transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:conll2003 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- conll2003
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-ner
  results: []
---

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# bert-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0814


## 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.

#  How to Use
You can use this model with Transformers pipeline for NER.

    from transformers import AutoTokenizer, AutoModelForTokenClassification
    from transformers import pipeline
    
    tokenizer = AutoTokenizer.from_pretrained("Hatman/bert-finetuned-ner")
    model = AutoModelForTokenClassification.from_pretrained("Hatman/bert-finetuned-ner")
    
    nlp = pipeline("ner", model=model, tokenizer=tokenizer)
    example = "My name is Wolfgang and I live in Berlin"
    
    ner_results = nlp(example)
    print(ner_results)


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0181        | 1.0   | 1756 | 0.1301          |
| 0.0166        | 2.0   | 3512 | 0.0762          |
| 0.0064        | 3.0   | 5268 | 0.0814          |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
