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Alternatives

Products that do what Autofit2 – End-to-end pipeline for multilingual text classification does

Hi HN, Stefan here. autofit2 is a project I have been using at my previous company and is now opensourced. It has been used extensively in automated text moderation, but can be applied to any text/document classification task. We had success modeling offensive texts in 20+ languages (cf. github.com/neospe/dataload for all the datasets). It's an integrated pipeline for lightweight multilingual text classification, covering preprocessing, training, and evaluation. It implements SetFit, a few-shot learning technique that works well for low-data regimes (down to a few dozen…

  1. 1

    Simple and customizable text classification with AI

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    See how a modern neural network completes your text.

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    Text prediction and autocorrect engine

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  5. 5DD

    We recently used DeepSeek V4 Flash as a teacher for finance tasks with GPT-OSS-120B. Distillation works well on this problem. At a constrained 8k token budget, our self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). We released the 20B open weights. With V4 as the teacher though, we realized it would be timely to measure if the censorship characteristic of it transferred to the distilled version of the base model. tl;dr it didn't, the teacher answered politically sensitive questions 7 SDs differently than expected, but the distilled model's…

    Jul 2026 · ctgt.ai

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    Semantic search for your technical documentation & knowledge

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    The most sophisticated AI paraphrasing tool in the industry

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    Humanize any AI text content in seconds for free

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  9. 9AP
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    Rewriting content has never been easier.

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    Stop typing. Start autofilling with Superfill.AI.

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  12. 12I4

    It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)

    Jun 2026 · github.com

  13. 13

    Enhance any text content in seconds for free

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    AI turns documents into multilingual training in 10 minutes

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    A command line translation automation tool for developers

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    AI subtitles & animated captions with faster editing

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  17. 17TN

    Here is a tool I built initially for myself to help with my German and Greek language studies. It started as a hack for creating Anki cards from native language audio. It extracts the words, finds their base forms (lemmas) and groups the examples by the lemma. At some point I realised that I have a transcription with word level timestamps that opens a lot of other opportunities. So I added a mode to click the first and last word in the transcript and it starts looping with the right gap and repeat count. Another feature I use a lot is selecting an audio fragment, sending a predefined prompt…

    Jun 2026 · lingochunk.com

  18. 18C0
  19. 19MM

    Hi HN! We (Thomas and Stéphan, hello!) recently released Model2Vec, a Python library for distilling any sentence transformer into a small set of static embeddings. This makes inference with such a model up to 500x faster, and reduces model size by a factor of 15 (7.5M params or 15/30MB on disk, depending on whether you use float16 or float32). This allows you to embed 50-100k documents per second on a cpu on a macbook. This reduction of course comes at a cost: distilled models are worse than their parent models. Even so, they are actually a lot better than large sets of conventional…

    2024 · github.com

  20. 20MM

    Hi HN! We (Thomas and Stéphan, hello!) recently released Model2Vec, a Python library for distilling any sentence transformer into a small set of static embeddings. This makes inference with such a model up to 500x faster, and reduces model size by a factor of 15 (7.5M params or 15/30MB on disk, depending on whether you use float16 or float32). This reduction of course comes at a cost: distilled models are a lot worse than their parent models. Even so, they are actually a lot better than large sets of conventional static embeddings, such as GLoVe or word2vec-based models, which are many…

    2024 · github.com

  21. 21HI

    Hey HN, the Common Crawl Foundation is trying to expand the coverage of our crawl to more languages, regions and cultures, and if you speak a language other than English (LOTE) you can help! By validating Language Identification data (LangID or LID): https://dynabench.org/tasks/text-language-identification By contributing urls for our seed crawl: https://github.com/commoncrawl/web-languages We're also organizing a Workshop on Multilingual Data Quality Signals (WMDQS) with MLCommons and EleutherAI where we have a call for papers open…

    2025

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    WebFill24

    Automate form filling, emails, and MCQs with AI precision.

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  23. 23PT
  24. 24LB

    Hello everyone. I built an AI-based toolset to help me with language learning. I wanted to be able to easily generate very specific study content and get rapid feedback on my writing. Unlike most language apps, it doesn’t actually try to teach you a language. Instead, it’s a collection of tools for people at an intermediate level who already have a learning process It’s particularly great for Anki users. There a demo video on the login page, and I set up anonymous auth for people who want to test it without creating an account. Feedback and bug reports welcome.

    2025 · drillapp.xyz

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