|
--- |
|
license: mit |
|
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest |
|
language: |
|
- en |
|
library_name: transformers |
|
tags: |
|
- Roberta |
|
- Sentiment Analysis |
|
widget: |
|
- text: This product is really great! |
|
- text: This product is really bad! |
|
--- |
|
|
|
# 🌟 Fine-tuned RoBERTa for Sentiment Analysis on Reviews 🌟 |
|
|
|
This is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on the [Amazon Reviews dataset](https://www.kaggle.com/datasets/bittlingmayer/amazonreviews) for sentiment analysis. |
|
|
|
## 📜 Model Details |
|
|
|
- **🆕 Model Name:** `AnkitAI/reviews-roberta-base-sentiment-analysis` |
|
- **🔗 Base Model:** `cardiffnlp/twitter-roberta-base-sentiment-latest` |
|
- **📊 Dataset:** [Amazon Reviews](https://www.kaggle.com/datasets/bittlingmayer/amazonreviews) |
|
- **⚙️ Fine-tuning:** This model was fine-tuned for sentiment analysis with a classification head for binary sentiment classification (positive and negative). |
|
|
|
## 🏋️ Training |
|
|
|
The model was trained using the following parameters: |
|
|
|
- **🔧 Learning Rate:** 2e-5 |
|
- **📦 Batch Size:** 16 |
|
- **⚖️ Weight Decay:** 0.01 |
|
- **📅 Evaluation Strategy:** Epoch |
|
|
|
|
|
### 🏋️ Training Details |
|
|
|
- **📉 Eval Loss:** 0.1049 |
|
- **⏱️ Eval Runtime:** 3177.538 seconds |
|
- **📈 Eval Samples/Second:** 226.591 |
|
- **🌀 Eval Steps/Second:** 7.081 |
|
- **🏃 Train Runtime:** 110070.6349 seconds |
|
- **📊 Train Samples/Second:** 78.495 |
|
- **🌀 Train Steps/Second:** 2.453 |
|
- **📉 Train Loss:** 0.0858 |
|
- **⏳ Eval Accuracy:** 97.19% |
|
- **🌀 Eval Precision:** 97.9% |
|
- **⏱️ Eval Recall:** 97.18% |
|
- **📈 Eval F1 Score:** 97.19% |
|
|
|
|
|
## 🚀 Usage |
|
|
|
You can use this model directly with the Hugging Face `transformers` library: |
|
|
|
```python |
|
from transformers import RobertaForSequenceClassification, RobertaTokenizer |
|
|
|
model_name = "AnkitAI/reviews-roberta-base-sentiment-analysis" |
|
model = RobertaForSequenceClassification.from_pretrained(model_name) |
|
tokenizer = RobertaTokenizer.from_pretrained(model_name) |
|
|
|
# Example usage |
|
inputs = tokenizer("This product is great!", return_tensors="pt") |
|
outputs = model(**inputs) # 1 for positive, 0 for negative |
|
|
|
``` |
|
|
|
|
|
## 📜 License |
|
|
|
This model is licensed under the [MIT License](LICENSE). |
