Text Classification
Transformers
Safetensors
exaone4
text-generation
exaone
lora
finetune
korean
tagger
Instructions to use FloatDo/exaone-4.0-1.2b-float-right-tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FloatDo/exaone-4.0-1.2b-float-right-tagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FloatDo/exaone-4.0-1.2b-float-right-tagger")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("FloatDo/exaone-4.0-1.2b-float-right-tagger") model = AutoModelForCausalLM.from_pretrained("FloatDo/exaone-4.0-1.2b-float-right-tagger") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7931457242ecbc5f20c3061a504c4c60fc7a27b02147af5907b97ebae68ef749
- Size of remote file:
- 2.56 GB
- SHA256:
- 87288afa2a34830a2fceed835549c64f7036e64e60d504e110d1b058c8a8c7a3
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