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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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: | |
| 1. Wikiann (LOC, PER, ORG) | |
| 2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO) | |
| 3. CNEC (LOC, ORG, MEDIA, ART, PER, TIME) | |
| 4. 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). |