Results for “natural-language-processing”

Process text with NLP. Use when tokenizing, analyzing sentiment, extracting entities, summarizing documents, or measuring similarity.

NLVR2: A Visual Reasoning Benchmark for Natural Language

Humanize text: strip AI-isms and add real voice.

当个人感到迷茫，不确定自己真正想要什么，或发现当前追求无法带来深层满足时，调用此模型来探索和澄清内在真实欲望

Language-specific super-code guidelines for elixir.

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Humanize text: strip AI-isms and add real voice.

Deduplicating Training Data Makes Language Models Better

LoRA: Low-Rank Adaptation of Large Language Models

去除或避免文本中的 AI 味。用于用户强制调用本 Skill，或写作/改写时提到“不要有 AI 味”“不要太假”“真情实感”“有人感”，或对已有文本/上一轮输出反馈“太像 AI”“太空泛”“太模板”“太官方”“不像我说的”等场景。

Generates high-fidelity images using reasoning-driven prompts and structured creative briefs via muapi.ai.

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

用自然语言描述生成目标音频。把一段场景描述（人声对话、环境声、音效、背景音乐等复合音频）一次性生成成音频。当用户描述一个声音场景、要求生成/合成/制作一段音频或声音、给出形如"角色：台词"的对话脚本要转成音频、或要按参考音频的音色说话时使用。支持两种模式：纯文本描述生成（T2A）和带参考音频生成（A2A，在描述中引用参考音频指定角色音色）

LLaVA-NeXT: Improved Reasoning, OCR, and World Knowledge

Train and use SentencePiece tokenizers for multilingual NLP, supporting BPE and Unigram algorithms with raw Unicode text.

Harness Router

Translate text faithfully into a target language while preserving meaning, register, and intent.

Sample Text Processor

GLIP: Grounded Language-Image Pre-training

DCLM: DataComp for Language Models

VILA: On Pre-training for Visual Language Models

SynthText: Synthetic Data for Text Detection

上下文压缩省 Token。对话变长或开新任务时输出/使用 6 块压缩结构，总长 400~800 tokens。必加载。