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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1_score |
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model-index: |
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- name: results |
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results: [] |
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license: apache-2.0 |
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language: |
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- th |
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base_model: |
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- distilbert/distilbert-base-uncased |
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--- |
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# Model: Fine-Tuned Transformer |
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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. |
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### Key Evaluation Metrics: |
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- **Loss**: 0.3656 |
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- **F1 Micro**: 0.8763 |
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- **Validation Set Size**: 7608 samples |
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## Usage |
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```python |
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from transformers import DistilBertForSequenceClassification, PreTrainedTokenizerFast |
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import torch |
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# Load the tokenizer and model |
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tokenizers = PreTrainedTokenizerFast.from_pretrained("FlukeTJ/distilbert-base-thai-sentiment") |
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models = DistilBertForSequenceClassification.from_pretrained("FlukeTJ/distilbert-base-thai-sentiment") |
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# Set device (GPU if available, else CPU) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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models = models.to(device) |
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def predict_sentiment(text): |
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# Tokenize the input text without token_type_ids |
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inputs = tokenizers(text, return_tensors="pt", padding=True, truncation=True, max_length=512) |
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inputs.pop("token_type_ids", None) # Remove token_type_ids if present |
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inputs = {k: v.to(device) for k, v in inputs.items()} |
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# Make prediction |
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with torch.no_grad(): |
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outputs = models(**inputs) |
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# Get probabilities |
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probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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# Get the predicted class |
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predicted_class = torch.argmax(probabilities, dim=1).item() |
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# Map class to sentiment |
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sentiment_map = {1: "Neutral", 0: "Positive", 2: "Negative"} |
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predicted_sentiment = sentiment_map[predicted_class] |
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# Get the confidence score |
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confidence = probabilities[0][predicted_class].item() |
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return predicted_sentiment, confidence |
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# Example usage |
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texts = [ |
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"สุดยอดดด" |
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] |
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for text in texts: |
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sentiment, confidence = predict_sentiment(text) |
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print(f"Text: {text}") |
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print(f"Predicted Sentiment: {sentiment}") |
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print(f"Confidence: {confidence:.2f}") |
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# ============================= |
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# Result |
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# Text: สุดยอดดด |
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# Predicted Sentiment: Positive |
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# Confidence: 0.96 |
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# ============================= |
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``` |
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## Model Description |
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This model is based on a **DistilBERT** architecture with the following configuration: |
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- **Sequence Length**: 512 tokens |
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- **Number of Layers**: 6 transformer layers |
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- **Number of Attention Heads**: 8 |
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- **Vocabulary Size**: 20,000 (custom Byte Pair Encoding tokenizer) |
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- **Max Position Embeddings**: 512 |
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- **Pad Token ID**: Defined by the custom tokenizer |
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- **Number of Labels**: 3 (for multi-class classification) |
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The tokenizer used for this model is a custom Byte Pair Encoding (BPE) tokenizer trained on the combined training and test datasets. |
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## Tokenizer |
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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. |
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## Training and Evaluation Data |
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- Training Set Size: 43,112 samples |
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- Validation Set Size: 7,608 samples |
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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. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 88 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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## Training Results |
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| Training Loss | Step | Validation Loss | F1 Micro | |
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|:-------------:|:----:|:---------------:|:--------:| |
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| 0.8035 | 500 | 0.5608 | 0.7821 | |
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| 0.4855 | 1000 | 0.4392 | 0.8266 | |
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| 0.3769 | 1500 | 0.3930 | 0.8433 | |
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| 0.3159 | 2000 | 0.3589 | 0.8675 | |
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| 0.279 | 2500 | 0.3552 | 0.8697 | |
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| 0.2463 | 3000 | 0.3812 | 0.8699 | |
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| 0.226 | 3500 | 0.3619 | 0.8690 | |
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| 0.2072 | 4000 | 0.3548 | 0.8754 | |
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| 0.1926 | 4500 | 0.3656 | 0.8763 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
