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