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.

October 15, 2020 - This is a new post in my NER series. I will show you how you can finetune the Bert model to do state-of-the art named entity recognition.

from bert import Ner model = Ner("out_base/") output = model.predict("Steve went to Paris") print(output) ''' [ { "confidence": 0.9981840252876282, "tag": "B-PER", "word": "Steve" }, { "confidence": 0.9998939037322998, "tag": "O", "word": "went" }, { "confidence": 0.999891996383667, "tag": "O", "word": "to" }, { "confidence": 0.9991968274116516, "tag": "B-LOC", "word": "Paris" } ] '''

April 5, 2024 - Fine-tuning BERT for Named Entity Recognition (NER) involves adapting the pre-trained BERT model to the specifics of an NER task. This process allows BERT to leverage its pre-trained contextual understanding for the specialized task of identifying ...

May 14, 2025 - We convert the predictions to a Python list for easier processing. Finally, you reconstruct the entity predictions using a loop. Since BERT’s tokenizer sometimes splits words into subwords (indicated by "##"), you merge them back into complete words. The entity type is determined using the label_list dictionary. Performing Named Entity Recognition (NER) is as simple as shown above.

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March 5, 2025 - In this article, we have implemented BERT for Named Entity Recognition (NER) task.

March 5, 2025 - For the next sentence prediction (NSP) task, two sentences are given in input to BERT, and he has to figure out whether the second sentence follows semantically from the first one. If you think about it, solving the named entity recognition task means classifying each token with a label (person, ...

May 2, 2025 - Find more details on Buy BERT based Named Entity Recognition (NER) fine-tuned model and PyTorch based Python + Flask code.

BERT is designed to pre-train deep bidirectional representations from an unlabeled text by jointly conditioning on both left and right context in all layers.So this Project utilizes the pre-trained BERT model by fine-tuning the parameters and ...

python BERT_NER.py\ --task_name="NER" \ --do_lower_case=False \ --crf=False \ --do_train=True \ --do_eval=True \ --do_predict=True \ --data_dir=data \ --vocab_file=cased_L-12_H-768_A-12/vocab.txt \ --bert_config_file=cased_L-12_H-768_A-12/bert_config.json \ --init_checkpoint=cased_L-12_H-768_A-12/bert_model.ckpt \ --max_seq_length=128 \ --train_batch_size=32 \ --learning_rate=2e-5 \ --num_train_epochs=3.0 \ --output_dir=./output/result_dir perl conlleval.pl -d '\t' < ./output/result_dir/label_test.txt

BERT-NER: named entity recognizer based on BERT and CRF. The goal of this project is creation of a simple Python package with the sklearn-like interface for solution of different named entity recognition tasks in case number of labeled texts ...

This model has BERT as its base architecture, with a token classification head on top, allowing it to make predictions at the token level, rather than the sequence level. Named entity recognition is typically treated as a token classification problem, so that's what we are going to use it for.\n",

from bert import Ner model = Ner("out_base/") output = model.predict("Steve went to Paris") print(output) ''' [ { "confidence": 0.9981840252876282, "tag": "B-PER", "word": "Steve" }, { "confidence": 0.9998939037322998, "tag": "O", "word": "went" }, { "confidence": 0.999891996383667, "tag": "O", "word": "to" }, { "confidence": 0.9991968274116516, "tag": "B-LOC", "word": "Paris" } ] '''

August 18, 2024 - Mastering Named Entity Recognition with BERT: A Comprehensive Guide Introduction In the vast domain of Natural Language Processing (NLP), Named Entity Recognition (NER) stands out as a crucial …

June 8, 2022 - This blog will learn how to Fine-tune a Pre-trained BERT model for the Named Entity Recognition task using HuggingFace Trainer API.

# load BERT model fine-tuned for Named Entity Recognition (NER) ner = pipeline("ner", model="dslim/bert-base-NER")

September 24, 2025 - Fine-tuning BERT for Named Entity Recognition (NER) involves adapting the pre-trained BERT model to the specifics of an NER task. This process allows BERT to leverage its pre-trained contextual understanding for the specialized task of identifying ...

May 20, 2024 - This article is on how to fine-tune BERT for Named Entity Recognition (NER). Specifically, how to train a BERT variation, SpanBERTa, for NER.

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