Text Classification
Transformers
Safetensors
camembert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Tippawan/wisesight_sentiment")
model = AutoModelForSequenceClassification.from_pretrained("Tippawan/wisesight_sentiment")Quick Links
wisesight_sentiment
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1412
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3594
- num_epochs: 10
Training results
Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tippawan/wisesight_sentiment")