Ramsrigouthamg
Rank:Average Model Cost: $0.0000
Number of Runs: 257,458
Models by this creator
t5_sentence_paraphraser
t5_sentence_paraphraser
The t5_sentence_paraphraser model is a text-to-text generation model that generates paraphrases of input sentences. It is trained to take a given sentence and generate alternative phrasings or restructuring of the same sentence. The model is designed to provide semantic equivalents of the input sentence, while ensuring grammatical correctness and preserving the overall meaning.
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233.3K
Huggingface
t5_paraphraser
t5_paraphraser
The t5_paraphraser model is a text-to-text generation model that specializes in paraphrasing text. It takes an input text and generates a paraphrased version of that text as output. This model can be useful for tasks that require generating alternative versions of a given text, such as data augmentation or text simplification.
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9.7K
Huggingface
t5-large-paraphraser-diverse-high-quality
t5-large-paraphraser-diverse-high-quality
The T5-Large Paraphraser model is a variant of the T5 transformer model that has been fine-tuned for the task of paraphrasing sentences. It is designed to take an input sentence and generate multiple diverse and high-quality paraphrases of that sentence. This can be useful for various natural language processing tasks such as data augmentation, text generation, and text simplification. The model is based on the T5 architecture, which is a powerful transformer-based model that has been pre-trained on a large corpus of text data. It uses a sequence-to-sequence framework with a encoder-decoder structure, where the encoder processes the input sentence and the decoder generates the paraphrases. The model is fine-tuned using a large dataset of sentence pairs that are labeled with paraphrases. It achieves state-of-the-art performance on several benchmark datasets for paraphrase generation.
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5.3K
Huggingface
t5_boolean_questions
t5_boolean_questions
T5 is a text-to-text transfer transformer model that can perform various tasks such as text classification, translation, summarization, and question-answering. It is trained in a multitask manner with a large amount of diverse data and achieves state-of-the-art performance on several benchmark datasets. The model follows the transformer architecture and leverages encoder-decoder attention mechanisms to generate high-quality text outputs.
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5.0K
Huggingface
t5_squad_v1
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4.1K
Huggingface
t5_squad
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28
Huggingface
BERT_WSD
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0
Huggingface