Feature Extraction
Transformers
Safetensors
English
modernbert
autonomous-driving
structured-output
compositional-semantics
research-only
text-embeddings-inference
Instructions to use UNIC0RN-Zhu/modernbert-drive-command-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UNIC0RN-Zhu/modernbert-drive-command-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="UNIC0RN-Zhu/modernbert-drive-command-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UNIC0RN-Zhu/modernbert-drive-command-base") model = AutoModel.from_pretrained("UNIC0RN-Zhu/modernbert-drive-command-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 772 Bytes
01b8532 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"schema": "modernbert-driving-command-test-v1",
"model": "/root/autodl-tmp/models/modernbert-drive-command-epoch2",
"dataset": "/root/autodl-tmp/LMM-in-AutoDrive/structured_command_parser/data/processed/english_pseudolabels/test.jsonl",
"metrics": {
"samples": 66270,
"action_exact_match": 0.9870076957899502,
"action_micro_precision": 0.9854056026269915,
"action_micro_recall": 0.9703780590043515,
"action_micro_f1": 0.9778340976747929,
"status_accuracy": 0.9939489965293496,
"category_accuracy": 0.9915044514863437,
"urgency_accuracy": 0.9991398822996831,
"direction_exact_match": 0.997540365172778,
"change_accuracy": 0.9982194054625019
},
"metric_scope": "PSEUDO_LABEL_TEACHER_AGREEMENT_NOT_HUMAN_GOLD_ACCURACY"
}
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