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README.md

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+---
+license: apache-2.0
+base_model: vinai/bartpho-syllable
+tags:
+- vietnamese
+- emotion-recognition
+- text-classification
+- VSMEC
+datasets:
+- VSMEC
+metrics:
+- accuracy
+- macro-f1
+model-index:
+- name: bartpho
+  results:
+  - task:
+      type: text-classification
+      name: Emotion Recognition
+    dataset:
+      name: VSMEC
+      type: VSMEC
+    metrics:
+      - type: accuracy
+        value: 0.6378066378066378
+      - type: macro-f1
+        value: 0.6288407005570578
+---
+
+# bartpho: Emotion Recognition for Vietnamese Text
+
+This model is a fine-tuned version of [vinai/bartpho-syllable](https://huggingface.co/vinai/bartpho-syllable) on the **VSMEC** dataset for emotion recognition in Vietnamese text.
+
+## Model Details
+
+* **Base Model**: vinai/bartpho-syllable
+* **Description**: BartPho - Vietnamese BART
+* **Dataset**: VSMEC (Vietnamese Social Media Emotion Corpus)
+* **Fine-tuning Framework**: HuggingFace Transformers
+* **Task**: Emotion Classification (7 classes)
+
+### Hyperparameters
+
+* Batch size: `32`
+* Learning rate: `2e-5`
+* Epochs: `100`
+* Max sequence length: `256`
+* Weight decay: `0.01`
+* Warmup steps: `500`
+
+## Dataset
+
+The model was trained on the **VSMEC** dataset, which contains 6,927 Vietnamese social media text samples annotated with emotion labels. The dataset includes the following emotion categories:
+
+* **Enjoyment** (0): Positive emotions, joy, happiness
+* **Sadness** (1): Sad, disappointed, gloomy feelings
+* **Anger** (2): Angry, frustrated, irritated
+* **Fear** (3): Scared, anxious, worried
+* **Disgust** (4): Disgusted, repelled
+* **Surprise** (5): Surprised, shocked, amazed
+* **Other** (6): Neutral or unclassified emotions
+
+## Results
+
+The model was evaluated using the following metrics:
+
+* **Accuracy**: `0.6378`
+* **Macro-F1**: `0.6288`
+* **Macro-Precision**: `0.6464`
+* **Macro-Recall**: `0.6326`
+
+## Usage
+
+You can use this model for emotion recognition in Vietnamese text. Below is an example of how to use it with the HuggingFace Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(f"visolex/{model_key}")
+model = AutoModelForSequenceClassification.from_pretrained(f"visolex/{model_key}")
+
+# Example text
+text = "Tôi rất vui vì hôm nay trời đẹp!"
+
+# Tokenize
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+
+# Predict
+outputs = model(**inputs)
+predicted_class = outputs.logits.argmax(dim=-1).item()
+
+# Map to emotion name
+emotion_map = {{
+    0: "Enjoyment",
+    1: "Sadness",
+    2: "Anger",
+    3: "Fear",
+    4: "Disgust",
+    5: "Surprise",
+    6: "Other"
+}}
+
+predicted_emotion = emotion_map[predicted_class]
+print(f"Text: {{text}}")
+print(f"Predicted emotion: {{predicted_emotion}}")
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{{visolex_emotion_{model_key},
+  title={{ {description} for Vietnamese Emotion Recognition}},
+  author={{ViSoLex Team}},
+  year={{2024}},
+  url={{https://huggingface.co/visolex/{model_key}}}
+}}
+```
+
+## License
+
+This model is released under the Apache-2.0 license.
+
+## Acknowledgments
+
+* Base model: [{base_model}](https://huggingface.co/{base_model})
+* Dataset: VSMEC (Vietnamese Social Media Emotion Corpus)
+* ViSoLex Toolkit