Text Classification
Transformers
Safetensors
English
Korean
modernbert
fill-mask
Eval Results (legacy)
text-embeddings-inference
Instructions to use skt/A.X-Encoder-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skt/A.X-Encoder-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="skt/A.X-Encoder-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("skt/A.X-Encoder-base") model = AutoModelForMaskedLM.from_pretrained("skt/A.X-Encoder-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Set tokenizer "model_max_length" property to 16384 (#1)
Verified signature- Set tokenizer "model_max_length" property to 16384 (c0288a3ea4b5d17d24e819a32cf45c9bbf9da7c3) Co-authored-by: Youngjoon Jang <yjoonjang@users.noreply.huggingface.co>9708f9c
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