---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: g-bert-NER
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.8925347222222222
    - name: Recall
      type: recall
      value: 0.9102337110481586
    - name: F1
      type: f1
      value: 0.901297335203366
    - name: Accuracy
      type: accuracy
      value: 0.9799720038763864
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# g-bert-NER

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1387
- Precision: 0.8925
- Recall: 0.9102
- F1: 0.9013
- Accuracy: 0.9800

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1773        | 1.0   | 878  | 0.1028          | 0.8910    | 0.8947 | 0.8928 | 0.9781   |
| 0.036         | 2.0   | 1756 | 0.1125          | 0.8901    | 0.9132 | 0.9015 | 0.9793   |
| 0.0194        | 3.0   | 2634 | 0.1202          | 0.8948    | 0.9093 | 0.9020 | 0.9800   |
| 0.0112        | 4.0   | 3512 | 0.1346          | 0.8889    | 0.9136 | 0.9011 | 0.9794   |
| 0.0081        | 5.0   | 4390 | 0.1387          | 0.8925    | 0.9102 | 0.9013 | 0.9800   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1