---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- essay_dataset
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: B001_cleaned
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: essay_dataset
      type: essay_dataset
      config: cleaned
      split: test
      args: cleaned
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.10526315789473684
    - name: Precision
      type: precision
      value:
        precision: 0.013157894736842105
    - name: Recall
      type: recall
      value:
        recall: 0.125
    - name: F1
      type: f1
      value:
        f1: 0.02380952380952381
---

<!-- 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. -->

# B001_cleaned

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: 2.2117
- Accuracy: {'accuracy': 0.10526315789473684}
- Precision: {'precision': 0.013157894736842105}
- Recall: {'recall': 0.125}
- F1: {'f1': 0.02380952380952381}

## 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   | 13   | 2.2061          | {'accuracy': 0.10526315789473684} | {'precision': 0.013157894736842105} | {'recall': 0.125} | {'f1': 0.02380952380952381} |
| No log        | 2.0   | 26   | 2.2050          | {'accuracy': 0.10526315789473684} | {'precision': 0.013157894736842105} | {'recall': 0.125} | {'f1': 0.02380952380952381} |
| No log        | 3.0   | 39   | 2.2045          | {'accuracy': 0.10526315789473684} | {'precision': 0.013157894736842105} | {'recall': 0.125} | {'f1': 0.02380952380952381} |
| No log        | 4.0   | 52   | 2.2117          | {'accuracy': 0.10526315789473684} | {'precision': 0.013157894736842105} | {'recall': 0.125} | {'f1': 0.02380952380952381} |


### Framework versions

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