| --- |
| library_name: transformers |
| pipeline_tag: text-classification |
| tags: |
| - sentiment-analysis |
| - bert |
| - goemotions |
| - nlp |
| --- |
| |
|
|
| # Model Card for Sentiment Analysis (Positive / Negative / Neutral) |
|
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| ## Model Details |
|
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| ### Model Description |
|
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| This model is a fine-tuned BERT-based transformer model for **sentiment analysis**. |
| It is trained using the GoEmotions dataset, where the original 27 emotion labels are **mapped into three categories**: |
|
|
| - Positive 😊 |
| - Negative 😡 |
| - Neutral 😐 |
|
|
| The model takes text input and predicts its overall sentiment. |
|
|
| - **Developed by:** Krish Agrawal |
| - **Model type:** BERT (Transformer-based classification model) |
| - **Language(s):** English |
| - **License:** Apache 2.0 |
| - **Finetuned from model:** bert-base-uncased |
|
|
| --- |
|
|
| ### Model Sources |
|
|
| - **Repository:** https://github.com/krishagrawal623/bert-goemotions-sentiment-model.git |
| - **Dataset:** GoEmotions (Google Research) |
|
|
| --- |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| This model can be used for: |
| - Sentiment analysis (positive / negative / neutral) |
| - Social media monitoring |
| - Customer feedback analysis |
| - Review classification |
|
|
| Example: |
| - Input: "This product is amazing!" |
| - Output: Positive |
|
|
| --- |
|
|
| ### Downstream Use |
|
|
| - Chatbots 🤖 |
| - Business analytics dashboards |
| - Customer support systems |
| - Market research tools |
|
|
| --- |
|
|
| ### Out-of-Scope Use |
|
|
| - Not suitable for: |
| - Non-English text |
| - Detecting detailed emotions (like anger, joy, fear separately) |
| - Sarcasm or complex context |
|
|
| --- |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - Mapping 27 emotions → 3 classes may **lose detailed emotional information** |
| - May misclassify: |
| - Sarcasm |
| - Mixed sentiments |
| - Dataset bias may affect predictions |
|
|
| --- |
|
|
| ### Recommendations |
|
|
| - Use only for general sentiment analysis |
| - Avoid using for sensitive or critical decisions |
| - Fine-tune further for domain-specific tasks |
|
|
| --- |
|
|
| ## How to Get Started with the Model |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline("text-classification", model="your-username/your-model-name") |
| |
| result = classifier("I am very happy today!") |
| print(result) |
| |
