djonuzi/goemotions-emotion-model

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--- library_name: transformers language: - en tags: - text-classification - multi-label - goemotions - distilbert datasets: - go_emotions metrics: - micro_f1 - macro_f1 pipeline_tag: text-classification --- # GoEmotions DistilBERT Emotion Classifier ## Model Overview This model is a fine-tuned version of **distilbert-base-uncased** for **multi-label emotion classification**. It predicts the probability of 28 possible emotions (27 emotion categories plus neutral). The model is trained using the GoEmotions dataset. This classifier takes in a short piece of text and outputs a list of emotions along with confidence scores. --- ## Intended Use - Emotion detection in short texts such as comments, headlines, messages, or social media posts - Educational demonstrations of multi-label classification - Research or experimentation with emotion analysis Not intended for clinical, legal, or high-stakes decision-making. --- ## Dataset **Dataset:** GoEmotions **Source:** https://huggingface.co/datasets/go_emotions **Size:** ~58,000 text examples **Labels:** 28 emotions (multi-label) Each example is annotated with zero or more emotions. The dataset is designed for studying fine-grained emotional expressions in short natural language. --- ## Training Details - **Base model:** distilbert-base-uncased - **Training:** Fine-tuned for 1 epoch - **Task:** Multi-label classification - **Loss function:** Binary cross entropy - **Input length:** 128 tokens - **Optimizer:** AdamW - **Batch size:** 16 Code used in the notebook: ```python model = AutoModelForSequenceClassification.from_pretrained( "distilbert-base-uncased", num_labels=28, problem_type="multi_label_classification" )

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