from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained('dansul/datadreamer-dev-abstracts_to_tweet_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('dansul/datadreamer-dev-abstracts_to_tweet_model', revision=None) # Load model
pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id)

inputs = ["In the ever-growing field of Natural Language Processing (NLP), understanding the nuances and depth of human expression and delivering contextualized outputs is an essential yet challenging task. The contribution of Deep Learning and Machine Learning methods toward tackling complex language processing tasks necessitates ongoing research. This paper outlines a novel architecture accounting for semantic bridges in the realm of NLP, utilizing sophisticated RNN and LSTM models. We connect phrase-level and sentence-level semantics under a unified framework, contributing towards generating better contextual understanding of textual data and providing detailed insights for tasks such as sentiment analysis and topic modeling. Our architecture outperforms most known models in these tasks due to its ability to consider longer textual context while simultaneously avoiding complications arising from language ambiguity. Our results provide inspiring indications on the benefits of capturing semantic bridges for more robust language models. We carry rigorous evaluations impinging both qualitative and quantitative insights, thereby showcasing our model's impressive generalizability to real-world applications."]
print(pipe(inputs, max_length=512, do_sample=False))

This model was trained with a synthetic dataset with DataDreamer 🤖💤. The synthetic dataset card and model card can be found here. The training arguments can be found here.