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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
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("svjack/T5-dialogue-collect-v5")
model = AutoModelForSeq2SeqLM.from_pretrained("svjack/T5-dialogue-collect-v5")
text = '''
根据下面的上下文进行分段：
上下文 他 喜欢 吃 汉堡 是 但 我 可 不 喜欢。
答案：
'''
tokenizer.decode(
model.generate(
tokenizer.encode(
text, return_tensors="pt", add_special_tokens=True
))[0],
skip_special_tokens = True
)
'''
'分段:他喜欢吃汉堡 分段:是的,但我可不喜欢。'
'''
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