**la_core_web_md**: Latin NLP pipeline for spaCy. Trained on harmonized Universal Dependencies treebanks (Perseus, PROIEL, ITTB, LLCT, UDante, CIRCSE) with LASLA data. Includes POS tagging, morphological analysis, lemmatization, dependency parsing, named entity recognition, and sentence segmentation. « Catus » — from "De cato seu fele" in Conrad Gesner's Historiae animalium liber primus de quadrupedibus viviparis (1551), p. 318 This is a maintenance release across the LatinCy pipeline family (`la_core_web_sm`/`md`/`lg`/`trf`). Model weights are **unchanged** from v3.9.x — no retraining. It also carries the 3.9.7 fix, which was built but never published. Evaluation metrics are not re-measured: the current test set (treebanks 3.10.13) differs from the one these weights were scored on, so the `meta.json` figures remain the v3.9 measurement.
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View details ↗This model is a fine-tuned version of monologg/koelectra-small-v3-discriminator on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1431 - Precision: 0.8232 - Recall: 0.8449 - F1: 0.8339 - Accuracy: 0.9628 태깅 시스템 : BIO 시스템 - B-(begin) : 개체명이 시작할 때 - I-(inside) : 토큰이 개체명 중간에 있을 때 - O(outside) : 토큰이 개체명이 아닐 경우 개체명 인식(NER) 모델 학습 데이터 셋 - 문화체육관광부 > 국립국어원 > 모두의 말뭉치 > 개체명 분석 말뭉치 2021 - https://corpus.korean.go.kr/request/reausetMain.do
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View details ↗- Website: https://injecguard.github.io/ - Paper: https://aclanthology.org/2025.acl-long.1468.pdf - Code Repo: https://github.com/leolee99/PIGuard Due to some licensing issues, the model name has been changed from **InjecGuard** to **PIGuard**. We apologize for any inconvenience this may have caused. Prompt injection attacks pose a critical threat to large language models (LLMs), enabling goal hijacking and data leakage. Prompt guard models, though effective in defense, suffer from over-defense—falsely flagging benign inputs as malicious due to trigger word bias. To address this issue, we introduce ***NotInject***, an evaluation dataset that systematically measures over-defense across various prompt guard models. NotInject contains 339 benign samples enriched with trigger words common in prompt injection attacks, enabling fine-grained evaluation. Our results show that state-of-the-art models suffer from over-defense issues, with accuracy dropping close to random guessing levels (60\%). To mitigate this, we propose ***PIGuard***, a novel prompt guard model that incorporates a new training strategy, *Mitigating Over-defense for Free* (MOF), which significantly reduces the bias on trigger words. InjecGuard demonstrates state-of-the-art performance on diverse benchmarks including NotInject, surpassing the existing best model by 30.8\%, offering a robust and open-source solution for detecting prompt injection attacks.
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View details ↗This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased It achieves the following results on the evaluation set: - Loss: 0.1140 - Precision: 0.9147 - Recall: 0.9483 - F1: 0.9312 - Accuracy: 0.9700 This model was fine-tunned on token classification task (NER) on Portuguese archival documents. The annotated labels are: Date, Profession, Person, Place, Organization All the training and evaluation data is available at: http://ner.epl.di.uminho.pt/
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