---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- indian_names
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: indian_names
      type: indian_names
      config: indian_names
      split: train
      args: indian_names
    metrics:
    - name: Precision
      type: precision
      value: 0.980007544322897
    - name: Recall
      type: recall
      value: 0.979145728643216
    - name: F1
      type: f1
      value: 0.9795764469301829
    - name: Accuracy
      type: accuracy
      value: 0.9962591162591162
---

<!-- 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. -->

# my_awesome_wnut_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indian_names dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0148
- Precision: 0.9800
- Recall: 0.9791
- F1: 0.9796
- Accuracy: 0.9963

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 66   | 0.1644          | 0.1903    | 0.1692 | 0.1791 | 0.8817   |
| No log        | 2.0   | 132  | 0.1154          | 0.9760    | 0.8181 | 0.8901 | 0.9703   |
| No log        | 3.0   | 198  | 0.0921          | 0.9755    | 0.9046 | 0.9387 | 0.9795   |
| No log        | 4.0   | 264  | 0.0586          | 0.9616    | 0.9193 | 0.9400 | 0.9849   |
| No log        | 5.0   | 330  | 0.0465          | 0.9588    | 0.9219 | 0.9400 | 0.9861   |
| No log        | 6.0   | 396  | 0.0346          | 0.9359    | 0.9460 | 0.9409 | 0.9902   |
| No log        | 7.0   | 462  | 0.0227          | 0.9708    | 0.9678 | 0.9693 | 0.9941   |
| 0.1068        | 8.0   | 528  | 0.0199          | 0.9753    | 0.9734 | 0.9743 | 0.9946   |
| 0.1068        | 9.0   | 594  | 0.0155          | 0.9801    | 0.9784 | 0.9793 | 0.9961   |
| 0.1068        | 10.0  | 660  | 0.0148          | 0.9800    | 0.9791 | 0.9796 | 0.9963   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3