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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 |