---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- essay_dataset
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert_B001
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: essay_dataset
      type: essay_dataset
      config: mittelwerte
      split: test
      args: mittelwerte
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.5280898876404494
    - name: Precision
      type: precision
      value:
        precision: 0.19377125850340135
    - name: Recall
      type: recall
      value:
        recall: 0.2962962962962963
    - name: F1
      type: f1
      value:
        f1: 0.21358825283243887
---

<!-- 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_B001

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the essay_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3451
- Accuracy: {'accuracy': 0.5280898876404494}
- Precision: {'precision': 0.19377125850340135}
- Recall: {'recall': 0.2962962962962963}
- F1: {'f1': 0.21358825283243887}

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | Precision                          | Recall                          | F1                          |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:----------------------------------:|:-------------------------------:|:---------------------------:|
| No log        | 1.0   | 42   | 1.6131          | {'accuracy': 0.4044943820224719} | {'precision': 0.10313447927199192} | {'recall': 0.2456896551724138}  | {'f1': 0.13425925925925927} |
| No log        | 2.0   | 84   | 1.4558          | {'accuracy': 0.4943820224719101} | {'precision': 0.16714285714285715} | {'recall': 0.24942129629629628} | {'f1': 0.19666725679383906} |
| No log        | 3.0   | 126  | 1.3405          | {'accuracy': 0.5730337078651685} | {'precision': 0.20856060606060606} | {'recall': 0.31513409961685823} | {'f1': 0.2357282221467332}  |
| No log        | 4.0   | 168  | 1.3451          | {'accuracy': 0.5280898876404494} | {'precision': 0.19377125850340135} | {'recall': 0.2962962962962963}  | {'f1': 0.21358825283243887} |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1