---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- f1_score
model-index:
- name: results
  results: []
license: apache-2.0
language:
- th
base_model:
- distilbert/distilbert-base-uncased
---

# Model: Fine-Tuned Transformer

This model is a fine-tuned version of the Transformer architecture using a custom-trained BPE tokenizer and a DistilBERT-like configuration. It has been fine-tuned on a specific dataset with a sequence length of 512 tokens for a classification task involving 3 labels.

### Key Evaluation Metrics:
- **Loss**: 0.3656
- **F1 Micro**: 0.8763
- **Validation Set Size**: 7608 samples

## Usage
```python
from transformers import DistilBertForSequenceClassification, PreTrainedTokenizerFast
import torch

# Load the tokenizer and model
tokenizers = PreTrainedTokenizerFast.from_pretrained("FlukeTJ/distilbert-base-thai-sentiment")
models = DistilBertForSequenceClassification.from_pretrained("FlukeTJ/distilbert-base-thai-sentiment")

# Set device (GPU if available, else CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
models = models.to(device)

def predict_sentiment(text):
    # Tokenize the input text without token_type_ids
    inputs = tokenizers(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    inputs.pop("token_type_ids", None)  # Remove token_type_ids if present
    
    inputs = {k: v.to(device) for k, v in inputs.items()}
    
    # Make prediction
    with torch.no_grad():
        outputs = models(**inputs)
    
    # Get probabilities
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    
    # Get the predicted class 
    predicted_class = torch.argmax(probabilities, dim=1).item()
    
    # Map class to sentiment
    sentiment_map = {1: "Neutral", 0: "Positive", 2: "Negative"}
    predicted_sentiment = sentiment_map[predicted_class]
    
    # Get the confidence score
    confidence = probabilities[0][predicted_class].item()
    
    return predicted_sentiment, confidence

# Example usage
texts = [
    "สุดยอดดด"
]

for text in texts:
    sentiment, confidence = predict_sentiment(text)
    print(f"Text: {text}")
    print(f"Predicted Sentiment: {sentiment}")
    print(f"Confidence: {confidence:.2f}")

# =============================
# Result    
# Text: สุดยอดดด
# Predicted Sentiment: Positive
# Confidence: 0.96
# =============================
```

## Model Description

This model is based on a **DistilBERT** architecture with the following configuration:
- **Sequence Length**: 512 tokens
- **Number of Layers**: 6 transformer layers
- **Number of Attention Heads**: 8
- **Vocabulary Size**: 20,000 (custom Byte Pair Encoding tokenizer)
- **Max Position Embeddings**: 512
- **Pad Token ID**: Defined by the custom tokenizer
- **Number of Labels**: 3 (for multi-class classification)

The tokenizer used for this model is a custom Byte Pair Encoding (BPE) tokenizer trained on the combined training and test datasets.

## Tokenizer

A custom tokenizer was built using **Byte Pair Encoding (BPE)** with a vocabulary size of 20,000. The tokenizer was trained on both the training and test sets to capture a wide range of token patterns.

## Training and Evaluation Data
- Training Set Size: 43,112 samples
- Validation Set Size: 7,608 samples
The model was trained and evaluated on a dataset that has not been publicly released. It was trained for a multi-class classification task with 3 possible labels. 

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 88
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

## Training Results

| Training Loss | Step | Validation Loss | F1 Micro |
|:-------------:|:----:|:---------------:|:--------:|
| 0.8035        | 500  | 0.5608          | 0.7821   |
| 0.4855        | 1000 | 0.4392          | 0.8266   |
| 0.3769        | 1500 | 0.3930          | 0.8433   |
| 0.3159        | 2000 | 0.3589          | 0.8675   |
| 0.279         | 2500 | 0.3552          | 0.8697   |
| 0.2463        | 3000 | 0.3812          | 0.8699   |
| 0.226         | 3500 | 0.3619          | 0.8690   |
| 0.2072        | 4000 | 0.3548          | 0.8754   |
| 0.1926        | 4500 | 0.3656          | 0.8763   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1