---
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: 0.9790805727433154
    - name: Recall
      type: recall
      value: 0.9648238440626479
    - name: F1
      type: f1
      value: 0.9718999284886718
    - name: Accuracy
      type: accuracy
      value: 0.985535111315454
---

<!-- 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: 0.0457
- Precision: 0.9791
- Recall: 0.9648
- F1: 0.9719
- Accuracy: 0.9855

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1213        | 0.58  | 500  | 0.0608          | 0.9658    | 0.9626 | 0.9642 | 0.9815   |
| 0.0562        | 1.17  | 1000 | 0.0513          | 0.9746    | 0.9638 | 0.9692 | 0.9841   |
| 0.0514        | 1.75  | 1500 | 0.0484          | 0.9778    | 0.9643 | 0.9710 | 0.9851   |
| 0.0468        | 2.33  | 2000 | 0.0471          | 0.9776    | 0.9653 | 0.9715 | 0.9853   |
| 0.0461        | 2.91  | 2500 | 0.0457          | 0.9791    | 0.9648 | 0.9719 | 0.9855   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1