Emotion-X is a state-of-the-art emotion detection model fine-tuned from Microsoft's DeBERTa-Xlarge model. Designed to accurately classify text into one of six emotional categories, Emotion-X leverages the robust capabilities of DeBERTa and fine-tunes it on a comprehensive emotion dataset, ensuring high accuracy and reliability.
Model Details
Model Name:
AnkitAI/deberta-xlarge-base-emotions-classifier
Fine-tuning:
This model was fine-tuned for emotion detection with a classification head for six emotional categories (anger, disgust, fear, joy, sadness, surprise).
Training
The model was trained using the following parameters:
Learning Rate:
2e-5
Batch Size:
4
Weight Decay:
0.01
Evaluation Strategy:
Epoch
Training Details
Evaluation Loss:
0.0858
Evaluation Runtime:
110070.6349 seconds
Evaluation Samples/Second:
78.495
Evaluation Steps/Second:
2.453
Training Loss:
0.1049
Evaluation Accuracy:
94.6%
Evaluation Precision:
94.8%
Evaluation Recall:
94.5%
Evaluation F1 Score:
94.7%
Usage
You can use this model directly with the Hugging Face
transformers
library:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "AnkitAI/deberta-xlarge-base-emotions-classifier"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Example usagedefpredict_emotion(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
outputs = model(**inputs)
logits = outputs.logits
predictions = logits.argmax(dim=1)
return predictions
text = "I'm so happy with the results!"
emotion = predict_emotion(text)
print("Detected Emotion:", emotion)
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