Text Classification
Transformers
ONNX
Safetensors
GLiClass
rlcd
typesafe-ai
jev
decision-engine
system-1
modernbert
gliclass
non-autoregressive
zero-token-generation
structured-outputs
calibration
expected-calibration-error
ece
brier-score
proper-scoring-rules
webgpu
edge-ai
fast-inference
banking77
Instructions to use heman10x/rlcd-modernbert-151m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heman10x/rlcd-modernbert-151m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="heman10x/rlcd-modernbert-151m")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("heman10x/rlcd-modernbert-151m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer_config.json with huggingface_hub
Browse files- tokenizer_config.json +17 -0
tokenizer_config.json
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{
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"is_local": false,
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"local_files_only": false,
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"mask_token": "[MASK]",
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 8192,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "[UNK]"
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}
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