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

paust/pko-t5-base model based

Since this model is based on paust/pko-t5-base tokenizer, you need to import it.

tokenizer = T5TokenizerFast.from_pretrained("paust/pko-t5-base")
model = T5ForConditionalGeneration.from_pretrained("emotionanalysis/diaryempathizer-t5-ko")

Test code

from transformers import T5TokenizerFast, T5ForConditionalGeneration

model = T5ForConditionalGeneration.from_pretrained("emotionanalysis/diaryempathizer-t5-ko")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

tokenizer = T5TokenizerFast.from_pretrained("paust/pko-t5-base")
input_text = """
  오늘은 정말로 즐거운 날이었다. 며칠째 잠도 못 자고 알고리즘 수업을 기대 중이다. 이제 정말로 더는 버티기 어렵겠다고 보인다. 낮에 교수님이 갑자기 열정적으로 수업을 하시는 것이었다. 그러다가 automata에
  대해 설명하시는데, 정말 감동의 눈물과 경탄을 금할 길 없었고, 이 수업을 듣기 위해 태어났다는 생각이 들었으며, 과제(포상)까지 주시는 것이었다.
  나는 황홀경에 빠졌다. 목숨을 바치고 젊을을 내던져야 할 인생의 이유를 찾았기 때문이다.
"""
  
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True, padding="max_length")
inputs = {key: value.to(device) for key, value in inputs.items()}
outputs = model.generate(input_ids=inputs["input_ids"], max_length=128, num_beams=4, early_stopping=True)

generated_comment = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_comment)

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Safetensors

Model size

0.3B params

Tensor type

F32

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