Create README.md

README.md

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+---
+language:
+- en
+---
+# Model Description
+This model was created for a thesis. This model was created with a dataset obtained from social media X, with hashtag #IsraelPalestineWar, collected from October - November 2023. This model is meant to classify sentiment of tweets and suitable for english language. 
+
+- **Reference Paper** : [Coming soon](comingsoon)
+- **Github** : [Coming soon](comingsoon)
+
+## Model Labels:
+- Label 0: **Negative**
+- Label 1: **Neutral**
+- Label 2: **Positive**
+
+
+## How to use model
+- **Loading the model**
+```python
+from transformers import RobertaForSequenceClassification, RobertaTokenizer
+import torch
+
+output_model_dir = 'RappyProgramming/IPW-DistilBERT-cased'
+model = RobertaForSequenceClassification.from_pretrained(output_model_dir)
+tokenizer = RobertaTokenizer.from_pretrained(output_model_dir)
+
+```
+- **Sample Input**
+```python
+input_texts = [
+    "this meeting is scheduled for next week",
+    "drop dead",
+    "you're the best friend i could ever have in this whole wide world!!"
+]
+```
+- **Output**
+```python
+inputs = tokenizer(input_texts, return_tensors="pt", padding=True, truncation=True)
+with torch.no_grad():
+    outputs = model(**inputs)
+
+predicted_class_indices = torch.argmax(outputs.logits, dim=1).tolist()
+probs = torch.softmax(outputs.logits, dim=1).tolist()
+labels = ["Negative", "Neutral", "Positive"]
+
+for i, input_text in enumerate(input_texts):
+    predicted_label = labels[predicted_class_indices[i]]
+    predicted_probabilities = {label: prob for label, prob in zip(labels, probs[i])}
+    
+    print(f"Input text {i+1}: {input_text}")
+    print(f"Predicted label: {predicted_label}")
+    print("Predicted probabilities:")
+    
+    for label, prob in predicted_probabilities.items():
+        print(f"{label}: {prob:.4f}")
+        
+    print()
+```
+```
+Output:
+
+Input text 1: this meeting is scheduled for next week
+Predicted label: Neutral
+Predicted probabilities:
+Negative: 0.0011
+Neutral: 0.9979
+Positive: 0.0010
+
+Input text 2: drop dead
+Predicted label: Negative
+Predicted probabilities:
+Negative: 0.9958
+Neutral: 0.0028
+Positive: 0.0014
+
+Input text 3: you're the best friend i could ever have in this whole wide world!!
+Predicted label: Positive
+Predicted probabilities:
+Negative: 0.0007
+Neutral: 0.0003
+Positive: 0.9990
+```