model card created

README.md

@@ -1,3 +1,93 @@
----
-license: apache-2.0
----
+---
+language: 
+- hu
+- sk
+- pl
+- cs
+tags:
+- emotion-classification
+- roberta
+- fine-tuned
+- multilingual
+
+license: mit
+datasets:
+- custom
+
+model-index:
+- name: Multilingual Fine-tuned RoBERTa for Emotion Classification
+  results:
+  - task:
+      type: text-classification
+      name: Multilingual Emotion Classification
+    dataset:
+      name: Multilingual Custom Dataset (Hungarian, Slovak, Polish, Czech)
+      type: text
+    metrics:
+      - name: Precision (Macro Avg)
+        type: precision
+        value: 0.86
+      - name: Recall (Macro Avg)
+        type: recall
+        value: 0.86
+      - name: F1 Score (Macro Avg)
+        type: f1
+        value: 0.86
+      - name: Accuracy
+        type: accuracy
+        value: 0.84
+
+---
+
+# Multilingual Fine-tuned RoBERTa Model for Emotion Classification
+
+## Model Description
+This model is a multilingual fine-tuned version of the [RoBERTa](https://huggingface.co/roberta-base) model, specifically tailored for emotion classification tasks in Hungarian, Slovak, Polish, and Czech languages. 
+The model was trained to classify textual data into six emotional categories (**anger, fear, disgust, sadness, joy,** and **none of them**).
+
+## Intended Use
+This model is intended for classifying textual data into emotional categories across multiple languages, including Hungarian, Slovak, Polish, and Czech. 
+It can be used in applications such as sentiment analysis, social media monitoring, customer feedback analysis, and similar tasks. 
+The model predicts the dominant emotion in a given text among the six predefined categories.
+
+## Metrics
+
+| **Class**       | **Precision (P)** | **Recall (R)** | **F1-Score (F1)** |
+|-----------------|-------------------|----------------|-------------------|
+| **anger**       | 0.74              | 0.81           | 0.77              |
+| **fear**        | 0.98              | 0.98           | 0.98              |
+| **disgust**     | 0.94              | 0.95           | 0.95              |
+| **sadness**     | 0.87              | 0.87           | 0.87              |
+| **joy**         | 0.89              | 0.89           | 0.89              |
+| **none of them**| 0.77              | 0.69           | 0.73              |
+| **Accuracy**    |                   |                | **0.84**          |
+| **Macro Avg**   | 0.86              | 0.86           | 0.86              |
+| **Weighted Avg**| 0.84              | 0.84           | 0.84              |
+
+### Overall Performance
+- **Accuracy:** 0.84
+- **Macro Average Precision:** 0.86
+- **Macro Average Recall:** 0.86
+- **Macro Average F1-Score:** 0.86
+
+### Class-wise Performance
+The model demonstrates strong performance across different emotional categories, with particularly high precision, recall, and F1 scores in the **fear**, **disgust**, and **joy** categories. 
+The model performs moderately well in detecting **anger** and **none of them** categories, but still achieves adequate accuracy in these cases.
+
+## Limitations
+- **Context Sensitivity:** The model may struggle with recognizing emotions that require deeper contextual understanding.
+- **Class Imbalance:** The model's performance on the "none of them" category suggests that further training with more balanced datasets could improve accuracy.
+- **Generalization:** The model's performance may vary depending on the text's domain, language style, and length, especially across different languages.
+
+## How to Use
+You can use this model directly with the `transformers` library from Hugging Face. Below is an example of how to load and use the model:
+
+```python
+from transformers import pipeline
+
+# Load the fine-tuned model
+classifier = pipeline("text-classification", model="visegradmedia-emotion/Emotion_RoBERTa_pooled_V4")
+
+# Example usage
+result = classifier("Nagyon örömtelinek érzem magam ma!")
+print(result)