Update README.md

README.md

@@ -6,18 +6,22 @@ tags:
 - text-classification
 widget:
 - text: "Auto, stop a diesel e benzina dal 2035. Ecco cosa cambia per i consumatori"
+- text: "CIA Realizes It's Been Using Black Highlighters All These Years."
 ---
 
-# Italian Sarcasm Detector
+# Multilingual Sarcasm Detector
 
-Italian Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on dbmdz/bert-base-italian-uncased and the training data consists of scraped data from Italian non-sarcastic newspaper (Il Giornale) and sarcastic newspaper (Lercio).
-
-## Metrics:
+Mltilingual Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on bert-multilingual uncased and the training data consists of ready-made datasets available on Kaggle as well scraped data from multiple newspapers in English, Dutch and Italian.
 
 
 ## Training Data
+	  
+Datasets:
+- English language data: [Kaggle: News Headlines Dataset For Sarcasm Detection]([https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection]).
+- Dutch non-sarcastic data: [Kaggle: Dutch News Articles]([https://www.kaggle.com/datasets/maxscheijen/dutch-news-articles])
 
 Scraped data:
+- Dutch sarcastic news from [De Speld]([https://speld.nl])
 - Italian non-sarcastic news from [Il Giornale]([https://www.ilgiornale.it])
 - Italian sarcastic news from [Lercio]([https://www.lercio.it])
 
@@ -36,12 +40,12 @@ import string
 def preprocess_data(text: str) -> str:
    return text.lower().translate(str.maketrans("", "", string.punctuation)).strip()
 
-MODEL_PATH = "helinivan/italian-sarcasm-detector"
+MODEL_PATH = "helinivan/multilingual-sarcasm-detector"
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
 
-text = "Auto, stop a diesel e benzina dal 2035. Ecco cosa cambia per i consumatori"
+text = "CIA Realizes It's Been Using Black Highlighters All These Years."
 tokenized_text = tokenizer([preprocess_data(text)], padding=True, truncation=True, max_length=512, return_tensors="pt")
 output = model(**tokenized_text)
 probs = output.logits.softmax(dim=-1).tolist()[0]