
# t5-large-korean-text-summary


이 모델은 lcw99 / t5-large-korean-text-summary을 klue-ynat으로 훈련시켜 만든 모델입니다.<br>
Input = ['IT과학','경제','사회','생활문화','세계','스포츠','정치']<br>
OUTPUT = 각 label에 맞는 뉴스 기사 제목을 생성합니다.<br>
배치단위로 추론하고싶다면 batch_encode_plus를 사용하시면 됩니다.<br>
git : https://github.com/taemin6697<br>
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_dir = "kfkas/t5-large-korean-news-title-klue-ynat"
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
model.to(device)

label_list = ['IT과학','경제','사회','생활문화','세계','스포츠','정치']
text = "IT과학"

input_ids = tokenizer.encode(text,return_tensors="pt").to(device)
with torch.no_grad():
  output = model.generate(
    input_ids,
    do_sample=True, #샘플링 전략 사용
    max_length=128, # 최대 디코딩 길이는 50
    top_k=50, # 확률 순위가 50위 밖인 토큰은 샘플링에서 제외
    top_p=0.95, # 누적 확률이 95%인 후보집합에서만 생성
)
decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
print(decoded_output)#SK텔레콤 스마트 모바일 요금제 시즌1 출시


```



## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: None
- training_precision: float16
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
{"language": ["ko"], "tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5-large-korean-news-title-klue-ynat", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_keras_callback ko autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_keras_callback #ko #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# t5-large-korean-text-summary


이 모델은 lcw99 / t5-large-korean-text-summary을 klue-ynat으로 훈련시켜 만든 모델입니다.<br>
Input = ['IT과학','경제','사회','생활문화','세계','스포츠','정치']<br>
OUTPUT = 각 label에 맞는 뉴스 기사 제목을 생성합니다.<br>
배치단위로 추론하고싶다면 batch_encode_plus를 사용하시면 됩니다.<br>
git : URL
## Usage




## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: None
- training_precision: float16
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
# t5-large-korean-text-summary


이 모델은 lcw99 / t5-large-korean-text-summary을 klue-ynat으로 훈련시켜 만든 모델입니다.<br>
Input = ['IT과학','경제','사회','생활문화','세계','스포츠','정치']<br>
OUTPUT = 각 label에 맞는 뉴스 기사 제목을 생성합니다.<br>
배치단위로 추론하고싶다면 batch_encode_plus를 사용하시면 됩니다.<br>
git : URL ## Usage ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: None
- training_precision: float16 ### Training results ### Framework versions
- Transformers 4.22.1
- TensorFlow 2.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_keras_callback #ko #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5-large-korean-text-summary


이 모델은 lcw99 / t5-large-korean-text-summary을 klue-ynat으로 훈련시켜 만든 모델입니다.<br>
Input = ['IT과학','경제','사회','생활문화','세계','스포츠','정치']<br>
OUTPUT = 각 label에 맞는 뉴스 기사 제목을 생성합니다.<br>
배치단위로 추론하고싶다면 batch_encode_plus를 사용하시면 됩니다.<br>
git : URL ## Usage ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: None
- training_precision: float16 ### Training results ### Framework versions
- Transformers 4.22.1
- TensorFlow 2.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1