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
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模型可以在学术或科研基础上免费发布,但不得用于商业用途、真实或模拟车辆/机器人运行,以及其他高风险用途。任何使用者都必须自行阅读并遵守上游许可;本模型不构成自动驾驶安全认证。
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## 运行
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```python
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from structured_command_parser.src.modernbert_parser import ModernBertEnglishIntentParser
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parser = ModernBertEnglishIntentParser(
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"/root/autodl-tmp/models/modernbert-drive-command-base",
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device="cuda",
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)
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parser.warmup()
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document = parser.parse("Slow down and stop before the red truck.")
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```
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实验链路必须在接受请求前调用`warmup()`,并保留规则短路与下游安全校验。
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模型可以在学术或科研基础上免费发布,但不得用于商业用途、真实或模拟车辆/机器人运行,以及其他高风险用途。任何使用者都必须自行阅读并遵守上游许可;本模型不构成自动驾驶安全认证。
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