---
license: apache-2.0
tags:
- generated_from_trainer
datasets: juliensimon/amazon-shoe-reviews
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-amazon-shoe-reviews
  results: []
---

# distilbert-amazon-shoe-reviews

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a [subset](https://huggingface.co/datasets/juliensimon/amazon-shoe-reviews) of the [Amazon US reviews](https://huggingface.co/datasets/amazon_us_reviews) dataset.

It achieves the following results on the evaluation set:
- Loss: 0.9532
- Accuracy: 0.5779
- F1: [0.62616119 0.46456105 0.50993865 0.55755123 0.734375  ]
- Precision: [0.62757927 0.46676662 0.49148534 0.58430541 0.72415507]
- Recall: [0.6247495  0.46237624 0.52983172 0.53313982 0.74488753]

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1                                                       | Precision                                                | Recall                                                   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:|
| 0.9713        | 1.0   | 2813 | 0.9532          | 0.5779   | [0.62616119 0.46456105 0.50993865 0.55755123 0.734375  ] | [0.62757927 0.46676662 0.49148534 0.58430541 0.72415507] | [0.6247495  0.46237624 0.52983172 0.53313982 0.74488753] |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1