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FlukeTJ/
distilbert-base-thai-sentiment

Name: distilbert-base-thai-sentiment
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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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