Text Classification
Transformers
TensorBoard
Safetensors
Thai
camembert
Generated from Trainer
text-embeddings-inference
Instructions to use SandboxBhh/sentiment-thai-text-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SandboxBhh/sentiment-thai-text-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SandboxBhh/sentiment-thai-text-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SandboxBhh/sentiment-thai-text-model") model = AutoModelForSequenceClassification.from_pretrained("SandboxBhh/sentiment-thai-text-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Seed: 42 (for reproducibility)
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Optimizer: Adam (with betas=(0.9, 0.999) and epsilon=1e-08)
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Scheduler: Linear learning rate scheduler
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Number of epochs:
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The training used a combination of cross-entropy loss for multi-class classification and early stopping based on evaluation metrics.
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### Training hyperparameters
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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### Framework versions
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Seed: 42 (for reproducibility)
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Optimizer: Adam (with betas=(0.9, 0.999) and epsilon=1e-08)
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Scheduler: Linear learning rate scheduler
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Number of epochs: 5
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The training used a combination of cross-entropy loss for multi-class classification and early stopping based on evaluation metrics.
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### Training hyperparameters
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Framework versions
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