Spaces:
Running
on
Zero
Running
on
Zero
Commit
β’
7d66c17
1
Parent(s):
b3d9c4b
update text
Browse files
app.py
CHANGED
@@ -82,7 +82,7 @@ def plot_and_df(texts, preds):
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def get_first_parquet_filename(dataset, config, split):
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parquet_resp = session.get(f"https://datasets-server.huggingface.co/parquet?dataset={dataset}&config={config}", timeout=
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if "error" in parquet_resp:
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raise ValueError(parquet_resp["error"])
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first_parquet_file_url = [file for file in parquet_resp["parquet_files"] if file["split"] == split][0]["url"]
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@@ -217,11 +217,13 @@ def call_perspective_api(texts_df, column_name, nested_column_name, dataset, con
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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#
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## Select dataset and text column
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"""
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)
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@@ -235,7 +237,6 @@ with gr.Blocks() as demo:
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subset_dropdown = gr.Dropdown(label="Subset", visible=False)
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split_dropdown = gr.Dropdown(label="Split", visible=False)
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# config_name = "default" # TODO: user input
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with gr.Accordion("Dataset preview", open=False):
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@gr.render(inputs=[dataset_name, subset_dropdown, split_dropdown])
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def embed(name, subset, split):
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@@ -261,7 +262,7 @@ with gr.Blocks() as demo:
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text_column_dropdown: gr.Dropdown(label="Text column name"),
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nested_text_column_dropdown: gr.Dropdown(visible=False)
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}
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info_resp = session.get(f"https://datasets-server.huggingface.co/info?dataset={dataset}", timeout=
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if "error" in info_resp:
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return {
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subset_dropdown: gr.Dropdown(visible=False),
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@@ -285,7 +286,7 @@ with gr.Blocks() as demo:
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return {
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subset_dropdown: gr.Dropdown(value=subset, choices=subsets, visible=len(subsets) > 1),
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split_dropdown: gr.Dropdown(value=split, choices=splits, visible=len(splits) > 1),
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text_column_dropdown: gr.Dropdown(choices=text_features + nested_text_features, label="Text column name"
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nested_text_column_dropdown: gr.Dropdown(visible=False),
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}
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logging.info(nested_text_features)
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@@ -364,8 +365,7 @@ with gr.Blocks() as demo:
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gr.Markdown("""## Explore toxicity
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Run [Perspective](https://perspectiveapi.com/how-it-works/) on 100 random samples to check toxicity
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""")
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gr_toxicity_btn = gr.Button("Run Perpspective API")
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toxicity_progress_bar = gr.Label(show_label=False)
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toxicity_hist = gr.Plot()
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with gr.Accordion("Explore examples with toxicity scores:", open=False):
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def get_first_parquet_filename(dataset, config, split):
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parquet_resp = session.get(f"https://datasets-server.huggingface.co/parquet?dataset={dataset}&config={config}", timeout=20).json()
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if "error" in parquet_resp:
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raise ValueError(parquet_resp["error"])
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first_parquet_file_url = [file for file in parquet_resp["parquet_files"] if file["split"] == split][0]["url"]
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# π Data Quality Checker π
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This space gives some instruments to have a quick glance at the quality of a text dataset.
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* It uses [NVIDIA's quality classifier model](https://huggingface.co/nvidia/quality-classifier-deberta)
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on a small subset of texts.
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* It uses [Perspective](https://perspectiveapi.com/how-it-works/) API to check toxicity of 100 random dataset texts
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## Select dataset and text column
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"""
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)
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subset_dropdown = gr.Dropdown(label="Subset", visible=False)
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split_dropdown = gr.Dropdown(label="Split", visible=False)
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with gr.Accordion("Dataset preview", open=False):
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@gr.render(inputs=[dataset_name, subset_dropdown, split_dropdown])
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def embed(name, subset, split):
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text_column_dropdown: gr.Dropdown(label="Text column name"),
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nested_text_column_dropdown: gr.Dropdown(visible=False)
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}
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info_resp = session.get(f"https://datasets-server.huggingface.co/info?dataset={dataset}", timeout=20).json()
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if "error" in info_resp:
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return {
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subset_dropdown: gr.Dropdown(visible=False),
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return {
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subset_dropdown: gr.Dropdown(value=subset, choices=subsets, visible=len(subsets) > 1),
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split_dropdown: gr.Dropdown(value=split, choices=splits, visible=len(splits) > 1),
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text_column_dropdown: gr.Dropdown(choices=text_features + nested_text_features, label="Text column name"),
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nested_text_column_dropdown: gr.Dropdown(visible=False),
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}
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logging.info(nested_text_features)
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gr.Markdown("""## Explore toxicity
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Run [Perspective](https://perspectiveapi.com/how-it-works/) on 100 random samples to check toxicity
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""")
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gr_toxicity_btn = gr.Button("Run Perpspective")
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toxicity_progress_bar = gr.Label(show_label=False)
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toxicity_hist = gr.Plot()
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with gr.Accordion("Explore examples with toxicity scores:", open=False):
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