Text Classification
Transformers
Safetensors
English
modernbert
encoder
decision-model
tool-routing
agentic
preview
text-embeddings-inference
Instructions to use MaziyarPanahi/ModernJEV-Decide-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaziyarPanahi/ModernJEV-Decide-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MaziyarPanahi/ModernJEV-Decide-Preview")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/ModernJEV-Decide-Preview") model = AutoModelForSequenceClassification.from_pretrained("MaziyarPanahi/ModernJEV-Decide-Preview", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download example-output.json from MaziyarPanahi/ModernJEV-Decide-Preview: direct link, hf CLI and curl.
- Browser
- Download file 520 Bytes
-
https://huggingface.co/MaziyarPanahi/ModernJEV-Decide-Preview/resolve/main/example-output.json
- Command line
-
hf download hf://MaziyarPanahi/ModernJEV-Decide-Preview/example-output.json
-
curl -L -o example-output.json https://huggingface.co/MaziyarPanahi/ModernJEV-Decide-Preview/resolve/main/example-output.json
520 Bytes
| { | |
| "predicted_label": "lookup_order", | |
| "allowed_choices": [ | |
| "lookup_order", | |
| "search_catalog" | |
| ], | |
| "candidates": [ | |
| { | |
| "label": "lookup_order", | |
| "score": 0.7431679368019104, | |
| "raw_score": -13.1875, | |
| "rank": 1 | |
| }, | |
| { | |
| "label": "search_catalog", | |
| "score": 0.25683197379112244, | |
| "raw_score": -14.25, | |
| "rank": 2 | |
| } | |
| ], | |
| "truncated": false, | |
| "max_sequence_length": 4096, | |
| "note": "Scores rank this supplied choice set; they are not calibrated confidence." | |
| } | |