Text Classification
Transformers
TensorBoard
Safetensors
camembert
Generated from Trainer
text-embeddings-inference
Instructions to use SirawitC/finetuned-WangchanBERTa-TSCC-property-HPTuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SirawitC/finetuned-WangchanBERTa-TSCC-property-HPTuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SirawitC/finetuned-WangchanBERTa-TSCC-property-HPTuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SirawitC/finetuned-WangchanBERTa-TSCC-property-HPTuned") model = AutoModelForSequenceClassification.from_pretrained("SirawitC/finetuned-WangchanBERTa-TSCC-property-HPTuned") - Notebooks
- Google Colab
- Kaggle
finetuned-WangchanBERTa-TSCC-property-HPTuned
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2168
- Accuracy: 0.9451
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 82 | 0.2399 | 0.9390 |
| No log | 2.0 | 164 | 0.5469 | 0.9024 |
| No log | 3.0 | 246 | 0.2480 | 0.9451 |
| No log | 4.0 | 328 | 0.2242 | 0.9451 |
| No log | 5.0 | 410 | 0.2168 | 0.9451 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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