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distilbert-base-thai-sentiment

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 ---
 library_name: transformers
-tags: []
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 ---
 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
+## 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