---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: test
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.558252427184466
    - name: Recall
      type: recall
      value: 0.4263206672845227
    - name: F1
      type: f1
      value: 0.48344718864950076
    - name: Accuracy
      type: accuracy
      value: 0.9477576845795391
---

<!-- 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/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4207
- Precision: 0.5583
- Recall: 0.4263
- F1: 0.4834
- Accuracy: 0.9478

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 213  | 0.3267          | 0.5351    | 0.4235 | 0.4728 | 0.9472   |
| No log        | 2.0   | 426  | 0.3741          | 0.4730    | 0.3818 | 0.4226 | 0.9428   |
| 0.0126        | 3.0   | 639  | 0.3431          | 0.5336    | 0.4189 | 0.4694 | 0.9466   |
| 0.0126        | 4.0   | 852  | 0.3790          | 0.5983    | 0.3920 | 0.4737 | 0.9477   |
| 0.008         | 5.0   | 1065 | 0.3610          | 0.5289    | 0.4328 | 0.4760 | 0.9472   |
| 0.008         | 6.0   | 1278 | 0.3580          | 0.5637    | 0.4347 | 0.4908 | 0.9477   |
| 0.008         | 7.0   | 1491 | 0.3569          | 0.5339    | 0.4458 | 0.4859 | 0.9474   |
| 0.0049        | 8.0   | 1704 | 0.3988          | 0.5602    | 0.4013 | 0.4676 | 0.9470   |
| 0.0049        | 9.0   | 1917 | 0.4180          | 0.5901    | 0.3976 | 0.4751 | 0.9471   |
| 0.0032        | 10.0  | 2130 | 0.3969          | 0.5320    | 0.4161 | 0.4670 | 0.9468   |
| 0.0032        | 11.0  | 2343 | 0.4265          | 0.5851    | 0.4013 | 0.4761 | 0.9473   |
| 0.003         | 12.0  | 2556 | 0.4003          | 0.5569    | 0.4263 | 0.4829 | 0.9475   |
| 0.003         | 13.0  | 2769 | 0.4234          | 0.5936    | 0.3967 | 0.4756 | 0.9480   |
| 0.003         | 14.0  | 2982 | 0.4016          | 0.5482    | 0.4272 | 0.4802 | 0.9482   |
| 0.002         | 15.0  | 3195 | 0.4312          | 0.5655    | 0.4041 | 0.4714 | 0.9471   |
| 0.002         | 16.0  | 3408 | 0.4310          | 0.5611    | 0.4087 | 0.4729 | 0.9470   |
| 0.0014        | 17.0  | 3621 | 0.4287          | 0.5556    | 0.4124 | 0.4734 | 0.9471   |
| 0.0014        | 18.0  | 3834 | 0.4193          | 0.5572    | 0.4198 | 0.4789 | 0.9475   |
| 0.0014        | 19.0  | 4047 | 0.4188          | 0.5583    | 0.4263 | 0.4834 | 0.9478   |
| 0.0014        | 20.0  | 4260 | 0.4207          | 0.5583    | 0.4263 | 0.4834 | 0.9478   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2