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This model was fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1. This model is case-insensitive: it does not make a difference between english and English.
MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint `google/mobilebert-uncased` on SQuAD2.0. > Created by Qingqing Cao | GitHub | Twitter
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This repository contains the pretrained discriminator checkpoint of the model **BanglaBERT**. This is an ELECTRA discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLP tasks in bengali. For finetuning on different downstream tasks such as `Sentiment classification`, `Named Entity Recognition`, `Natural Language Inference` etc., refer to the scripts in the official GitHub repository. **Note**: This model was pretrained using a specific normalization pipeline available here. All finetuning scripts in the official GitHub repository uses this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below:
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This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. For finetuning details and scripts, see the paper and the official repository. Language | ROUGE-1 / ROUGE-2 / ROUGE-L ---------|---------------------------- Amharic | 20.0485 / 7.4111 / 18.0753 Arabic | 34.9107 / 14.7937 / 29.1623 Azerbaijani | 21.4227 / 9.5214 / 19.3331 Bengali | 29.5653 / 12.1095 / 25.1315 Burmese | 15.9626 / 5.1477 / 14.1819 Chinese (Simplified) | 39.4071 / 17.7913 / 33.406 Chinese (Traditional) | 37.1866 / 17.1432 / 31.6184 English | 37.601 / 15.1536 / 29.8817 French | 35.3398 / 16.1739 / 28.2041 Gujarati | 21.9619 / 7.7417 / 19.86 Hausa | 39.4375 / 17.6786 / 31.6667 Hindi | 38.5882 / 16.8802 / 32.0132 Igbo | 31.6148 / 10.1605 / 24.5309 Indonesian | 37.0049 / 17.0181 / 30.7561 Japanese | 48.1544 / 23.8482 / 37.3636 Kirundi | 31.9907 / 14.3685 / 25.8305 Korean | 23.6745 / 11.4478 / 22.3619 Kyrgyz | 18.3751 / 7.9608 / 16.5033 Marathi | 22.0141 / 9.5439 / 19.9208 Nepali | 26.6547 / 10.2479 / 24.2847 Oromo | 18.7025 / 6.1694 / 16.1862 Pashto | 38.4743 / 15.5475 / 31.9065 Persian | 36.9425 / 16.1934 / 30.0701 Pidgin | 37.9574 / 15.1234 / 29.872 Portuguese | 37.1676 / 15.9022 / 28.5586 Punjabi | 30.6973 / 12.2058 / 25.515 Russian | 32.2164 / 13.6386 / 26.1689 Scottish Gaelic | 29.0231 / 10.9893 / 22.8814 Serbian (Cyrillic) | 23.7841 / 7.9816 / 20.1379 Serbian (Latin) | 21.6443 / 6.6573 / 18.2336 Sinhala | 27.2901 / 13.3815 / 23.4699 Somali | 31.5563 / 11.5818 / 24.2232 Spanish | 31.5071 / 11.8767 / 24.0746 Swahili | 37.6673 / 17.8534 / 30.9146 Tamil | 24.3326 / 11.0553 / 22.0741 Telugu | 19.8571 / 7.0337 / 17.6101 Thai | 37.3951 / 17.275 / 28.8796 Tigrinya | 25.321 / 8.0157 / 21.1729 Turkish | 32.9304 / 15.5709 / 29.2622 Ukrainian | 23.9908 / 10.1431 / 20.9199 Urdu | 39.5579 / 18.3733 / 32.8442 Uzbek | 16.8281 / 6.3406 / 15.4055 Vietnamese | 32.8826 / 16.2247 / 26.0844 Welsh | 32.6599 / 11.596 / 26.1164 Yoruba | 31.6595 / 11.6599 / 25.0898
Give it a video and a camera offset — azimuth, elevation — and it generates the same scene from that new viewpoint. This is the CrossView-Warp idea ported to **MiniMax-H3**. The model reads two videos: a depth-warp of your clip, which carries the geometry, and the clip itself, which carries identity and appearance. The warp comes from the **CrossViewWarp ComfyUI node**. **Trigger word:** crossview (note: You can also influence the generation by prompting what you want to see in the unseen (magenta) areas.)
**LegalOne** 是一系列专门为中文法律领域训练的LLM，采用**多阶段训练框架**来联合增强法律知识和推理能力。模型基于 **昇腾 Atlas 910B** 计算平台与 **昇思 MindSpore** AI 框架完成训练。 **LegalOne-8B** 在**法规解释、判例法推理、法律问答、文档起草**等任务上超越通用 LLM 和现有法律模型。在 **LexEval、JecQA** 等权威评测中，整体性能可媲美更大参数规模的通用模型（如 **DeepSeek-R1、Qwen3-Max**），并在部分任务上实现超越。尤其在**法律概念理解、法条记忆、多跳推理**等关键任务上，**LegalOne-8B** 达到了当前开源模型的**领先水平**。 基于高质量法律文书，通过智能体 CoT 蒸馏管道，我们合成了涵盖法律咨询、判决预测、法律摘要、法律适用、文书生成等多种经典司法场景的数据。结合部分开源的通用指令遵循数据以保持模型的通用能力，最终获得 **500k** 高质量监督微调数据。
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GGUF conversions of **AutoArk-AI/ARK-ASR-3B**, a 19-language automatic speech recognition model, for use with **CrispASR** (a ggml/llama.cpp-style C++ engine). > ⚠️ **Experimental / work-in-progress.** Core ASR is validated and accurate (see > below), but the backend has known rough edges — read **Limitations** before relying on it. ARK-ASR-3B = **Whisper-large-v3 encoder with partial rotary position embeddings** → **MLP adapter** → **Qwen2.5-3B decoder** with audio-token injection. A single self-contained GGUF holds the encoder, adapter, and language model.
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