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### 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()]
``` |