Text Classification
Transformers
Safetensors
English
deberta-v2
feature-extraction
typed-decisions
calibrated
decision-model
open-jev
deberta-v3
text-embeddings-inference
Instructions to use com-kotobalabs/open-jev-deberta-v3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use com-kotobalabs/open-jev-deberta-v3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="com-kotobalabs/open-jev-deberta-v3-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("com-kotobalabs/open-jev-deberta-v3-large") model = AutoModel.from_pretrained("com-kotobalabs/open-jev-deberta-v3-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
性能实测(3 问/state,RTX 4060 Laptop):单条 69 ms;8 条 136 ms;32 条 527 ms(≈61 states/s)。开 bf16 后 32 条降到 207 ms(≈155 states/s),概率漂移 <0.3%。
#2
by hyzx86 - opened
