import requests from collections import Counter from requests.adapters import HTTPAdapter, Retry import multiprocessing import os import time import logging import gradio as gr import pandas as pd import polars as pl import matplotlib.pyplot as plt import spaces from gradio_huggingfacehub_search import HuggingfaceHubSearch from huggingface_hub import PyTorchModelHubMixin import torch from torch import nn from transformers import AutoModel, AutoTokenizer, AutoConfig from tqdm import tqdm logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") session = requests.Session() retries = Retry(total=5, backoff_factor=1, status_forcelist=[502, 503, 504]) session.mount('http://', HTTPAdapter(max_retries=retries)) class QualityModel(nn.Module, PyTorchModelHubMixin): def __init__(self, config): super(QualityModel, self).__init__() self.model = AutoModel.from_pretrained(config["base_model"]) self.dropout = nn.Dropout(config["fc_dropout"]) self.fc = nn.Linear(self.model.config.hidden_size, len(config["id2label"])) def forward(self, input_ids, attention_mask): features = self.model( input_ids=input_ids, attention_mask=attention_mask ).last_hidden_state dropped = self.dropout(features) outputs = self.fc(dropped) return torch.softmax(outputs[:, 0, :], dim=1) device = "cuda" if torch.cuda.is_available() else "cpu" config = AutoConfig.from_pretrained("nvidia/quality-classifier-deberta") tokenizer = AutoTokenizer.from_pretrained("nvidia/quality-classifier-deberta") model = QualityModel.from_pretrained("nvidia/quality-classifier-deberta").to(device) # model = torch.compile(model) model.eval() @spaces.GPU def predict(texts: list[str]): inputs = tokenizer( texts, return_tensors="pt", padding="longest", truncation=True ).to(device) outputs = model(inputs["input_ids"], inputs["attention_mask"]) predicted_classes = torch.argmax(outputs, dim=1) predicted_domains = [ config.id2label[class_idx.item()] for class_idx in predicted_classes.cpu().numpy() ] return predicted_domains def plot_and_df(texts, preds): texts_df = pd.DataFrame({"quality": preds, "text": texts}) counts = Counter(preds) counts_df = pd.DataFrame( { "quality": ["Low", "Medium", "High"], "count": [counts.get("Low", 0), counts.get("Medium", 0), counts.get("High", 0)] } ) # counts.reset_index(inplace=True) return ( gr.BarPlot(counts_df, x="quality", y="count", sort=None), texts_df[texts_df["quality"] == "Low"][["text"]][:20], texts_df[texts_df["quality"] == "Medium"][["text"]][:20], texts_df[texts_df["quality"] == "High"][["text"]][:20], ) @spaces.GPU def run_quality_check(dataset, config, split, column, batch_size, num_examples): logging.info(f"Fetching data for {dataset=} {config=} {split=} {column=}") try: data = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/{split}/0000.parquet", columns=[column]) except pl.exceptions.ComputeError: try: data = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/partial-{split}/0000.parquet", columns=[column]) except pl.exceptions.ComputeError: try: data = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/{split}-part0/0000.parquet", columns=[column]) except Exception as error: yield f"❌ {error}", gr.BarPlot(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), return logging.info("Data fetched.") data_sample = data.sample(num_examples, seed=16) if data.shape[0] > num_examples else data texts = [text[:10000] for text in data_sample[column].to_list()] predictions, texts_processed = [], [] num_examples = min(len(texts), num_examples) for i in range(0, num_examples, batch_size): batch_texts = texts[i:i+batch_size] batch_predictions = predict(batch_texts) predictions.extend(batch_predictions) texts_processed.extend(batch_texts) yield {"check in progress...": i / num_examples}, *plot_and_df(texts_processed, predictions), pd.DataFrame() # with multiprocessing.Pool(processes=8) as pool: # props = pool.map(proportion_non_ascii, texts) # # # non_ascii_df = pd.DataFrame.from_dict({"prop_non_ascii": props, "text": texts}) # plt.hist(props, bins=20, range=(0., 1.)) # plt.title('Histogram of proportion of non-ASCII characters') # plt.xlabel('Proportion of non-ASCII characters') # plt.ylabel('Number of texts') yield {"finished": 1.