YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

FLAN T5

Source Code

FLAN T5는 paust/pko-t5-large 모델을 기반으로 다양한 태스크를 instruction finetuning을 통해서 만든 모델입니다.

현재 계속 Instruction Finetuning 을 진행하면서 중간결과를 모델로 업데이트하고 있습니다.

학습된 태스크

Task name	Task type
NSMC	Classification
Klue Ynat	Classification
KorNLI	Classification
KorSTS	Classification
QuestionPair	Classification
Klue STS	Classification
AIHub news Summary	Summarization
AIHub document Summary	Summarization
AIHub book Summary	Summarization
AIHub conversation Summary	Summarization
AIHub ko-to-en	Translation
AIHub ko-to-en Expert	Translation
AIHub ko-to-en Tech	Translation
AIHub ko-to-en social	Translation
AIHub ko-to-jp	Translation
AIHub ko-to-cn Tech	Translation
AIHub Translation Corpus	Translation
korquad	QA
Klue MRC	QA
AIHub mindslab's MRC	QA

모델

Hugginface 링크

사용 예시

from transformers import T5ForConditionalGeneration, T5TokenizerFast

tokenizer = T5TokenizerFast.from_pretrained('paust/pko-flan-t5-large')
model = T5ForConditionalGeneration.from_pretrained('paust/pko-flan-t5-large', device_map='cuda')

prompt = """서울특별시(서울特別市, 영어: Seoul Metropolitan Government)는 대한민국 수도이자 최대 도시이다. 선사시대부터 사람이 거주하였으나 본 역사는 백제 첫 수도 위례성을 시초로 한다. 삼국시대에는 전략적 요충지로서 고구려, 백제, 신라가 번갈아 차지하였으며, 고려 시대에는 왕실의 별궁이 세워진 남경(南京)으로 이름하였다.
한국의 수도는 어디입니까?"""
input_ids = tokenizer(prompt, add_special_tokens=True, return_tensors='pt').input_ids
output_ids = model.generate(input_ids=input_ids.cuda(), max_new_tokens=32, num_beams=12)
text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
print(text)  # 서울특별시

License

PAUST에서 만든 pko-t5는 MIT license 하에 공개되어 있습니다.

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Safetensors

Model size

0.8B params

Tensor type

BF16