"Author": "H, Yang (hy345@exeter.ac.uk)"
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}
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},
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"UPPERTASKCODE": {
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"promise": {
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"id": "",
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"Author": "H, Yang (hy345@exeter.ac.uk)"
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}
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},
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"ACOS": {
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"multilingual": {
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"id": "",
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"Training Model": "DeBERTa-v3-Base",
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"Training Dataset": "SemEval + Synthetic + Chinese_Zhang datasets",
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"Language": "Multilingual",
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"Description": "Trained on RTX3090",
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"Available Version": "2.1.8+",
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"Checkpoint File": "multilingual-acos.zip",
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"Author": "H, Yang (hy345@exeter.ac.uk)"
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}
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},
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"UPPERTASKCODE": {
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"promise": {
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"id": "",
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checkpoints.json
ADDED
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+
{"2.0.0": {"APC": {"multilingual": {"id": "", "Training Model": "FAST-LSA-T-V2-Deberta", "Training Dataset": "APCDatasetList.Multilingual", "Language": "Multilingual", "Description": "Trained on RTX3090", "Available Version": "1.10.5+", "Checkpoint File": "fast_lcf_bert_Multilingual_acc_87.18_f1_83.11.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "multilingual2": {"id": "", "Training Model": "FAST-LSA-T-V2-Deberta", "Training Dataset": "APCDatasetList.Multilingual", "Language": "Multilingual", "Description": "Trained on RTX3090", "Available Version": "1.10.5+", "Checkpoint File": "fast_lcf_bert_Multilingual_acc_82.66_f1_82.06.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "english": {"id": "", "Training Model": "FAST-LSA-T-V2-Deberta", "Training Dataset": "APCDatasetList.English", "Language": "English", "Description": "Trained on RTX3090", "Available Version": "1.10.5+", "Checkpoint File": "fast_lsa_t_v2_English_acc_82.21_f1_81.81.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, 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