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---
datasets:
- sem_eval_2018_task_1
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
## Description
The **BERT-Emotions-Classifier** is a fine-tuned **BERT-based** model designed for multi-label emotion classification. It has been trained on the sem_eval_2018_task_1 dataset, which includes text samples labeled with a variety of emotions, including anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, and trust. The model is capable of classifying text inputs into one or more of these emotion categories.
## Overview
+ **Model Name**: BERT-Emotions-Classifier
+ **Task**: Multi-label emotion classification
+ **Dataset**: sem_eval_2018_task_1
+ **Labels**: ['anger', 'anticipation', 'disgust', 'fear', 'joy', 'love', 'optimism', 'pessimism', 'sadness', 'surprise', 'trust']
+ **Base Model**: BERT (Bidirectional Encoder Representations from Transformers)
### Input Format
The model expects text input in the form of a string.
### Output Format
+ The model provides a list of labels and associated scores, indicating the predicted emotions and their confidence scores.
### Example Applications
+ Emotion analysis in social media posts
+ Sentiment analysis in customer reviews
+ Content recommendation based on emotional context
## Limitations
+ **Limited Emotion Categories**: The BERT-Emotions-Classifier model is trained on a specific set of emotion categories. It may not accurately classify emotions that do not fall within these predefined categories.
+ **Model Performance**: The accuracy of emotion classification depends on the quality and diversity of the training data. The model's performance may vary for text inputs with uncommon or complex emotional expressions.
+ **Bias and Fairness**: Like any machine learning model, the BERT-Emotions-Classifier may exhibit bias in its predictions. Care should be taken to address and mitigate bias in real-world applications to ensure fairness and inclusivity.
+ **Input Length**: The model has limitations on the maximum input text length it can process effectively. Very long texts may be truncated or may not receive accurate classifications.
## Ethical Considerations
When using this model, it's essential to consider the ethical implications of emotion analysis. Ensure that the use of emotional data respects privacy and consent, and avoid making decisions that could have adverse effects based solely on emotion analysis.
## Inference
```python
from transformers import pipeline
# Load the BERT-Emotions-Classifier
classifier = pipeline("text-classification", model="ayoubkirouane/BERT-Emotions-Classifier")
# Input text
text = "Your input text here"
# Perform emotion classification
results = classifier(text)
# Display the classification results
print(results)
``` |