| ### Model information | |
| * language : Korean | |
| * fine tuning data : [klue-tc (a.k.a. YNAT) ](https://klue-benchmark.com/tasks/66/overview/description) | |
| * License : CC-BY-SA 4.0 | |
| * Base model : [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) | |
| * input : news headline | |
| * output : topic | |
| ---- | |
| ### Train information | |
| * train_runtime: 1477.3876 | |
| * train_steps_per_second: 2.416 | |
| * train_loss: 0.3722160959110207 | |
| * epoch: 5.0 | |
| ---- | |
| ### How to use | |
| ``` | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained ( | |
| "seongju/klue-tc-bert-base-multilingual-cased" | |
| ) | |
| model = AutoModelForSequenceClassification.from_pretrained ( | |
| "seongju/klue-tc-bert-base-multilingual-cased" | |
| ) | |
| mapping = {0: 'IT๊ณผํ', 1: '๊ฒฝ์ ', 2: '์ฌํ', | |
| 3: '์ํ๋ฌธํ', 4: '์ธ๊ณ', 5: '์คํฌ์ธ ', 6: '์ ์น'} | |
| inputs = tokenizer( | |
| "๋ฐฑ์ ํํผ ๊ฐ๋ฅ์ฑ? ๋จ๋ฏธ์์ ์๋ก์ด ๋ณ์ด ๋ฐ์ด๋ฌ์ค ๊ธ์ ํ์ฐ ", | |
| padding=True, truncation=True, max_length=128, return_tensors="pt" | |
| ) | |
| outputs = model(**inputs) | |
| probs = outputs[0].softmax(1) | |
| output = mapping[probs.argmax().item()] | |
| ``` |