# Named Entity Recognition

PrunaAI's compressed version of the UniNER-7B-all model, significantly reducing memory usage and energy consumption through quantization techniques while maintaining good named entity recognition capabilities.

Korean large language model based on beomi/llama-2-ko-7b, suitable for Q&A tasks

A named entity recognition model fine-tuned based on electra-small, capable of identifying three types of entities: locations, person names, and organizations

State-of-the-art multi-task joint parsing BERT model for Modern Hebrew, supporting five tasks: prefix segmentation, morphological disambiguation, lexical analysis, syntactic parsing, and named entity recognition

A named entity recognition model fine-tuned on the CoNLL2003 dataset based on BERT-base-cased

A BERT model pre-trained for Brazilian Portuguese, excelling in various NLP tasks

AfroLM is a pretrained language model optimized for 23 African languages, employing an active learning framework to achieve high performance with minimal data

A fine-tuned version of the RoBERTa-base model on the MNLI dataset, suitable for natural language inference tasks

BERT-base-cased fine-tuned Named Entity Recognition (NER) model

This model is a lightweight version based on DistilBERT, fine-tuned for Named Entity Recognition (NER) tasks on a toy dataset.

A language model specifically designed for astronomy and astrophysics, developed by the NASA/ADS team, supporting masked token filling, named entity recognition, and text classification tasks.

A lightweight named entity recognition model based on DistilBERT, fine-tuned on a specific dataset

This model is a fine-tuned version of bert-base-cased on an unknown dataset, primarily used for Named Entity Recognition (NER) tasks.

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset

Swedish BERT base model released by the National Library of Sweden/KBLab, trained on multi-source texts

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset

This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset, designed for token classification tasks.

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset, featuring efficient inference performance and high accuracy.

Named Entity Recognition (NER) model fine-tuned based on DistilBERT-base-uncased

This model is a lightweight version based on DistilBERT, fine-tuned on the conll2003 dataset for named entity recognition tasks.

This is a lightweight model based on DistilBERT, fine-tuned on the CoNLL-2003 Named Entity Recognition (NER) task.

CPU-optimized Finnish language processing pipeline with NLP features including token classification and dependency parsing

Korean processing pipeline optimized for CPU, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, etc.

This model is a fine-tuned version based on hfl/chinese-bert-wwm-ext on the conll2003 dataset, designed for token classification tasks.

A lightweight Named Entity Recognition (NER) model based on the DistilBERT architecture, fine-tuned for specific tasks

A named entity recognition model fine-tuned on the CoNLL2003 dataset based on bert-base-cased

This model is a Named Entity Recognition (NER) model fine-tuned based on the DistilBERT base version, trained on an unknown dataset with an evaluation set F1 score of 0.8545.

This model is a named entity recognition (NER) model fine-tuned on the wnut_17 dataset based on distilbert-base-uncased, used to identify specific entity categories in text.

This model is a Named Entity Recognition (NER) model fine-tuned on the few_nerd dataset based on distilbert-base-uncased, achieving an F1 score of 0.7621 on the evaluation set.

AfroXLMR-mini is created by adapting the XLM-R-miniLM model through masked language model (MLM) training on 17 African languages, covering major African language families and three high-resource languages (Arabic, French, and English).

A compact variant of XLM-R optimized for 17 African languages, enhanced through vocabulary reduction and multilingual adaptive training

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset

A named entity recognition model fine-tuned on the conll2003 dataset based on the DistilBERT-base-uncased model, excelling in NER tasks.

A Ukrainian named entity recognition model fine-tuned based on XLM-RoBERTa-Uk, capable of identifying person names, locations, and organization names in text.

Named Entity Recognition model fine-tuned based on MobileBERT architecture

BERT-base based named entity recognition model, capable of identifying four types of entities including locations, organizations, and person names

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset, suitable for entity tagging tasks in English text.

This model is a fine-tuned version of distilbert-base-uncased on a specific dataset, primarily designed for Named Entity Recognition (NER) tasks.

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset

Pretrained BERT model for Brazilian Portuguese, achieving state-of-the-art performance in multiple NLP tasks