---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base
  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: 0.5251322751322751
    - name: Recall
      type: recall
      value: 0.36793327154772937
    - name: F1
      type: f1
      value: 0.43269754768392366
    - name: Accuracy
      type: accuracy
      value: 0.9450643409858492
---

<!-- 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. -->

# distilbert-base

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: 0.2693
- Precision: 0.5251
- Recall: 0.3679
- F1: 0.4327
- Accuracy: 0.9451

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 107  | 0.3088          | 0.3506    | 0.1446 | 0.2047 | 0.9328   |
| No log        | 2.0   | 214  | 0.2634          | 0.5403    | 0.3170 | 0.3995 | 0.9414   |
| No log        | 3.0   | 321  | 0.2530          | 0.5282    | 0.3559 | 0.4252 | 0.9435   |
| No log        | 4.0   | 428  | 0.2587          | 0.5206    | 0.3753 | 0.4362 | 0.9446   |
| 0.1695        | 5.0   | 535  | 0.2693          | 0.5251    | 0.3679 | 0.4327 | 0.9451   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3