Text Classification
Transformers
Safetensors
Korean
electra
korean
multi-label-classification
kcelectra
fine-tuned
Eval Results (legacy)
Instructions to use Kaaeun/Labeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaaeun/Labeling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kaaeun/Labeling")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kaaeun/Labeling") model = AutoModelForSequenceClassification.from_pretrained("Kaaeun/Labeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 531 Bytes
cb0794f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"id2label": {
"0": "label_0",
"1": "label_1",
"2": "label_2",
"3": "label_3",
"4": "label_4"
},
"label2id": {
"label_0": 0,
"label_1": 1,
"label_2": 2,
"label_3": 3,
"label_4": 4
},
"thresholds": [
0.25,
0.675,
0.8,
0.7500000000000002,
0.7000000000000002
],
"pos_weight": [
2.600910470409712,
1.898615635179153,
5.294429708222812,
4.484591679506933,
1.7307249712313004
],
"max_length": 256,
"model_name": "beomi/KcELECTRA-base"
} |