---
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: 0.9825882454474842
    - name: Recall
      type: recall
      value: 0.9473498086204027
    - name: F1
      type: f1
      value: 0.9646473204829485
    - name: Accuracy
      type: accuracy
      value: 0.9779358957308153
---

<!-- 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: 0.0525
- Precision: 0.9826
- Recall: 0.9473
- F1: 0.9646
- Accuracy: 0.9779

## 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: 5e-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0568        | 1.0   | 875  | 0.0813          | 0.9641    | 0.9244 | 0.9438 | 0.9655   |
| 0.0524        | 2.0   | 1750 | 0.0784          | 0.9619    | 0.9283 | 0.9448 | 0.9660   |
| 0.0481        | 3.0   | 2625 | 0.0719          | 0.9684    | 0.9301 | 0.9489 | 0.9685   |
| 0.0449        | 4.0   | 3500 | 0.0621          | 0.9736    | 0.9428 | 0.9579 | 0.9738   |
| 0.0384        | 5.0   | 4375 | 0.0525          | 0.9826    | 0.9473 | 0.9646 | 0.9779   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3