---
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-uncased'
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: 0.0012
Neutral: 0.9978
Positive: 0.0010

Input text 2: drop dead
Predicted label: Negative
Predicted probabilities:
Negative: 0.9973
Neutral: 0.0017
Positive: 0.0010

Input text 3: you're the best friend i could ever have in this whole wide world!!
Predicted label: Positive
Predicted probabilities:
Negative: 0.0004
Neutral: 0.0003
Positive: 0.9992
```