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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- sentiment-analysis |
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- text-classification |
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model_name: "Your Model Name" |
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datasets: "dataset-used" |
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--- |
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--- |
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# **Fine-Tuned Sentiment Analysis Model** |
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[](https://huggingface.co/ktr008/sentiment) |
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🔗 **Model ID:** `ktr008/sentiment` |
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📅 **Last Updated:** `2025-02-25` |
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🚀 **Framework:** PyTorch | Transformers |
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## **📌 Model Description** |
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This is a **fine-tuned RoBERTa model** for **sentiment analysis** based on the **Twitter RoBERTa Base** model. It classifies text into **Positive, Neutral, or Negative** sentiments. |
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## **🛠️ How to Use** |
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### **Install Dependencies** |
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```sh |
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pip install transformers torch scipy |
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``` |
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### **Load Model & Tokenizer** |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import numpy as np |
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from scipy.special import softmax |
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model = AutoModelForSequenceClassification.from_pretrained("ktr008/sentiment") |
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tokenizer = AutoTokenizer.from_pretrained("ktr008/sentiment") |
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def predict_sentiment(text): |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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scores = output[0][0].detach().numpy() |
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scores = softmax(scores) |
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labels = ["Negative", "Neutral", "Positive"] |
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ranking = np.argsort(scores)[::-1] |
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return {labels[i]: round(float(scores[i]), 4) for i in ranking} |
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# Example usage |
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print(predict_sentiment("I love this product!")) |
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``` |
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## **💡 Training Details** |
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- **Base Model:** `cardiffnlp/twitter-roberta-base-sentiment-latest` |
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- **Dataset:** Custom dataset with labeled sentiment texts |
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- **Fine-Tuning:** Performed on AWS EC2 with PyTorch |
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- **Batch Size:** 16 |
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- **Optimizer:** AdamW |
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- **Learning Rate:** 5e-5 |
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## **📝 Example Predictions** |
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| Text | Prediction | |
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|-------|------------| |
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| `"I love this product!"` | Positive (98.3%) | |
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| `"It's an okay experience."` | Neutral (67.4%) | |
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| `"I hate this! Never buying again."` | Negative (92.1%) | |
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## **📈 Performance** |
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- **Accuracy:** `0.9344` |
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## **📌 Model Limitations** |
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- May struggle with **sarcasm or ambiguous phrases**. |
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- Can be **biased** towards the training dataset. |
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- Not suitable for **long texts** without truncation. |
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## **📍 Citation** |
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If you use this model, please cite: |
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``` |
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@model{ktr008_sentiment_2025, |
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author = {ktr008}, |
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title = {Fine-Tuned Sentiment Analysis Model}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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version = {1.0} |
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} |
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``` |
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## **📬 Contact** |
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For issues or suggestions, reach out to me on [Hugging Face](https://huggingface.co/ktr008) 🚀 |
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--- |
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