Text Classification
Transformers
Safetensors
Vietnamese
English
distilbert
text-embeddings-inference
Instructions to use ki4n-4nt/spam_text_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ki4n-4nt/spam_text_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ki4n-4nt/spam_text_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ki4n-4nt/spam_text_classifier") model = AutoModelForSequenceClassification.from_pretrained("ki4n-4nt/spam_text_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| datasets: | |
| - mshenoda/spam-messages | |
| language: | |
| - vi | |
| - en | |
| metrics: | |
| - f1 | |
| - accuracy | |
| base_model: | |
| - distilbert/distilbert-base-multilingual-cased | |
| pipeline_tag: text-classification | |
| library_name: transformers | |