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
Upload semantic_token_head_metrics.json with huggingface_hub
Browse files
semantic_token_head_metrics.json
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{
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"schema": "modernbert-semantic-token-head-v1",
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"labels": [
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"O",
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"B_ENTITY",
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"I_ENTITY",
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"B_RELATION",
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"I_RELATION"
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],
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"train_rows": 3288,
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"validation_rows": 1065,
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"best_validation_token_f1": 0.9948555599525128,
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"frozen_backbone": true,
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"elapsed_seconds": 23.438019558787346,
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"history": [
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{
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"train_loss": 0.14505504634122826
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{
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"token_recall": 0.9887888836254539,
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{
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{
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}
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]
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}
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