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Download scripts/download_data.py from ArturMats/sentiment-analysis-api: direct link, hf CLI and curl.
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https://huggingface.co/spaces/ArturMats/sentiment-analysis-api/resolve/main/scripts/download_data.py
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hf download hf://spaces/ArturMats/sentiment-analysis-api/scripts/download_data.py
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curl -L -o download_data.py https://huggingface.co/spaces/ArturMats/sentiment-analysis-api/resolve/main/scripts/download_data.py
825 Bytes
| # Importo le librerie necessarie: | |
| import pandas as pd | |
| from datasets import load_dataset | |
| def download_dataset(): | |
| """ | |
| Funzione che scarica un dataset presente in rete, assegna le classi all'output e salva i dati in un file .csv | |
| """ | |
| # Scarico il dataset e ne prendo solo una parte: | |
| print("Inizio download...") | |
| dataset = load_dataset("cardiffnlp/tweet_eval", "sentiment", split="test[:200]") | |
| # Assegno le "etichette" all'output: | |
| labels = {0: "negative", 1: "neutral", 2: "positive"} | |
| df = pd.DataFrame(dataset) | |
| df["sentiment_label"] = df["label"].map(labels) | |
| # Definisco il percorso dove salvare il file e salvo in formato .csv: | |
| path = "data/tweets.csv" | |
| df.to_csv(path, index=False) | |
| print(f"File salvato in {path}") | |
| if __name__ == "__main__": | |
| download_dataset() |