---
license: apache-2.0
base_model: 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: 0.5524652338811631
    - name: Recall
      type: recall
      value: 0.40500463392029656
    - name: F1
      type: f1
      value: 0.467379679144385
    - name: Accuracy
      type: accuracy
      value: 0.9464751400111154
---

<!-- 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 wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4068
- Precision: 0.5525
- Recall: 0.4050
- F1: 0.4674
- Accuracy: 0.9465

## 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  | 0.2742          | 0.5216    | 0.3355 | 0.4083 | 0.9421   |
| No log        | 2.0   | 426  | 0.2810          | 0.6107    | 0.3503 | 0.4452 | 0.9455   |
| 0.0744        | 3.0   | 639  | 0.3305          | 0.6560    | 0.3411 | 0.4488 | 0.9456   |
| 0.0744        | 4.0   | 852  | 0.3382          | 0.5480    | 0.3596 | 0.4342 | 0.9443   |
| 0.0295        | 5.0   | 1065 | 0.3461          | 0.5635    | 0.3865 | 0.4585 | 0.9454   |
| 0.0295        | 6.0   | 1278 | 0.3823          | 0.5744    | 0.3828 | 0.4594 | 0.9454   |
| 0.0295        | 7.0   | 1491 | 0.3404          | 0.5080    | 0.4096 | 0.4536 | 0.9445   |
| 0.0128        | 8.0   | 1704 | 0.3926          | 0.5302    | 0.3744 | 0.4389 | 0.9441   |
| 0.0128        | 9.0   | 1917 | 0.3505          | 0.5033    | 0.4226 | 0.4594 | 0.9449   |
| 0.0071        | 10.0  | 2130 | 0.3825          | 0.5685    | 0.3846 | 0.4588 | 0.9456   |
| 0.0071        | 11.0  | 2343 | 0.3806          | 0.5155    | 0.4171 | 0.4611 | 0.9451   |
| 0.0044        | 12.0  | 2556 | 0.4035          | 0.5422    | 0.3985 | 0.4594 | 0.9454   |
| 0.0044        | 13.0  | 2769 | 0.4106          | 0.5940    | 0.3865 | 0.4683 | 0.9465   |
| 0.0044        | 14.0  | 2982 | 0.4069          | 0.5485    | 0.4032 | 0.4647 | 0.9457   |
| 0.0032        | 15.0  | 3195 | 0.4280          | 0.6029    | 0.3800 | 0.4662 | 0.9466   |
| 0.0032        | 16.0  | 3408 | 0.4049          | 0.5798    | 0.4208 | 0.4876 | 0.9472   |
| 0.0026        | 17.0  | 3621 | 0.4129          | 0.5758    | 0.4013 | 0.4730 | 0.9470   |
| 0.0026        | 18.0  | 3834 | 0.4131          | 0.5731    | 0.4069 | 0.4759 | 0.9469   |
| 0.0021        | 19.0  | 4047 | 0.4074          | 0.5557    | 0.4022 | 0.4667 | 0.9465   |
| 0.0021        | 20.0  | 4260 | 0.4068          | 0.5525    | 0.4050 | 0.4674 | 0.9465   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3