---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert-NER
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ner
      type: ner
      config: indian_names
      split: test
      args: indian_names
    metrics:
    - name: Precision
      type: precision
      value: 0.9752319346327347
    - name: Recall
      type: recall
      value: 0.9923783128356141
    - name: F1
      type: f1
      value: 0.9837304142519855
    - name: Accuracy
      type: accuracy
      value: 0.9730393535444438
---

<!-- 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. -->

# Bert-NER

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1205
- Precision: 0.9752
- Recall: 0.9924
- F1: 0.9837
- Accuracy: 0.9730

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0825        | 1.0   | 501  | 0.1031          | 0.9600    | 0.9917 | 0.9756 | 0.9770   |
| 0.0337        | 2.0   | 1002 | 0.1491          | 0.9615    | 0.9942 | 0.9776 | 0.9648   |
| 0.0285        | 3.0   | 1503 | 0.1169          | 0.9754    | 0.9913 | 0.9833 | 0.9723   |
| 0.0249        | 4.0   | 2004 | 0.1054          | 0.9724    | 0.9921 | 0.9821 | 0.9783   |
| 0.0232        | 5.0   | 2505 | 0.1205          | 0.9752    | 0.9924 | 0.9837 | 0.9730   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1