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Add evaluation results on the conll2003 config and test split of conll2003
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the conll2003 config and test split of the [conll2003](https://huggingface.co/datasets/conll2003) dataset by @rdecoupes, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-eval-conll2003-conll2003-623e8b-1865063750).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=conll2003).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=conll2003).bf7ca2f
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--- language: en datasets: - conll2003 --- # bert-large-NER ## Model description **bert-large-NER** is a fine-tuned BERT-Large 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-large-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. If you'd like to use a smaller model fine-tuned on the same dataset, a [bert-base-cased](https://huggingface.co/dslim/bert-base-NER) version is also available. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import…de53cbc
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