---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- reuters21578
metrics:
- f1
- accuracy
model-index:
- name: distilbert-finetuned-reuters21578-multilabel
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: reuters21578
      type: reuters21578
      config: ModApte
      split: test
      args: ModApte
    metrics:
    - name: F1
      type: f1
      value: 0.8628858578607322
    - name: Accuracy
      type: accuracy
      value: 0.8195625759416768
---

<!-- 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-finetuned-reuters21578-multilabel

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the reuters21578 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0110
- F1: 0.8629
- Roc Auc: 0.9063
- Accuracy: 0.8196

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.1801        | 1.0   | 300  | 0.0439          | 0.3896 | 0.6210  | 0.3566   |
| 0.0345        | 2.0   | 600  | 0.0287          | 0.6289 | 0.7318  | 0.5954   |
| 0.0243        | 3.0   | 900  | 0.0219          | 0.6721 | 0.7579  | 0.6084   |
| 0.0178        | 4.0   | 1200 | 0.0177          | 0.7505 | 0.8128  | 0.6908   |
| 0.014         | 5.0   | 1500 | 0.0151          | 0.7905 | 0.8376  | 0.7278   |
| 0.0115        | 6.0   | 1800 | 0.0135          | 0.8132 | 0.8589  | 0.7555   |
| 0.0096        | 7.0   | 2100 | 0.0124          | 0.8291 | 0.8727  | 0.7725   |
| 0.0082        | 8.0   | 2400 | 0.0124          | 0.8335 | 0.8757  | 0.7822   |
| 0.0071        | 9.0   | 2700 | 0.0119          | 0.8392 | 0.8847  | 0.7883   |
| 0.0064        | 10.0  | 3000 | 0.0123          | 0.8339 | 0.8810  | 0.7828   |
| 0.0058        | 11.0  | 3300 | 0.0114          | 0.8538 | 0.8999  | 0.8047   |
| 0.0053        | 12.0  | 3600 | 0.0113          | 0.8525 | 0.8967  | 0.8044   |
| 0.0048        | 13.0  | 3900 | 0.0115          | 0.8520 | 0.8982  | 0.8029   |
| 0.0045        | 14.0  | 4200 | 0.0111          | 0.8566 | 0.8962  | 0.8104   |
| 0.0042        | 15.0  | 4500 | 0.0110          | 0.8610 | 0.9060  | 0.8165   |
| 0.0039        | 16.0  | 4800 | 0.0112          | 0.8583 | 0.9021  | 0.8138   |
| 0.0037        | 17.0  | 5100 | 0.0110          | 0.8620 | 0.9055  | 0.8196   |
| 0.0035        | 18.0  | 5400 | 0.0110          | 0.8629 | 0.9063  | 0.8196   |
| 0.0035        | 19.0  | 5700 | 0.0111          | 0.8624 | 0.9062  | 0.8180   |
| 0.0034        | 20.0  | 6000 | 0.0111          | 0.8626 | 0.9055  | 0.8177   |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3