---
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: 0.9941348973607038
    - name: Recall
      type: recall
      value: 0.9921951219512195
    - name: F1
      type: f1
      value: 0.9931640625
    - name: Accuracy
      type: accuracy
      value: 0.9998052125131481
---

<!-- 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: 0.0012
- Precision: 0.9941
- Recall: 0.9922
- F1: 0.9932
- Accuracy: 0.9998

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 63   | 0.1413          | 0.0       | 0.0    | 0.0    | 0.9745   |
| No log        | 2.0   | 126  | 0.1211          | 0.0       | 0.0    | 0.0    | 0.9745   |
| No log        | 3.0   | 189  | 0.0656          | 0.6231    | 0.2790 | 0.3854 | 0.9811   |
| No log        | 4.0   | 252  | 0.0380          | 0.7297    | 0.6059 | 0.6620 | 0.9894   |
| No log        | 5.0   | 315  | 0.0259          | 0.8341    | 0.7259 | 0.7762 | 0.9931   |
| No log        | 6.0   | 378  | 0.0136          | 0.8842    | 0.8712 | 0.8776 | 0.9963   |
| No log        | 7.0   | 441  | 0.0076          | 0.9286    | 0.9268 | 0.9277 | 0.9981   |
| 0.0748        | 8.0   | 504  | 0.0054          | 0.9409    | 0.9473 | 0.9441 | 0.9985   |
| 0.0748        | 9.0   | 567  | 0.0042          | 0.9520    | 0.9678 | 0.9598 | 0.9991   |
| 0.0748        | 10.0  | 630  | 0.0025          | 0.9738    | 0.9795 | 0.9767 | 0.9995   |
| 0.0748        | 11.0  | 693  | 0.0019          | 0.9863    | 0.9863 | 0.9863 | 0.9997   |
| 0.0748        | 12.0  | 756  | 0.0015          | 0.9961    | 0.9912 | 0.9936 | 0.9998   |
| 0.0748        | 13.0  | 819  | 0.0014          | 0.9912    | 0.9912 | 0.9912 | 0.9998   |
| 0.0748        | 14.0  | 882  | 0.0013          | 0.9912    | 0.9912 | 0.9912 | 0.9998   |
| 0.0748        | 15.0  | 945  | 0.0012          | 0.9941    | 0.9922 | 0.9932 | 0.9998   |


### Framework versions

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