---
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: 0.7241379310344828
    - name: Recall
      type: recall
      value: 0.7170731707317073
    - name: F1
      type: f1
      value: 0.7205882352941175
    - name: Accuracy
      type: accuracy
      value: 0.9920721492851299
---

<!-- 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: 0.0278
- Precision: 0.7241
- Recall: 0.7171
- F1: 0.7206
- Accuracy: 0.9921

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 63   | 0.1197          | 0.0       | 0.0    | 0.0    | 0.9745   |
| No log        | 2.0   | 126  | 0.0818          | 0.8438    | 0.0263 | 0.0511 | 0.9751   |
| No log        | 3.0   | 189  | 0.0470          | 0.5270    | 0.4859 | 0.5056 | 0.9857   |
| No log        | 4.0   | 252  | 0.0315          | 0.7042    | 0.6341 | 0.6674 | 0.9906   |
| No log        | 5.0   | 315  | 0.0278          | 0.7241    | 0.7171 | 0.7206 | 0.9921   |


### Framework versions

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