---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test_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: 0.516566265060241
    - name: Recall
      type: recall
      value: 0.3178869323447637
    - name: F1
      type: f1
      value: 0.39357429718875503
    - name: Accuracy
      type: accuracy
      value: 0.9431405241332136
---

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# test_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: 0.2848
- Precision: 0.5166
- Recall: 0.3179
- F1: 0.3936
- Accuracy: 0.9431

## 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-06
- train_batch_size: 6
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.376         | 1.0   | 566  | 0.3069          | 0.3829    | 0.1242 | 0.1875 | 0.9331   |
| 0.1664        | 2.0   | 1132 | 0.2941          | 0.5151    | 0.2530 | 0.3393 | 0.9387   |
| 0.1259        | 3.0   | 1698 | 0.3256          | 0.5982    | 0.2456 | 0.3482 | 0.9405   |
| 0.1189        | 4.0   | 2264 | 0.2935          | 0.5420    | 0.3049 | 0.3903 | 0.9428   |
| 0.0992        | 5.0   | 2830 | 0.2848          | 0.5166    | 0.3179 | 0.3936 | 0.9431   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.2
- Tokenizers 0.13.3