Update README.md

README.md

@@ -1,11 +1,76 @@
 ---
+language: vi
+tags:
+- emotion-recognition
+- vietnamese
+- phobert
+license: apache-2.0
 datasets:
-- visolex/UIT-VSMEC
-language:
-- vi
+- VSMEC
 metrics:
 - accuracy
+- f1
+model-index:
+- name: phobert-emotion
+  results:
+  - task:
+      type: text-classification
+      name: Emotion Recognition
+    dataset:
+      name: VSMEC
+      type: custom
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: <INSERT_ACCURACY>
+    - name: F1 Score
+      type: f1
+      value: <INSERT_F1_SCORE>
 base_model:
 - vinai/phobert-base
 pipeline_tag: text-classification
----
+---
+
+# PhoBERT-Emotion: Emotion Recognition for Vietnamese Text
+
+This model is a fine-tuned version of [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) on the **VSMEC** dataset for emotion recognition in Vietnamese text. It achieves competitive performance on this task.
+
+## Model Details
+
+- **Base Model**: [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base)
+- **Dataset**: [VSMEC](https://github.com/uitnlp/vsmec) (Vietnamese Social Media Emotion Corpus)
+- **Fine-tuning Framework**: HuggingFace Transformers
+- **Hyperparameters**:
+  - Batch size: `32`
+  - Learning rate: `5e-5`
+  - Epochs: `100`
+  - Max sequence length: `256`
+
+## Dataset
+
+The model was trained on the **VSMEC** dataset, which contains Vietnamese social media text annotated with emotion labels. The dataset includes the following emotion categories: 
+`{"Anger": 0, "Disgust": 1, "Enjoyment": 2, "Fear": 3, "Other": 4, "Sadness": 5, "Surprise": 6}`.
+
+## Results
+
+The model was evaluated using the following metrics:
+
+- **Accuracy**: `<INSERT_ACCURACY>`
+- **F1 Score**: `<INSERT_F1_SCORE>`
+
+## 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
+
+tokenizer = AutoTokenizer.from_pretrained("visolex/phobert-emotion")
+model = AutoModelForSequenceClassification.from_pretrained("visolex/phobert-emotion")
+
+text = "Tôi rất vui vì hôm nay trời đẹp!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+outputs = model(**inputs)
+predicted_class = outputs.logits.argmax(dim=-1).item()
+
+print(f"Predicted emotion: {predicted_class}")