Instructions to use hcisbmm/vial_fast_tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hcisbmm/vial_fast_tokenizer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hcisbmm/vial_fast_tokenizer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload FAST tokenizer trained on hcisbmm/vial_insertion
Browse files- processor_config.json +3 -3
- tokenizer.json +0 -0
processor_config.json
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{
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"action_dim":
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"auto_map": {
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"AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
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},
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"min_token": -
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"processor_class": "UniversalActionProcessor",
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"scale": 10.0,
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"time_horizon": 10,
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"vocab_size": 1024
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}
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{
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"action_dim": 14,
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"auto_map": {
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"AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
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},
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"min_token": -32,
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"processor_class": "UniversalActionProcessor",
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"scale": 10.0,
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"time_horizon": 10,
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"vocab_size": 1024
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}
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tokenizer.json
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