Instructions to use digo-prayudha/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use digo-prayudha/emotion_classification with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/paligemma-3b-pt-224") model = PeftModel.from_pretrained(base_model, "digo-prayudha/emotion_classification") - Notebooks
- Google Colab
- Kaggle
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README.md
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This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
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## Intended uses & limitations
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This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
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## Training Data
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This model was trained on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
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The dataset contains:
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- **800 images** annotated with **8 emotion labels**:
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- Anger
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- Contempt
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- Disgust
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- Fear
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- Happy
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- Neutral
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- Sad
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- Surprise
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## Intended uses & limitations
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