Update README

README.md

@@ -33,28 +33,86 @@ model-index:
       type: F1 score (Macro)
       value: 0.970818
 ---
-# Model Card for Model ID
+# Model Details of TVL_GeneralLayerClassifier
+
+## Base Model
+This model is fine-tuned from [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese).
+
+## Model Architecture
+- **Type**: BERT-based text classification model
+- **Hidden Size**: 768
+- **Number of Layers**: 12
+- **Number of Attention Heads**: 12
+- **Intermediate Size**: 3072
+- **Max Sequence Length**: 512
+- **Vocabulary Size**: 21,128
+
+## Key Components
+1. **Embeddings**
+   - Word Embeddings
+   - Position Embeddings
+   - Token Type Embeddings
+   - Layer Normalization
+
+2. **Encoder**
+   - 12 layers of:
+     - Self-Attention Mechanism
+     - Intermediate Dense Layer
+     - Output Dense Layer
+     - Layer Normalization
+
+3. **Pooler**
+   - Dense layer for sentence representation
+
+4. **Classifier**
+   - Output layer with 4 classes
+
+## Training Hyperparameters
 
-<!-- Provide a quick summary of what the model is/does. -->
+The model was trained using the following hyperparameters:
 
-## Model Details
+```
+Learning rate: 1e-05
+Batch size: 32
+Number of epochs: 10
+Optimizer: Adam
+Loss function: torch.nn.BCEWithLogitsLoss()
+```
 
-### Model Description
+## Training Infrastructure
 
-<!-- Provide a longer summary of what this model is. -->
+- **Hardware Type:** NVIDIA Quadro RTX8000
+- **Library:** PyTorch
+- **Hours used:** 2hr 56mins
+
+## Model Parameters
+- Total parameters: ~102M (estimated)
+- All parameters are in 32-bit floating point (F32) format
+
+## Input Processing
+- Uses BERT tokenization
+- Supports sequences up to 512 tokens
 
-- **Developed by:** [scfengv](https://huggingface.co/scfengv)
-- **Model type:** BERT Multi-label Text Classification
-- **Language:** Chinese (Zh)
-- **Finetuned from model:** [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese)
+## Output
+- 4-class multi-label classification
 
-### Model Sources
+## Performance Metrics
+- Accuracy score: 0.952902
+- F1 score (Micro): 0.968717
+- F1 score (Macro): 0.970818
 
-- **Repository:** [scfengv/NLP-Topic-Modeling-for-TVL-livestream-comments](https://github.com/scfengv/NLP-Topic-Modeling-for-TVL-livestream-comments)
+## Training Dataset
+This model was trained on the [scfengv/TVL-general-layer-dataset](https://huggingface.co/datasets/scfengv/TVL-general-layer-dataset).
 
-## How to Get Started with the Model
+## Testing Dataset
+
+- [scfengv/TVL-general-layer-dataset](https://huggingface.co/datasets/scfengv/TVL-general-layer-dataset)
+  - validation
+  - Remove Emoji
+  - Emoji2Desc
+  - Remove Punctuation
 
-Use the code below to get started with the model.
+## Usage
 
 ```python
 import torch
@@ -76,7 +134,9 @@ with torch.no_grad():
 print(predictions)
 ```
 
-## Training Details
+## Additional Notes
+- This model is specifically designed for TVL general layer classification tasks.
+- It's based on the Chinese BERT model, indicating it's optimized for Chinese text.
 
 - **Hardware Type:** NVIDIA Quadro RTX8000
 - **Library:** PyTorch