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: 694 Bytes
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"cls_token": {
"content": "[CLS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"mask_token": {
"content": "[MASK]",
"lstrip": true,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "[PAD]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"sep_token": {
"content": "[SEP]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "[UNK]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}
|