---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-amazon-shoe-reviews
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      type: amazon_us_reviews
      name: Amazon US reviews
      split: Shoes
    metrics:
    - type: accuracy
      value: 0.6819221967963387
      name: Accuracy
---

<!-- 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-amazon-shoe-reviews

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: 0.9536
- Accuracy: 0.5767
- F1: [0.62380713 0.45806452 0.5077951  0.56106774 0.73541247]
- Precision: [0.62537764 0.45920398 0.49326923 0.58508403 0.72376238]
- Recall: [0.62224449 0.45693069 0.52320245 0.53894533 0.74744376]

## 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                                                   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:|
| 0.9704        | 1.0   | 2813 | 0.9536          | 0.5767   | [0.62380713 0.45806452 0.5077951  0.56106774 0.73541247] | [0.62537764 0.45920398 0.49326923 0.58508403 0.72376238] | [0.62224449 0.45693069 0.52320245 0.53894533 0.74744376] |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1