Model Card
Example Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained('CCB/abstracts_to_tweet_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('CCB/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 this paper, we present a novel method for Natural Language Processing (NLP) based on the introduction of deep learning techniques adapted to linguistics. We demonstrate that by integrating syntactic and semantic analysis in pre-processing stages, superior text understanding can be facilitated. Initial processes involve tokenization, POS-tagging, syntactic-semantic hinging for all corpus. To further the learning precision, we introduce a framework powered by a hybrid of Transformer and Recurrent Neural Networks architectures that manifest in increased efficiency both theoretically and empirically. This paper shares exhaustive results, detailing improvements in feature engineering, promising a reduction in human-size semantic labor. We additionally propose that integrating deep learning methods with traditional linguistics dramatically improves contextual understanding and performance on tasks such as language translation, sentiment analysis, and automated thesaurus generation. The innovations reported here make significant strides towards realizing viable, sophisticated machine-level NLP systems. Additionally, the research represents groundwork for further exploration and development promising higher degrees of culture-language contextuality and robustness integral in future NLP 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.
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