Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. For more details on the library and usage please refer to the github page **Huggingface** lists 12 paraphrase models, **RapidAPI** lists 7 fremium and commercial paraphrasers like QuillBot, Rasa has discussed an experimental paraphraser for augmenting text data here, Sentence-transfomers offers a paraphrase mining utility and NLPAug offers word level augmentation with a PPDB (a multi-million paraphrase database). While these attempts at paraphrasing are great, there are still some gaps and paraphrasing is NOT yet a mainstream option for text augmentation in building NLU models....Parrot is a humble attempt to fill some of these gaps. **What is a good paraphrase?** Almost all conditioned text generation models are validated on 2 factors, (1) if the generated text conveys the same meaning as the original context (Adequacy) (2) if the text is fluent / grammatically correct english (Fluency). For instance Neural Machine Translation outputs are tested for Adequacy and Fluency. But a good paraphrase should be adequate and fluent while being as different as possible on the surface lexical form. With respect to this definition, the **3 key metrics** that measures the quality of paraphrases are: - **Adequacy** (Is the meaning preserved adequately?) - **Fluency** (Is the paraphrase fluent English?) - **Diversity (Lexical / Phrasal / Syntactical)** (How much has the paraphrase changed the original sentence?)

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文本生成开放权重

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Splade_PP_en_v1

prithivida

This work stands on the shoulders of 2 robust researches: Naver's From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective paper and Google's SparseEmbed. Props to both the teams for such a robust work. **Beginner ?** expand this. **Expert in Sparse & Dense representations ?** feel free skip to next section 2, Lexical search with BOW based sparse vectors are strong baselines, but they famously suffer from vocabulary mismatch problem, as they can only do exact term matching. Here are the pros and cons:

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向量与相似度

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Splade_PP_en_v2

prithivida

译文排队中 · 当前显示来源原文

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题

API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

找到适合你的 AI 模型。

探索 AI 模型 7

bert-for-patents-64d

grammar_error_correcter_v1

parrot_adequacy_model

parrot_fluency_model

parrot_paraphraser_on_T5

Splade_PP_en_v1

Splade_PP_en_v2

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。