sat-3l-sm

Model · Token classification

gliner25_agents_v3

Rafael Macalaba

Fine-tune of fastino/gliner2.5-base-v1 (GLiNER2, boundary architecture) for data-use mention extraction (dataset / survey / census / registry mentions in economics research papers). Labels were produced with a locally run LLM through the passage-annotation harness, with human-in-the-loop passage review and adjudication under the project doctrine. Holdout F1 is therefore agreement with the annotating agent, not owner accuracy. - NAMEDDATA — a proper name, title, or acronym of a specific data source - DESCRIPTIVEDATA — a source described in words but not named - VAGUEDATA — generic data wording with no identifiable source 2 gold mention(s) in the holdout sit inside a run the word splitter…

Model · Token classification

LondonLB

J

LondonLB is a fine-tuned version of microsoft/deberta-v3-base that identifies personal names in Reginald R. Sharpe's Calendar of Letter-Books of the City of London, volumes A to I (c. 1275–1422). It was built to support research on medieval London naming practices, in particular the transition from by-names to hereditary family names, by making it possible to extract every named individual from roughly 14,800 calendar entries. On a held-out gold-standard set of 200 entries containing 1,136 names, the model reaches an exact-match F1 of 0.977, and finds 1,135 of the 1,136 names at least partially. The model is intended for researchers working with Sharpe's calendar who want to locate personal…

Model · Token classification

llmlingua-2-bert-base-multilingual-cased-meetingbank

Microsoft

This model was introduced in the paper LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (Pan et al, 2024). It is a BERT multilingual base model (cased) finetuned to perform token classification for task agnostic prompt compression. The probability $p{preserve}$ of each token $xi$ is used as the metric for compression. This model is trained on the extractive text compression dataset constructed with the methodology proposed in the LLMLingua-2, using training examples from MeetingBank (Hu et al, 2023) as the seed data. You can evaluate the model on downstream tasks such as question answering (QA) and summarization over compressed meeting transcripts…

Model · Token classification

wikineural-multilingual-ner

Babelscape

This is the model card for the EMNLP 2021 paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. We fine-tuned a multilingual language model (mBERT) for 3 epochs on our WikiNEuRal dataset for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. If you use the model, please reference this work in your paper: The original repository for the paper can be found at https://github.com/Babelscape/wikineural. You can use this model with Transformers pipeline for NER. This model is trained on WikiNEuRal, a…

Model · Token classification

bert-base-multilingual-cased-ner-hrl

David Adelani

Hugging Face's logo - multilingual bert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers pipeline for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may…

Model · Token classification

OpenMed-NER-PharmaDetect-BigMed-278M

OpenMed

Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - chemical entities from the bc5cdr dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research…