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glotlid

cis-lmu

**GlotLID** is a Fasttext language identification (LID) model that supports more than **2000 labels**. **Latest:** GlotLID is now updated to **V3**. V3 supports **2102 labels** (three-letter ISO codes with script). For more details on the supported languages and performance, as well as significant changes from previous versions, please refer to https://github.com/cisnlp/GlotLID/blob/main/languages-v3.md. - **Demo:** huggingface - **Repository:** github - **Paper:** paper (EMNLP 2023) - **Point of Contact:** amir@cis.lmu.de

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This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

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This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

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This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). - Mu Yang at CKIP (Author & Maintainer) For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.

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This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

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This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). - Mu Yang at CKIP (Author & Maintainer) For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.

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ruri-v3-310m

cl-nagoya

**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-310m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737

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FastPDN

clarin-pl

FastPolDeepNer is model for Named Entity Recognition, designed for easy use, training and configuration. The forerunner of this project is PolDeepNer2. The model implements a pipeline consisting of data processing and training using: hydra, pytorch, pytorch-lightning, transformers. The FastPDN model was trained on datasets (with 82 class versions) of kpwr and cen. Annotation guidelines are specified here. FastPDN models have been fine-tuned, thanks to pretrained models: - herbert-base-case - distiluse-base-multilingual-cased-v1

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News topic classification model based on `xlm-roberta-large` and fine-tuned on a news corpus in 4 languages (Croatian, Slovenian, Catalan and Greek), annotated with the top-level IPTC Media Topic NewsCodes labels. The development and evaluation of the model is described in the paper LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in IPTC News Topic Classification (Kuzman and Ljubešić, 2025). The model can be used for classification into topic labels from the IPTC NewsCodes schema and can be applied to any news text in a language, supported by the `xlm-roberta-large`. Based on a manually-annotated test set (in Croatian, Slovenian, Catalan and Greek), the model achieves macro-F1 score of 0.746, micro-F1 score of 0.734, and accuracy of 0.734, and outperforms the GPT-4o model (version `gpt-4o-2024-05-13`) used in a zero-shot setting. If we use only labels that are predicted with a confidence score equal or higher than 0.90, the model achieves micro-F1 and macro-F1 of 0.80.

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