Text Classification
Transformers
Safetensors
English
modernjev
decision-making
modernbert
classification
noul
scoring
tasksource
jev
typed-decisions
calibration
Instructions to use tasksource/modernbert-tasksource-jev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tasksource/modernbert-tasksource-jev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tasksource/modernbert-tasksource-jev")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tasksource/modernbert-tasksource-jev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# ModernBERT-JEV (`tasksource/modernbert-tasksource-jev`)
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**ModernBERT-JEV** is an open-source, high-throughput foundation model for **typed decisions** built on [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) (149M parameters, 22 layers, bidirectional encoder, native context up to 8,192 tokens) equipped with an **option-query cross-attention decision head**.
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# ModernBERT-JEV (`tasksource/modernbert-tasksource-jev`) (preview, not ready to use)
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**ModernBERT-JEV** is an open-source, high-throughput foundation model for **typed decisions** built on [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) (149M parameters, 22 layers, bidirectional encoder, native context up to 8,192 tokens) equipped with an **option-query cross-attention decision head**.
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