Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use netaicsco/v1.2_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use netaicsco/v1.2_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="netaicsco/v1.2_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("netaicsco/v1.2_classifier") model = AutoModelForSequenceClassification.from_pretrained("netaicsco/v1.2_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a93e51548fbcc73e93ee4efc4dc09fbe335cba1a90b6b2e8385537278c003bb9
- Size of remote file:
- 268 MB
- SHA256:
- 37a610c4a57bece3b661eb898b758c3fe3719f8667c05b06723acfc060e1f23c
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