
# KB-BERT for NER

## Cased data

This model is based on [KB-BERT](https://huggingface.co/KB/bert-base-swedish-cased) and was fine-tuned on the [SUCX 3.0 - NER](https://huggingface.co/datasets/KBLab/sucx3_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
{"language": "sv", "tags": ["token-classification", "sequence-tagger-model", "bert"], "datasets": ["KBLab/sucx3_ner"], "widget": [{"text": "Emil bor i L\u00f6nneberga"}]}
transformers pytorch megatron-bert token-classification sequence-tagger-model bert sv dataset:KBLab/sucx3_ner autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #megatron-bert #token-classification #sequence-tagger-model #bert #sv #dataset-KBLab/sucx3_ner #autotrain_compatible #endpoints_compatible #region-us 


# 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: URL
# 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: URL
TAGS
#transformers #pytorch #megatron-bert #token-classification #sequence-tagger-model #bert #sv #dataset-KBLab/sucx3_ner #autotrain_compatible #endpoints_compatible #region-us 
 # 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: URL
passage: TAGS
#transformers #pytorch #megatron-bert #token-classification #sequence-tagger-model #bert #sv #dataset-KBLab/sucx3_ner #autotrain_compatible #endpoints_compatible #region-us 
# 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: URL