---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_classification_wnut
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: train
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.5846994535519126
    - name: Recall
      type: recall
      value: 0.39666357738646896
    - name: F1
      type: f1
      value: 0.47266703478741023
    - name: Accuracy
      type: accuracy
      value: 0.947714933093925
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# token_classification_wnut

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: 0.2932
- Precision: 0.5847
- Recall: 0.3967
- F1: 0.4727
- Accuracy: 0.9477

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 107  | 0.2419          | 0.5242    | 0.4319 | 0.4736 | 0.9469   |
| No log        | 2.0   | 214  | 0.2932          | 0.5847    | 0.3967 | 0.4727 | 0.9477   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2