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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
from transformers import pipeline
Load the model from the Hugging Face Model Hub
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ihgn/similar-questions")
model = AutoModelForSeq2SeqLM.from_pretrained("ihgn/similar-questions")
model = pipeline("text2text-generation", model=model_name)
Configure the generation parameters
generation_config = {
"max_length": 512,
"num_beams": 1,
"top_k": 50,
"top_p": 0.92,
"do_sample": True,
"num_return_sequences": 1
}
Generate text using the configured parameters
input_text= "Your input text goes here."
input_ids = tokenizer.encode(input_text, return_tensors="pt")
generated_ids = model(input_ids, **generation_config)
generated_text = tokenizer.decode(generated_ids.squeeze(), skip_special_tokens=True)
Print the generated text
print(generated_text)
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