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--- |
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license: mit |
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datasets: |
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- dair-ai/emotion |
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language: |
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- en |
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library_name: transformers |
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widget: |
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- text: I am so happy with the results! |
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- text: I am so pissed with the results! |
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tags: |
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- debarta |
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- debarta-xlarge |
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- emotions-classifier |
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--- |
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# Emotion-X: Fine-tuned DeBERTa-Xlarge Based Emotion Detection |
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This is a fine-tuned version of [microsoft/deberta-xlarge-mnli](https://huggingface.co/microsoft/deberta-xlarge-mnli) for emotion detection on the [dair-ai/emotion](https://huggingface.co/dair-ai/emotion) dataset. |
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## Overview |
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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. |
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## Model Details |
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- **Model Name:** `AnkitAI/deberta-xlarge-base-emotions-classifier` |
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- **Base Model:** `microsoft/deberta-xlarge-mnli` |
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- **Dataset:** [dair-ai/emotion](https://huggingface.co/dair-ai/emotion) |
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- **Fine-tuning:** This model was fine-tuned for emotion detection with a classification head for six emotional categories (anger, disgust, fear, joy, sadness, surprise). |
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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:** 4 |
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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:** 0.0858 |
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- **Evaluation Runtime:** 110070.6349 seconds |
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- **Evaluation Samples/Second:** 78.495 |
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- **Evaluation Steps/Second:** 2.453 |
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- **Training Loss:** 0.1049 |
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- **Evaluation Accuracy:** 94.6% |
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- **Evaluation Precision:** 94.8% |
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- **Evaluation Recall:** 94.5% |
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- **Evaluation F1 Score:** 94.7% |
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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 AutoModelForSequenceClassification, AutoTokenizer |
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model_name = "AnkitAI/deberta-xlarge-base-emotions-classifier" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Example usage |
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def predict_emotion(text): |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128) |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predictions = logits.argmax(dim=1) |
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return predictions |
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text = "I'm so happy with the results!" |
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emotion = predict_emotion(text) |
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print("Detected Emotion:", emotion) |
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``` |
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## Emotion Labels |
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- Anger |
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- Disgust |
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- Fear |
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- Joy |
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- Sadness |
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- Surprise |
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## Model Card Data |
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| Parameter | Value | |
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|-------------------------------|------------------------------| |
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| Model Name | microsoft/deberta-xlarge-mnli | |
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| Training Dataset | dair-ai/emotion | |
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| Learning Rate | 2e-5 | |
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| Per Device Train Batch Size | 4 | |
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| Evaluation Strategy | Epoch | |
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| Best Model Accuracy | 94.6% | |
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## License |
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This model is licensed under the [MIT License](LICENSE). |
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