| 6 | | * [https://huggingface.co/witiko/ahisto-ner-model-l Model L]: the largest and most accurate of the AHISTO NER models |
| 7 | | * [https://huggingface.co/witiko/ahisto-ner-model-s Model S]: a smaller and more efficient variant of [https://huggingface.co/witiko/ahisto-ner-model-l Model L], used in the [https://gitlab.fi.muni.cz/nlp/ahisto-modules/ner AHISTO named entity recognition tool] |
| 8 | | * [https://huggingface.co/witiko/ahisto-ner-model-tds1 Model TDS1] and [https://huggingface.co/witiko/ahisto-ner-model-tds2 Model TDS2]: two smaller models trained on data of different size as a part of our ablation study |
| 9 | | * [https://huggingface.co/witiko/ahisto-ner-model-ci Model CI]: a smaller model trained with a loss function that does not address class imbalance as a part of our ablation study |
| | 6 | * [https://huggingface.co/MU-NLPC/ahisto-ner-model-l Model L]: the largest and most accurate of the AHISTO NER models |
| | 7 | * [https://huggingface.co/MU-NLPC/ahisto-ner-model-s Model S]: a smaller and more efficient variant of [https://huggingface.co/MU-NLPC/ahisto-ner-model-l Model L], used in the [https://gitlab.fi.muni.cz/nlp/ahisto-modules/ner AHISTO named entity recognition tool] |
| | 8 | * [https://huggingface.co/MU-NLPC/ahisto-ner-model-tds1 Model TDS1] and [https://huggingface.co/MU-NLPC/ahisto-ner-model-tds2 Model TDS2]: two smaller models trained on data of different size as a part of our ablation study |
| | 9 | * [https://huggingface.co/MU-NLPC/ahisto-ner-model-ci Model CI]: a smaller model trained with a loss function that does not address class imbalance as a part of our ablation study |
