Text Classification
Transformers
Safetensors
camembert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kew-ae/fls_wangchanberta_model")
model = AutoModelForSequenceClassification.from_pretrained("kew-ae/fls_wangchanberta_model")Quick Links
fls_wangchanberta_model
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the None dataset.
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kew-ae/fls_wangchanberta_model")