}, *plot_and_df(texts_processed, predictions), data_sample PERSPECTIVE_API_KEY = os.environ.get("PERSPECTIVE_API_KEY") PERSPECTIVE_URL = f"https://commentanalyzer.googleapis.com/v1alpha1/comments:analyze?key={PERSPECTIVE_API_KEY}" REQUESTED_ATTRIBUTES = {"TOXICITY": {}, "SEVERE_TOXICITY": {}, "IDENTITY_ATTACK": {}, "INSULT": {}, "PROFANITY": {}, "THREAT": {}} ATT_SCORE = "attributeScores" SUM_SCORE = "summaryScore" def plot_toxicity(scores): fig, axs = plt.subplots(2, 3)#, figsize=(10, 6)) for x, y, score_name in zip([0,0,0,1,1,1], [0,1,2,0,1,2], scores): axs[x,y].hist(scores[score_name], bins=20, range=(0., 1.)) axs[x,y].set_xlabel(score_name) fig.supylabel("Number of texts") fig.suptitle("Histogram of toxicity scores") fig.tight_layout() return fig def call_perspective_api(texts_df, column_name, dataset, config, split):#, full_check=False): headers = { "content-type": "application/json", } req_att_scores = {attr: [] for attr in REQUESTED_ATTRIBUTES} # fetch data if it doesn't exist yet if texts_df.values.tolist() == [['', '', '']]: logging.info(f"Fetching data for {dataset=} {config=} {split=} {column_name=}") try: texts_df = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/{split}/0000.parquet", columns=[column_name]) except pl.exceptions.ComputeError: try: texts_df = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/partial-{split}/0000.parquet", columns=[column_name]) except pl.exceptions.ComputeError: try: texts_df = pl.read_parquet(f"hf://datasets/{dataset}@~parquet/{config}/{split}-part0/0000.parquet", columns=[column_name]) except Exception as error: yield f"❌ {error}", plt.gcf(), pd.DataFrame(), return logging.info("Data fetched.") texts_df = texts_df.to_pandas() # texts = texts_df.sample(100, seed=16)[column_name].values if not full_check else texts_df[column_name].values texts = texts_df.sample(100, random_state=16)[column_name].values if texts_df.shape[0] > 100 else texts_df[column_name].values n_samples = len(texts) for i, text in tqdm(enumerate(texts), desc="scanning with perspective"): data = { "comment": {"text": text}, "languages": ["en"], "requestedAttributes": REQUESTED_ATTRIBUTES } time.sleep(1) try: req_response = requests.post(PERSPECTIVE_URL, json=data, headers=headers) except Exception as e: print(e) return req_att_scores if req_response.ok: response = req_response.json() if ATT_SCORE in response: for req_att in REQUESTED_ATTRIBUTES: if req_att in response[ATT_SCORE]: att_score = response[ATT_SCORE][req_att][SUM_SCORE]["value"] req_att_scores[req_att].append(att_score) else: req_att_scores[req_att].append(0) else: raise ValueError(req_response) else: try: req_response.raise_for_status() except Exception as e: logging.info(e) return req_att_scores if i % 10 == 0: plot_toxicity(req_att_scores) print(len(texts[:i]), len(req_att_scores["TOXICITY"])) yield {"toxicity check in progress...": i / n_samples}, plt.gcf(), pd.DataFrame.from_dict({column_name: texts[:i+1], **req_att_scores}) plot_toxicity(req_att_scores) yield {"toxicity check finished.": 1.}, plt.gcf(), pd.DataFrame.from_dict({column_name: texts, **req_att_scores}) def proportion_non_ascii(s): """ Compute the proportion of non-ASCII characters in a string. Parameters: s (str): The input string. Returns: float: The proportion of non-ASCII characters in the string. """ non_ascii_count = sum(1 for c in s if ord(c) > 127) total_chars = len(s) return non_ascii_count / total_chars if total_chars > 0 else 0.0 def non_ascii_check(texts_df, column_name): texts = texts_df[column_name].to_list() with multiprocessing.Pool(processes=8) as pool: props = pool.map(proportion_non_ascii, texts) # non_ascii_df = pd.DataFrame.from_dict({"prop_non_ascii": props, "text": texts}) plt.hist(props, bins=20, range=(0., 1.)) plt.title('Histogram of proportion of non-ASCII characters') plt.xlabel('Proportion of non-ASCII characters') plt.ylabel('Number of texts') return plt.gcf() with gr.Blocks() as demo: gr.Markdown( """ # 💫 Dataset Quality Checker 💫 Use [nvidia/quality-classifier-deberta](https://huggingface.co/nvidia/quality-classifier-deberta) on any text dataset on the Hub. ## Select dataset and text column """ ) with gr.Row(): with gr.Column(scale=3): dataset_name = HuggingfaceHubSearch( label="Hub Dataset ID", placeholder="Search for dataset id on Huggingface", search_type="dataset", ) subset_dropdown = gr.Dropdown(label="Subset", visible=False) split_dropdown = gr.Dropdown(label="Split", visible=False) # config_name = "default" # TODO: user input with gr.Accordion("Dataset preview", open=False): @gr.render(inputs=[dataset_name, subset_dropdown, split_dropdown]) def embed(name, subset, split): html_code = f""" """ return gr.HTML(value=html_code) text_column_dropdown = gr.Dropdown(label="Text column name", info="Text colum name to check (only non-nested texts are supported)") def _resolve_dataset_selection(dataset: str, default_subset: str, default_split: str): if "/" not in dataset.strip().strip("/"): return { subset_dropdown: gr.Dropdown(visible=False), split_dropdown: