---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: distilbert_amazon_book_classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert_amazon_book_classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4475
- Accuracy: 0.5871
- F1 Score: 0.5865
- Precision: 0.5967
- Recall: 0.5871

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1 Score | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| 1.6436        | 0.9999 | 9679  | 1.4688          | 0.5680   | 0.5624   | 0.5822    | 0.5680 |
| 1.0845        | 1.9998 | 19358 | 1.4475          | 0.5871   | 0.5865   | 0.5967    | 0.5871 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 4.1.1
- Tokenizers 0.20.1