Instructions to use creat89/NER_FEDA_Bg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use creat89/NER_FEDA_Bg with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, BERT_model_multidata tokenizer = AutoTokenizer.from_pretrained("creat89/NER_FEDA_Bg") model = BERT_model_multidata.from_pretrained("creat89/NER_FEDA_Bg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 846 Bytes
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license: mit
language:
- multilingual
- bg
- mk
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:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
7. 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, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models 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). |