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--- |
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base_model: google/t5-v1_1-base |
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tags: |
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- datadreamer |
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- datadreamer-0.28.0 |
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- synthetic |
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- gpt-4 |
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- gpt-4 |
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- text2text-generation |
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widget: |
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- text: "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." |
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example_title: "Example 1" |
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- text: "This paper proposes a novel approach to improve performance in Natural Language Processing (NLP) tasks by harnessing the potential of deep learning algorithms using multilingual transformer models. Our work investigates the challenging problem of understanding and manipulating sentences to Toucan dialogue language in context-dependent situations. We present a comprehensive analysis of established NLP approaches, novel methods based on transformer models, and thorough experiments that demonstrate substantial advancements over the state-of-art performances. Our primary contribution lies in the intelligent integration of thematic role labeling with multilingual models to improve the comprehension of sentence structure; for instance, recognizing grammatical relations irrespective of a word\u2019s syntactic position or morphological form. In addition, our method progresses automatic predicate argument structure analysis, giving significance and having potential applications in tasks such as information extraction, summarization, and machine translation. We provide task-specific models that reveal the comparative strength of our architecture set over a cross-lingual task. Systematic evaluations conducted on several linguistic databases have demonstrated robust effectiveness in extracting and reconstructing meaningful entities from unstructured language data. The empirical results show notable enhancements in NLP task competence and thus stimulate further research avenues for substantial developments in multimodal natural language understanding and endow opportunities for practical applications." |
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example_title: "Example 2" |
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- text: "In recent years, natural language processing (NLP) has seen impressive advancements because of the advent of deep learning technologies, like transformer-based models such as BERT., However, there remain significant challenges in obtaining human-level understanding, notably concerning effectively extracting semantics from context, deeper discourse analysis and anticipatory prediction during discourse development. In this research paper, we propose a novel integrative NLP model named Contextualized Anticipatory Semantic Humor Analysis (CASHA), which creates a sophisticated blend of semantic context understanding, discourse reference instantiation, and humorous setting anticipation. Inspired by human cognitive processing, CASHA layers a sentence-level semantic extractor and a transformer-based discourse modelling layer harboring informal semantics to understand intricate discourse embeddings accurately. It subsequently employs an adaptive humor anticipation layer based logically on previous discourse understanding. For rigorous model evaluation, we performed several experiments across diverse data sets encompassing assorted types of humor. Results demonstrate significantly improved performance in both humor detection and humor semantics understanding. They prompt profound thinking about NLP applications regarding human-level understanding of semantics from context. This work represents a potentially influential step in advancing the transformative urban initiatives prioritized by smart cities-examples abound about interfaces for ordinary citizens to interact more creatively with city experiences and for cities authorities to react empathetically to citizen-specific humor, metaphors, and cultural dialects." |
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example_title: "Example 3" |
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pipeline_tag: text2text-generation |
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--- |
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# Model Card |
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[Add more information here](https://huggingface.co/templates/model-card-example) |
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## Example Usage |
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```python3 |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline |
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tokenizer = AutoTokenizer.from_pretrained('CCB/abstracts_to_tweet_model', revision=None) # Load tokenizer |
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model = AutoModelForSeq2SeqLM.from_pretrained('CCB/abstracts_to_tweet_model', revision=None) # Load model |
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pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id) |
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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.'] |
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print(pipe(inputs, max_length=512, do_sample=False)) |
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``` |
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--- |
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This model was trained with a synthetic dataset with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card and model card can be found [here](datadreamer.json). The training arguments can be found [here](training_args.json). |
