Instructions to use bnunticha/wangchanberta-base-att-spm-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bnunticha/wangchanberta-base-att-spm-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bnunticha/wangchanberta-base-att-spm-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bnunticha/wangchanberta-base-att-spm-uncased") model = AutoModelForTokenClassification.from_pretrained("bnunticha/wangchanberta-base-att-spm-uncased") - Notebooks
- Google Colab
- Kaggle
wangchanberta-base-att-spm-uncased
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1488
- Precision: 0.7295
- Recall: 0.6121
- F1: 0.6657
- Accuracy: 0.9399
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1912 | 1.0 | 1379 | 0.1583 | 0.7079 | 0.5883 | 0.6426 | 0.9355 |
| 0.1625 | 2.0 | 2758 | 0.1488 | 0.7236 | 0.6273 | 0.6720 | 0.9399 |
| 0.1532 | 3.0 | 4137 | 0.1488 | 0.7295 | 0.6121 | 0.6657 | 0.9399 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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