---
license: apache-2.0
base_model: distilbert-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: train
      args: indian_names
    metrics:
    - name: Precision
      type: precision
      value: 0.9896954662296407
    - name: Recall
      type: recall
      value: 0.9704150478224023
    - name: F1
      type: f1
      value: 0.9799604321344418
    - name: Accuracy
      type: accuracy
      value: 0.9894401834309103
---

<!-- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0320
- Precision: 0.9897
- Recall: 0.9704
- F1: 0.9800
- Accuracy: 0.9894

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0503        | 0.58  | 500  | 0.0506          | 0.9744    | 0.9656 | 0.9700 | 0.9846   |
| 0.0461        | 1.17  | 1000 | 0.0450          | 0.9781    | 0.9657 | 0.9719 | 0.9856   |
| 0.0428        | 1.75  | 1500 | 0.0424          | 0.9804    | 0.9677 | 0.9740 | 0.9864   |
| 0.0379        | 2.33  | 2000 | 0.0375          | 0.9839    | 0.9704 | 0.9771 | 0.9880   |
| 0.0352        | 2.91  | 2500 | 0.0320          | 0.9897    | 0.9704 | 0.9800 | 0.9894   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1