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
Upload ModernBertForSequenceClassification
Browse files- config.json +9 -1
config.json
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{
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-
"_name_or_path": "/
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"architectures": [
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"ModernBertForSequenceClassification"
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],
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"gradient_checkpointing": false,
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"hidden_activation": "gelu",
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"hidden_size": 1024,
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"initializer_cutoff_factor": 2.0,
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"initializer_range": 0.02,
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"intermediate_size": 2624,
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"layer_norm_eps": 1e-05,
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"local_attention": 128,
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"local_rope_theta": 10000.0,
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{
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"_name_or_path": "TrustHLT/ModernBERT-large-Czech-Legal-binary-argument",
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"architectures": [
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"ModernBertForSequenceClassification"
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],
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"gradient_checkpointing": false,
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"hidden_activation": "gelu",
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"hidden_size": 1024,
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"id2label": {
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"0": "non-argumentative",
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"1": "argumentative"
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},
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"initializer_cutoff_factor": 2.0,
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"initializer_range": 0.02,
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"intermediate_size": 2624,
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"label2id": {
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"argumentative": 1,
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"non-argumentative": 0
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},
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"layer_norm_eps": 1e-05,
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"local_attention": 128,
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"local_rope_theta": 10000.0,
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