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

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

Framework versions

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