bert-base-NER EU AI Act Compliance Profile

bert-base-NER

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Model description

bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).

Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset.

If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available.

Available NER models

| Model Name | Description | Parameters | |-------------------|-------------|------------------| | distilbert-NER (NEW!) | Fine-tuned DistilBERT - a smaller, faster, lighter version of BERT | 66M | | bert-large-NER | Fine-tuned bert-large-cased - larger model with slightly better performance | 340M | | bert-base-NER-(uncased) | Fine-tuned bert-base, available in both cased and uncased versions | 110M |

Intended uses & limitations

How to use

You can use this model with Transformers pipeline for NER.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeli

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