| --- |
| language: |
| - tr |
| license: mit |
| base_model: dbmdz/bert-base-turkish-128k-uncased |
| tags: |
| - text-classification |
| - emotion-classification |
| - turkish |
| - bert |
| - social-media |
| pipeline_tag: text-classification |
| widget: |
| - text: "Bugün harika bir gün geçirdim, çok mutluyum 😊" |
| --- |
| |
| # Turkish Social Media Emotion Classifier (BERTurk fine-tuned) |
|
|
| A fine-tuned version of [`dbmdz/bert-base-turkish-128k-uncased`](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) for **7-class emotion classification** of Turkish (and mixed Turkish/English) social media text. |
|
|
| ## Labels |
|
|
| The model predicts one of 7 basic emotion categories (Ekman's basic emotions + neutrality): |
|
|
| | Label | Description | |
| |---|---| |
| | `happiness` | Joy, gratitude, positive appreciation | |
| | `surprise` | Shock, disbelief, unexpectedness | |
| | `sadness` | Grief, sorrow | |
| | `anger` | Frustration, criticism, hostility | |
| | `fear` | Anxiety, worry, distress | |
| | `disgust` | Revulsion, contempt | |
| | `neutral` | No dominant emotional content | |
|
|
| ## Training Data |
|
|
| The model was fine-tuned on a semi-supervised training set of Turkish/English social media messages (brand posts and user comments), weakly labeled via emoji co-occurrence with emotion lexicons (Turkish Emotion Lexicon and NRC Emotion Lexicon). The training corpus is heavily imbalanced toward happiness, reflecting the natural distribution of emoji-labeled social media content: |
|
|
| | Label | N | % | |
| |---|---:|---:| |
| | Happiness | 68,977 | 66.5 | |
| | Anger | 11,156 | 10.8 | |
| | Surprise | 7,349 | 7.1 | |
| | Fear | 6,279 | 6.0 | |
| | Sadness | 5,785 | 5.6 | |
| | Disgust | 3,322 | 3.2 | |
| | Neutrality | 919 | 0.9 | |
| | **Total** | **103,787** | **100.0** | |
|
|
| ## Training Procedure |
|
|
| Fine-tuned for 5 epochs with a 15% held-out validation split. |
|
|
| | Epoch | Training Loss | Validation Loss | Accuracy | Macro F1 | |
| |---|---|---|---|---| |
| | 1 | 0.6537 | 0.5061 | 0.8307 | 0.7377 | |
| | 2 | 0.4716 | 0.4775 | 0.8370 | 0.7487 | |
| | 3 | 0.4251 | 0.4842 | 0.8354 | 0.7492 | |
| | 4 | 0.3907 | 0.4846 | 0.8372 | 0.7568 | |
| | 5 | 0.3674 | 0.4937 | 0.8352 | 0.7547 | |
|
|
| ## Evaluation Results |
|
|
| Evaluated on a held-out 15% test split (n=15,569): |
|
|
| | Label | Precision | Recall | F1-score | Support | |
| |---|---:|---:|---:|---:| |
| | Anger | 0.61 | 0.72 | 0.66 | 1,674 | |
| | Disgust | 0.97 | 0.83 | 0.90 | 498 | |
| | Fear | 0.79 | 0.55 | 0.65 | 942 | |
| | Happy | 0.88 | 0.94 | 0.91 | 10,347 | |
| | Neutral | 0.90 | 0.94 | 0.92 | 138 | |
| | Sad | 0.87 | 0.67 | 0.76 | 867 | |
| | Surprise | 0.75 | 0.45 | 0.56 | 1,103 | |
| | **Accuracy** | | | **0.84** | 15,569 | |
| | **Macro avg** | 0.82 | 0.73 | 0.77 | 15,569 | |
| | **Weighted avg** | 0.84 | 0.84 | 0.83 | 15,569 | |
|
|
| Model performance is strongest on the majority `happy` class and the (small-support) `disgust` and `neutral` classes, and weaker on `surprise` and `fear`, largely reflecting class imbalance in the weakly-labeled training data. |
|
|
| ## Intended Use |
|
|
| This model classifies the dominant emotion in Turkish-language social media text, including brand posts and user replies/comments. It may generalize to other Turkish social media sentiment/emotion tasks depending on domain similarity. |
|
|
| **Limitations:** Training labels were derived automatically from emoji co-occurrence rather than human annotation, so label noise is present. Performance on minority classes (`fear`, `surprise`) is comparatively weaker. |
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
| |
| model_name = "<your-username>/<model-repo-name>" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) |
| |
| classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) |
| classifier("Bugün harika bir gün geçirdim, çok mutluyum 😊") |
| ``` |
|
|
| ## Base Model |
|
|
| This model was fine-tuned from [`dbmdz/bert-base-turkish-128k-uncased`](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) (BERTurk), a Turkish BERT model trained by MDZ Digital Library team at the Bavarian State Library. |
|
|
