---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: amazon_review_classification
  results: []
widget:
  - text: "Title: These earrings are much smaller than pictured. They are so tiny \n Text: The online picture is deceiving. They are shown much larger than their actual size. Was very disappointed"
    output:
      - label: Not Recommended
        score: 0.783
      - label: Negative Experience
        score: 0.087
      - label: Low Quality
        score: 0.040
      - label: Poor Service
        score: 0.026
      - label: Overpriced
        score: 0.021
      - label: Positive Experience
        score: 0.015
      - label: Excellent Service
        score: 0.009
      - label: Great Value
        score: 0.007
      - label: Highly Recommended
        score: 0.006
      - label: High Quality
        score: 0.005
---

<!-- 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. -->

# amazon_review_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: 1.3976
- Accuracy: 0.6732

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 115  | 1.0703          | 0.6732   |
| No log        | 2.0   | 230  | 1.2393          | 0.6341   |
| No log        | 3.0   | 345  | 1.1084          | 0.6683   |
| No log        | 4.0   | 460  | 1.1262          | 0.6829   |
| 0.3201        | 5.0   | 575  | 1.3179          | 0.6732   |
| 0.3201        | 6.0   | 690  | 1.3832          | 0.6585   |
| 0.3201        | 7.0   | 805  | 1.2997          | 0.6683   |
| 0.3201        | 8.0   | 920  | 1.3872          | 0.6634   |
| 0.0863        | 9.0   | 1035 | 1.3832          | 0.6634   |
| 0.0863        | 10.0  | 1150 | 1.3976          | 0.6732   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2


### Usage

```python
from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="eren23/amazon_review_classification")
classifier(text)
```