Create README.md

README.md

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+---
+language: "en"
+tags:
+- bert
+- sarcasm-detection
+- text-classification
+widget:
+- text: "CIA Realizes It's Been Using Black Highlighters All These Years."
+---
+
+# English Sarcasm Detector
+
+English Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on bert-base-uncased and the training data consists of ready-made dataset available on Kaggle.
+
+
+## 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]).
+
+Codebase:
+- Git Repo: [Official repository]([https://github.com/helinivan/multilingual-sarcasm-detector]).
+
+---
+
+## Example of classification
+
+```python
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import string
+
+def preprocess_data(text: str) -> str:
+return text.lower().translate(str.maketrans("", "", string.punctuation)).strip()
+
+MODEL_PATH = "helinivan/english-sarcasm-detector"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+
+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]
+confidence = max(probs)
+prediction = probs.index(confidence)
+results = {"is_sarcastic": prediction, "confidence": confidence}
+
+```
+
+Output: 
+
+```
+{'is_sarcastic': 1, 'confidence': 0.9999909400939941}
+```
+
+## Performance
+| Model-Name | F1 | Precision | Recall | Accuracy 
+| ------------- |:-------------| -----| -----| ----| 
+| helinivan/english-sarcasm-detector | 94.48 | 94.46 | 94.51 | 94.48
+| helinivan/multilingual-sarcasm-detector | 90.91 | 91.51 | 90.44 | 91.55