---
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: 0.9860607282009942
    - name: Recall
      type: recall
      value: 0.9693364297742606
    - name: F1
      type: f1
      value: 0.9776270584382788
    - name: Accuracy
      type: accuracy
      value: 0.9882459717748076
---

<!-- 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: 0.0372
- Precision: 0.9861
- Recall: 0.9693
- F1: 0.9776
- Accuracy: 0.9882

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0461        | 1.0   | 858  | 0.0450          | 0.9853    | 0.9602 | 0.9725 | 0.9859   |
| 0.0408        | 2.0   | 1716 | 0.0400          | 0.9836    | 0.9679 | 0.9757 | 0.9873   |
| 0.0391        | 3.0   | 2574 | 0.0372          | 0.9861    | 0.9693 | 0.9776 | 0.9882   |


### Framework versions

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