Dev tools · alternatives · 2026
24 alternatives to Autofit2 – End-to-end pipeline for multilingual text classification
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…
Autofit2 is an open-source pipeline for multilingual text classification that handles preprocessing, training, and evaluation end-to-end. It uses SetFit, a few-shot learning technique that works effectively with… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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A robust Python tool for text-based AI training and generation using GPT-2. - minimaxir/aitextgen
2020 · github.com · its alternatives →
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Lingoedit 2.0▲445Simplify translation and editing: your AI language companion
2024 · its alternatives →
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New AI classifier for indicating AI-written text
2023 · openai.com · its alternatives →
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GPT-4▲1,160More accurate, less cringe, smoother & useful daily chats!
2023 · openai.com · its alternatives →
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Hi HN! I'm excited to share Autolabel, an open-source Python library to label and enrich text datasets with any Large Language Model (LLM) of your choice. We built Autolabel because access to clean, labeled data is a huge bottleneck for most ML/data science teams. The most capable LLMs are able to label data with high accuracy, and at a fraction of the cost and time compared to manual labeling. With Autolabel, you can leverage LLMs to label any text dataset with <5 lines of code. We’re eager for your feedback!
2023 · github.com · its alternatives →
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aiq lets you use a single CLI command to auto-label text data with a language model API & train an efficient classifier on the labels. Training and inference are super fast (both run on CPU).
2024 · github.com · its alternatives →
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Simple and customizable text classification with AI
2019 · monkeylearn.com · its alternatives →
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TLDR: I’ve made a transformer model and a wrapper library that segments text into meaningful semantic chunks. The current text splitting approaches rely on heuristics (although one can use neural embedder to group semantically related sentences). I propose a fully neural approach to semantic chunking. I took the base distilbert model and trained it on a bookcorpus to split concatenated text paragraphs into original paragraphs. Basically it’s a token classification task. Model fine-tuning took day and a half on a 2x1080ti. The library could be used as a text splitter module in a RAG system or…
2025 · github.com · its alternatives →
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Hi HN, I’ve been working on an OCR pipeline specifically optimized for machine learning dataset preparation. It’s designed to process complex academic materials — including math formulas, tables, figures, and multilingual text — and output clean, structured formats like JSON and Markdown. Some features: • Multi-stage OCR combining DocLayout-YOLO, Google Vision, MathPix, and Gemini Pro Vision • Extracts and understands diagrams, tables, LaTeX-style math, and multilingual text (Japanese/Korean/English) • Highly tuned for ML training pipelines, including dataset generation and…
2025 · github.com · its alternatives →
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TLDR: I’m expanding the family of text-splitting Chonky models with new multilingual model. You can learn more about this neural approach in a previous post: https://news.ycombinator.com/item?id=43652968 Since the release of the first distilbert-based model I’ve released two more models based on a ModernBERT. All these models were pre-trained and fine-tuned primary on English texts. But recently mmBERT(https://huggingface.co/blog/mmbert) has been released. This model pre-trained on massive dataset that contains 1833 languages. So I had an idea of…
Oct 2025 · huggingface.co · its alternatives →
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See how a modern neural network completes your text.
2019 · inferkit.com · its alternatives →
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YourGPT 2.0▲215Today we are launching YourGPT 2.0, an update focused on making support, sales, and operational workflows work together more smoothly in one system. This release improves how workflows are built, how the platform connects to external tools, and how context is maintained across long, multi-channel interactions. AI Studio now makes it much easier to create and refine workflows. You can describe the process you want in natural language and the system generates the full workflow for you. It can also help debug issues by showing what happened at each step and explaining where things went off…
Nov 2025 · yourgpt.ai · its alternatives →
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We recently shipped the Tiptap AI Toolkit (https://tiptap.dev/docs/content-ai/capabilities/ai-toolkit/o...), which lets developers embed LLM-powered editing into rich text editors. Now we’re exploring the next step: making agents operate on documents without a browser involved. The idea is a server-side system where agents can read, write, and transform documents in real time — essentially treating your document layer as a programmable data store that agents can interact with directly. Internally we’ve been joking about the name “real-time agentic document…
2025 · tiptap.dev · its alternatives →
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Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available. I've found it particularly useful for - Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly - Understanding different tokenization patterns and how it affects model output - Getting a general sense of how "hard" different prediction tasks are for GPT-style models Known problems (that I probably…
2023 · perplexity.vercel.app · its alternatives →
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I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license:…
2025 · nature.com · its alternatives →
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text classification - no machine learning knowledge needed - Windsooon/cherry
2019 · github.com · its alternatives →
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Rank a list of documents according to their relevance to a query primarily and your custom instructions secondarily. We evaluated the model on instructions for recency, document type, source, and metadata, and it can generalize to other instructions as well. The reranker supports multilinguality. Th
2025 · docs.contextual.ai · its alternatives →
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Add Google-style smart compose to your software
2021 · typewise.app · its alternatives →
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We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
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💥 Fast State-of-the-Art Tokenizers optimized for Research and Production - huggingface/tokenizers
2020 · github.com · its alternatives →
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We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…
2024 · github.com · its alternatives →
- 24IB
Hello Hacker News, When learning foreign languages, I made the most progress by speaking them throughout the day, every day. So I made a site where you can *speak* to an AI language teacher to practice both listening and speaking. # The product *What I have now:* * Multilingual speech recognition: You can ask a question in English and get an answer in your target language. * Feedback on your grammar. * Suggestions: See examples of what to say next to keep the conversation flowing. * Speed: Choose a lower speed for beginners or a faster one for advanced levels. * Translations: Click to see a…
2023 · gliglish.com · its alternatives →
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