Instructions to use creat89/NER_FEDA_Cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use creat89/NER_FEDA_Cs with Transformers:
# Load model directly from transformers import AutoTokenizer, BERT_model_multidata tokenizer = AutoTokenizer.from_pretrained("creat89/NER_FEDA_Cs") model = BERT_model_multidata.from_pretrained("creat89/NER_FEDA_Cs") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
language:
- multilingual
- cs
tags:
- labse
- ner
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
- Wikiann (LOC, PER, ORG)
- SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
- CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
- Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).