---
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: test
      args: indian_names
    metrics:
    - name: Precision
      type: precision
      value: 0.7254647322919372
    - name: Recall
      type: recall
      value: 0.8467001558981465
    - name: F1
      type: f1
      value: 0.7814079891293488
    - name: Accuracy
      type: accuracy
      value: 0.8557099199430039
---

<!-- 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: 0.9081
- Precision: 0.7255
- Recall: 0.8467
- F1: 0.7814
- Accuracy: 0.8557

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0934        | 1.0   | 501  | 0.6938          | 0.7190    | 0.8536 | 0.7805 | 0.8502   |
| 0.035         | 2.0   | 1002 | 0.7709          | 0.7087    | 0.8383 | 0.7681 | 0.8446   |
| 0.0196        | 3.0   | 1503 | 0.7814          | 0.7130    | 0.8439 | 0.7729 | 0.8477   |
| 0.0109        | 4.0   | 2004 | 0.8572          | 0.7206    | 0.8467 | 0.7786 | 0.8526   |
| 0.0065        | 5.0   | 2505 | 0.9081          | 0.7255    | 0.8467 | 0.7814 | 0.8557   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3