Text Classification
Transformers
Safetensors
Czech
modernbert
legal
czech
legal-nlp
text-embeddings-inference
Instructions to use TrustHLT/ModernBERT-large-madon-arg-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TrustHLT/ModernBERT-large-madon-arg-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TrustHLT/ModernBERT-large-madon-arg-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TrustHLT/ModernBERT-large-madon-arg-detection") model = AutoModelForSequenceClassification.from_pretrained("TrustHLT/ModernBERT-large-madon-arg-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,572 Bytes
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"added_tokens_decoder": {
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"lstrip": false,
"normalized": false,
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"single_word": false,
"special": true
},
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"special": true
},
"2": {
"content": "[UNK]",
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"special": true
},
"3": {
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},
"5": {
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},
"6": {
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"lstrip": true,
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"special": true
}
},
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"extra_special_tokens": {},
"mask_token": "[MASK]",
"max_length": 3000,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 8192,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "PreTrainedTokenizer",
"unk_token": "[UNK]"
}
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