---
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: 0.5218579234972678
    - name: Recall
      type: recall
      value: 0.3540315106580167
    - name: F1
      type: f1
      value: 0.4218663721700718
    - name: Accuracy
      type: accuracy
      value: 0.9427130092770724
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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: 0.2816
- Precision: 0.5219
- Recall: 0.3540
- F1: 0.4219
- Accuracy: 0.9427

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3664        | 1.0   | 566  | 0.3082          | 0.4777    | 0.1687 | 0.2493 | 0.9354   |
| 0.1672        | 2.0   | 1132 | 0.2867          | 0.5395    | 0.3105 | 0.3941 | 0.9407   |
| 0.1265        | 3.0   | 1698 | 0.3171          | 0.5976    | 0.2753 | 0.3769 | 0.9413   |
| 0.116         | 4.0   | 2264 | 0.2914          | 0.5712    | 0.3420 | 0.4278 | 0.9431   |
| 0.0974        | 5.0   | 2830 | 0.2816          | 0.5219    | 0.3540 | 0.4219 | 0.9427   |


### Framework versions

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