**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider supporting my research to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕ This is the initial release of our artificial intelligence model on Hugging Face. It is important to note that this version is just the beginning; the model will be constantly improved over time. **Currently, the model boasts an accuracy of 79%, but we plan to increase this regularly through monthly updates.** We are proud to announce that our model is currently the only one in Italy capable of identifying a wide range of **52** different categories. This capability distinctly sets it apart from other models available in the Italian landscape, offering an unprecedented level of versatility and breadth in entity recognition.
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**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider supporting my research to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕ Welcome to the second generation of our state-of-the-art Named Entity Recognition model for Italian text. Building on the success of our previous version, Italian_NER_XXL_v2 delivers significantly enhanced performance with an **accuracy of 87.5%** and **F1 score of 89.2%** - an improvement of over 8 percentage points from my previous model. Italian_NER_XXL_v2 remains the only model in Italy capable of identifying a comprehensive range of **52** different entity categories, maintaining our unique position in the Italian NLP landscape. This unparalleled breadth of entity recognition makes our model the premier choice for privacy, legal, and financial applications.
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**USER2** is a new generation of the **U**niversal **S**entence **E**ncoder for **R**ussian, designed for sentence representation with long-context support of up to 8,192 tokens. The models are built on top of the `RuModernBERT` encoders and are fine-tuned for retrieval and semantic tasks. They also support Matryoshka Representation Learning (MRL) — a technique that enables reducing embedding size with minimal loss in representation quality. To evaluate the model, we measure quality on the `MTEB-rus` benchmark. Additionally, to measure long-context retrieval, we run Russian subset of MultiLongDocRetrieval (MLDR) task.
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Type a word or a sentence and get the emoji that fits. Emo is tuned for to-dos, calendar entries, notes, and message drafts across **22 languages** (including CJK, Arabic, Thai, Hindi, and more). The model and tokenizer together are small (**5MB** on Apple via Core ML, **11MB** via LiteRT elsewhere) and runs in well under 2ms on device. > `"Dentist appointment"` → 🦷 · `"réserver un vol pour Tokyo"` → ✈️ · `"犬の散歩"` → 🐕 · `"จองโรงแรม"` → 🏨 - **Live demo:** desert-ant-labs/emo-demo: type a phrase, get emojis, fully in your browser. **Swift** (requirements)
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Strip names, addresses, emails, phone numbers, cards, IBANs and national IDs out of text before it leaves the device, across **27 languages** (see Languages). A token classifier plus a deterministic layer for structured IDs. The deployable model is **11.6MB** (4-bit Core ML on Apple) or **24.5MB** (int8 LiteRT `.tflite` on Android, Linux and the web). > `"Call Anna Kovács at anna@example.hu, IBAN GB29NWBK60161331926819"` → > `"Call [GIVEN_NAME] [SURNAME] at [EMAIL], IBAN [BANK_ACCOUNT]"` `GIVEN_NAME`, `SURNAME`, `STREET_NAME`, `BUILDING_NUMBER`, `SECONDARY_ADDRESS`, `CITY`, `STATE`, `ZIP_CODE`, `EMAIL`, `PHONE`, `CREDIT_CARD`, `BANK_ACCOUNT`, `ROUTING_NUMBER`, `IP_ADDRESS`, `URL`, `GOVERNMENT_ID`, `PASSPORT`, `DRIVERS_LICENSE`, `TAX_ID`, `SSN`.
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This AI-generated text detection model, developed by Desklib, is designed to classify English text as either human-written or AI-generated. It currently leads the RAID Benchmark for AI Detection. This model is a fine-tuned version of **microsoft/deberta-v3-large**, leveraging a transformer-based architecture to achieve high accuracy. It is robust and handles various adversarial attacks across different domains remarkably well. This model is particularly useful for applications in content moderation, academic integrity, journalism, and anywhere the authenticity of text is paramount. **Desklib** provides AI-based tools for personalized learning and study help. This model is one of the many tools offered by Desklib for students, educators and universities. **Try the model online!**: Desklib AI Detector
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