---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: IgboBERT-NER-finetuned-Final-Version
  results: []
---

<!-- 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. -->

# IgboBERT-NER-finetuned-Final-Version

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2420
- Precision: 0.8000
- Recall: 0.8188
- F1: 0.8093
- Accuracy: 0.9751

## 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: 0.0001
- 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1164        | 1.0   | 1234  | 0.1290          | 0.6049    | 0.7572 | 0.6726 | 0.9612   |
| 0.0637        | 2.0   | 2468  | 0.1195          | 0.7329    | 0.7507 | 0.7417 | 0.9693   |
| 0.0364        | 3.0   | 3702  | 0.1288          | 0.6905    | 0.7683 | 0.7274 | 0.9698   |
| 0.0219        | 4.0   | 4936  | 0.1475          | 0.7554    | 0.7848 | 0.7699 | 0.9730   |
| 0.0172        | 5.0   | 6170  | 0.1592          | 0.7596    | 0.7977 | 0.7782 | 0.9743   |
| 0.0115        | 6.0   | 7404  | 0.1980          | 0.7488    | 0.7493 | 0.7491 | 0.9697   |
| 0.0097        | 7.0   | 8638  | 0.1754          | 0.7728    | 0.7758 | 0.7743 | 0.9725   |
| 0.0069        | 8.0   | 9872  | 0.1872          | 0.7473    | 0.7862 | 0.7663 | 0.9717   |
| 0.0058        | 9.0   | 11106 | 0.1870          | 0.7797    | 0.8079 | 0.7936 | 0.9744   |
| 0.0039        | 10.0  | 12340 | 0.2041          | 0.7741    | 0.7937 | 0.7837 | 0.9735   |
| 0.0039        | 11.0  | 13574 | 0.2263          | 0.7836    | 0.7964 | 0.7899 | 0.9741   |
| 0.0021        | 12.0  | 14808 | 0.2201          | 0.7867    | 0.7769 | 0.7818 | 0.9720   |
| 0.002         | 13.0  | 16042 | 0.2060          | 0.7842    | 0.7957 | 0.7899 | 0.9738   |
| 0.0015        | 14.0  | 17276 | 0.2047          | 0.7862    | 0.8204 | 0.8029 | 0.9750   |
| 0.001         | 15.0  | 18510 | 0.2270          | 0.7732    | 0.8068 | 0.7896 | 0.9743   |
| 0.0007        | 16.0  | 19744 | 0.2402          | 0.7983    | 0.8122 | 0.8052 | 0.9745   |
| 0.0008        | 17.0  | 20978 | 0.2235          | 0.7874    | 0.8244 | 0.8055 | 0.9751   |
| 0.0005        | 18.0  | 22212 | 0.2303          | 0.7956    | 0.8181 | 0.8067 | 0.9750   |
| 0.0001        | 19.0  | 23446 | 0.2424          | 0.8012    | 0.8133 | 0.8072 | 0.9751   |
| 0.0003        | 20.0  | 24680 | 0.2420          | 0.8000    | 0.8188 | 0.8093 | 0.9751   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1