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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: 0.0
-      - type: macro-f1
-        value: 0.0
----
-
-# 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**: `0.0000`
-* **Macro-F1**: `0.0000`
-* **Macro-Precision**: `0.0000`
-* **Macro-Recall**: `0.0000`
-
-## 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