Text Classification
Transformers
Safetensors
document-scoring
education
norwegian
nordic
🇪🇺 Region: EU
Instructions to use NbAiLab/nb-embed-edu-scorer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-embed-edu-scorer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NbAiLab/nb-embed-edu-scorer")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NbAiLab/nb-embed-edu-scorer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,425 Bytes
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"_sliding_window_pattern": 4,
"architectures": [
"Gemma3TextModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": null,
"bos_token_id": 1,
"cls_token_id": 5,
"dtype": "bfloat16",
"eos_token_id": 2,
"final_logit_softcapping": null,
"head_dim": 64,
"hidden_activation": "silu",
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_types": [
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention"
],
"mask_token_id": 4,
"max_position_embeddings": 8192,
"model_type": "gemma3_text",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"num_key_value_heads": 12,
"pad_token_id": 3,
"prefix_token_ids": [
1
],
"query_pre_attn_scalar": 64,
"rms_norm_eps": 1e-07,
"rope_parameters": {
"full_attention": {
"rope_theta": 100000,
"rope_type": "default"
},
"sliding_attention": {
"rope_theta": 10000,
"rope_type": "default"
}
},
"sliding_window": 1024,
"tie_word_embeddings": true,
"transformers_version": "5.17.0",
"unk_token_id": 0,
"use_bidirectional_attention": true,
"use_cache": false,
"vocab_size": 128000
}
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