---
tags:
- ner
---

# NER NER-finetuning-BERT
This is the BERT-cased model for NER [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) using the CONLL2002 dataset. The results were as follows:

- Precision: 0.8265
- Recall: 0.8443
- F1: 0.8353
- Accuracy: 0.9786


## Model description

Fine-Tuned BERT-cased for Named Entity Recognition (NER)
Overview:
This model is a fine-tuned version of the bert-cased pre-trained model specifically tailored for the task of Named Entity Recognition (NER). BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art transformer-based model designed to understand the context of words in a sentence by considering both the left and right surrounding words. The bert-cased variant ensures that the model distinguishes between uppercase and lowercase letters, preserving the case sensitivity which is crucial for NER tasks.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
 - evaluation_strategy="epoch",
- save_strategy="epoch",
- learning_rate=2e-5,
- num_train_epochs=4,
- per_device_train_batch_size=16,
- weight_decay=0.01,


### Training results


| Epoch | Training Loss | Validation Loss |
|:-------:|:---------------:|:-----------------:|
| 1       | 0.005700        | 0.258581          |
| 2       | 0.004600        | 0.248794          |
| 3       | 0.002800        | 0.257513          |
| 4       | 0.002100        | 0.275097          |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1