---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
  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: 0.9994470908472269
    - name: Recall
      type: recall
      value: 0.9994045846978268
    - name: F1
      type: f1
      value: 0.9994258373205741
    - name: Accuracy
      type: accuracy
      value: 0.9998240191819092
---

<!-- 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 ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0015
- Precision: 0.9994
- Recall: 0.9994
- F1: 0.9994
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1477        | 1.0   | 626  | 0.0548          | 0.9686    | 0.9604 | 0.9644 | 0.9887   |
| 0.0571        | 2.0   | 1252 | 0.0249          | 0.9833    | 0.9820 | 0.9827 | 0.9949   |
| 0.037         | 3.0   | 1878 | 0.0075          | 0.9962    | 0.9953 | 0.9957 | 0.9987   |
| 0.0101        | 4.0   | 2504 | 0.0027          | 0.9987    | 0.9984 | 0.9986 | 0.9996   |
| 0.004         | 5.0   | 3130 | 0.0015          | 0.9994    | 0.9994 | 0.9994 | 0.9998   |


### Framework versions

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