Text Classification
Transformers
Safetensors
Korean
bert
klue
korean
minwon
complaint
public-administration
text-embeddings-inference
Instructions to use atti433/minde-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atti433/minde-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="atti433/minde-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("atti433/minde-classifier") model = AutoModelForSequenceClassification.from_pretrained("atti433/minde-classifier") - Notebooks
- Google Colab
- Kaggle
File size: 1,451 Bytes
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"add_cross_attention": false,
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
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"dtype": "float32",
"eos_token_id": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "\uad50\ud1b5",
"1": "\uac74\ucd95",
"2": "\ud589\uc815",
"3": "\ubcf4\uac74\uc704\uc0dd",
"4": "\ud658\uacbd",
"5": "\ubb38\ud654_\uc5ec\uac00",
"6": "\ub18d\ucd95\uc0b0",
"7": "\ubcf5\uc9c0",
"8": "\uc138\ubb34",
"9": "\uc0c1\ud558\uc218\ub3c4",
"10": "\uacbd\uc81c"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
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"\uacbd\uc81c": 10,
"\uad50\ud1b5": 0,
"\ub18d\ucd95\uc0b0": 6,
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"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"tie_word_embeddings": true,
"transformers_version": "5.12.1",
"type_vocab_size": 2,
"use_cache": false,
"vocab_size": 32009
}
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