Text Classification
Transformers
Safetensors
English
modernbert
memory
darkmem
text-embeddings-inference
Instructions to use darkraise/darkmem-classifier-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darkraise/darkmem-classifier-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="darkraise/darkmem-classifier-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("darkraise/darkmem-classifier-v1") model = AutoModelForSequenceClassification.from_pretrained("darkraise/darkmem-classifier-v1") - Notebooks
- Google Colab
- Kaggle
File size: 2,409 Bytes
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"architectures": [
"ModernBertForSequenceClassification"
],
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"classifier_activation": "gelu",
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"classifier_pooling": "mean",
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"decoder_bias": true,
"deterministic_flash_attn": false,
"dtype": "float32",
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"eos_token_id": null,
"global_attn_every_n_layers": 3,
"gradient_checkpointing": false,
"hidden_activation": "gelu",
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"id2label": {
"0": "fact",
"1": "decision",
"2": "preference",
"3": "problem",
"4": "reference",
"5": "architecture",
"6": "milestone"
},
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"reference": 4
},
"layer_norm_eps": 1e-05,
"layer_types": [
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"sliding_attention",
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"full_attention",
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"sliding_attention",
"full_attention",
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],
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"model_type": "modernbert",
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"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
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},
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"rope_type": "default"
}
},
"sep_token_id": 50282,
"sparse_pred_ignore_index": -100,
"sparse_prediction": false,
"tie_word_embeddings": true,
"transformers_version": "5.1.0",
"use_cache": false,
"vocab_size": 50368
}
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