Instructions to use KBLab/bert-base-swedish-cased-reallysimple-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/bert-base-swedish-cased-reallysimple-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KBLab/bert-base-swedish-cased-reallysimple-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KBLab/bert-base-swedish-cased-reallysimple-ner") model = AutoModelForTokenClassification.from_pretrained("KBLab/bert-base-swedish-cased-reallysimple-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
KB-BERT for NER
Cased data
This model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the simple tags and cased data. For this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and insides (I) of named entity tags.
The model was trained on the training data only, with the best model chosen by its performance on the validation data. You find more information about the model and the performance on our blog: https://kb-labb.github.io/posts/2022-02-07-sucx3_ner
Dataset source
We have used a processed and remixed version of SUC 3.0 to train this model. See [SUCX 3.0][https://huggingface.co/datasets/KBLab/sucx3_ner].
Original dataset citation: Språkbanken (2024). SUC 3.0 (updated: 2024-06-03). [Data set]. Enriched and distributed by Språkbanken. https://doi.org/10.23695/wy84-ar30
Model license
MIT License
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