---
license: apache-2.0
base_model: distilbert-base-cased
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: 0.9779481031086752
    - name: Recall
      type: recall
      value: 0.950199700449326
    - name: F1
      type: f1
      value: 0.96387423507069
    - name: Accuracy
      type: accuracy
      value: 0.977337411889879
---

<!-- 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-cased](https://huggingface.co./distilbert-base-cased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0518
- Precision: 0.9779
- Recall: 0.9502
- F1: 0.9639
- Accuracy: 0.9773

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 438  | 0.0725          | 0.9691    | 0.9325 | 0.9505 | 0.9693   |
| 0.0435        | 2.0   | 876  | 0.0635          | 0.9687    | 0.9392 | 0.9537 | 0.9711   |
| 0.039         | 3.0   | 1314 | 0.0569          | 0.9790    | 0.9416 | 0.9599 | 0.9751   |
| 0.0392        | 4.0   | 1752 | 0.0542          | 0.9744    | 0.9490 | 0.9615 | 0.9758   |
| 0.0378        | 5.0   | 2190 | 0.0518          | 0.9779    | 0.9502 | 0.9639 | 0.9773   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0