gr.Dropdown(visible=False), text_column_dropdown: gr.Dropdown(info="Text colum name to check (only non-nested texts are supported)"), } info_resp = session.get(f"https://datasets-server.huggingface.co/info?dataset={dataset}", timeout=3).json() if "error" in info_resp: return { subset_dropdown: gr.Dropdown(visible=False), split_dropdown: gr.Dropdown(visible=False), text_column_dropdown: gr.Dropdown(label="Text column name", info="Text colum name to check (only non-nested texts are supported)") } subsets: list[str] = list(info_resp["dataset_info"]) subset = default_subset if default_subset in subsets else subsets[0] splits: list[str] = info_resp["dataset_info"][subset]["splits"] split = default_split if default_split in splits else splits[0] features = info_resp["dataset_info"][subset]["features"] text_features = [feature_name for feature_name, feature in features.items() if isinstance(feature, dict) and feature.get("dtype") == "string"] # and feature.get("_type") == "Value"] return { subset_dropdown: gr.Dropdown(value=subset, choices=subsets, visible=len(subsets) > 1), split_dropdown: gr.Dropdown(value=split, choices=splits, visible=len(splits) > 1), text_column_dropdown: gr.Dropdown(choices=text_features, label="Text column name", info="Text colum name to check (only non-nested texts are supported)"), } @dataset_name.change(inputs=[dataset_name], outputs=[subset_dropdown, split_dropdown, text_column_dropdown]) def show_input_from_subset_dropdown(dataset: str) -> dict: return _resolve_dataset_selection(dataset, default_subset="default", default_split="train") @subset_dropdown.change(inputs=[dataset_name, subset_dropdown], outputs=[subset_dropdown, split_dropdown, text_column_dropdown]) def show_input_from_subset_dropdown(dataset: str, subset: str) -> dict: return _resolve_dataset_selection(dataset, default_subset=subset, default_split="train") @split_dropdown.change(inputs=[dataset_name, subset_dropdown, split_dropdown], outputs=[subset_dropdown, split_dropdown, text_column_dropdown]) def show_input_from_split_dropdown(dataset: str, subset: str, split: str) -> dict: return _resolve_dataset_selection(dataset, default_subset=subset, default_split=split) gr.Markdown("## Run nvidia quality classifier") batch_size = gr.Slider(0, 64, 32, step=4, label="Inference batch size", info="(set this to smaller value if this space crashes.)") num_examples = gr.Slider(0, 1000, 500, step=10, label="Number of examples", info="Number of random examples to run quality classifier on") gr_check_btn = gr.Button("Check Dataset") progress_bar = gr.Label(show_label=False) plot = gr.BarPlot() with gr.Accordion("Explore some individual examples for each class", open=False): gr.Markdown("### Low") df_low = gr.DataFrame() gr.Markdown("### Medium") df_medium = gr.DataFrame() gr.Markdown("### High") df_high = gr.DataFrame() texts_df = gr.DataFrame(visible=False) gr.Examples( [ ["HuggingFaceFW/fineweb-edu", "default", "train", "text", 16, 500], ["fka/awesome-chatgpt-prompts", "default", "train", "prompt", 64, 200], ["proj-persona/PersonaHub", "instruction", "train", "synthesized text", 32, 1000], ["argilla/FinePersonas-v0.1", "default", "train", "persona", 64, 1000], ["Open-Orca/OpenOrca", "default", "train", "response", 16, 500], ], [dataset_name, subset_dropdown, split_dropdown, text_column_dropdown, batch_size, num_examples], [progress_bar, plot, df_low, df_medium, df_high, texts_df], fn=run_quality_check, run_on_click=False, cache_examples=False, ) gr_check_btn.click( run_quality_check, inputs=[dataset_name, subset_dropdown, split_dropdown, text_column_dropdown, batch_size, num_examples], outputs=[progress_bar, plot, df_low, df_medium, df_high, texts_df] ) # gr.Markdown("""## Compute text quality measures # * proportion of non-ascii characters # * #TODO""") # gr_ascii_btn = gr.Button("Data measures") # non_ascii_hist = gr.Plot() # # gr_ascii_btn.click(non_ascii_check, inputs=[texts_df, text_column], outputs=[non_ascii_hist]) gr.Markdown("## Explore toxicity") # checkbox = gr.Checkbox(value=False, label="Run on full first parquet data (better not)") gr_toxicity_btn = gr.Button("Run perpspective API to check toxicity of random samples.") toxicity_progress_bar = gr.Label(show_label=False) toxicity_hist = gr.Plot() with gr.Accordion("Explore examples with toxicity scores:", open=False): toxicity_df = gr.DataFrame() gr_toxicity_btn.click( call_perspective_api, inputs=[texts_df, text_column_dropdown, dataset_name, subset_dropdown, split_dropdown],#, checkbox], outputs=[toxicity_progress_bar, toxicity_hist, toxicity_df] ) demo.launch()