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---
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
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