---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: category-1-delivery-cancellation-distilbert-base-uncased-finetuned-sst-2-english-v1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# category-1-delivery-cancellation-distilbert-base-uncased-finetuned-sst-2-english-v1

This model is a fine-tuned version of [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3010
- Accuracy: 0.9416

## 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: 60
- eval_batch_size: 60
- 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   | 334  | 0.3121          | 0.8923   |
| 0.2186        | 2.0   | 668  | 0.1633          | 0.9436   |
| 0.1323        | 3.0   | 1002 | 0.1761          | 0.9370   |
| 0.1323        | 4.0   | 1336 | 0.2427          | 0.9233   |
| 0.0856        | 5.0   | 1670 | 0.2129          | 0.9399   |
| 0.0606        | 6.0   | 2004 | 0.2481          | 0.9355   |
| 0.0606        | 7.0   | 2338 | 0.2382          | 0.9447   |
| 0.0389        | 8.0   | 2672 | 0.2654          | 0.9406   |
| 0.0266        | 9.0   | 3006 | 0.2953          | 0.9411   |
| 0.0266        | 10.0  | 3340 | 0.3010          | 0.9416   |


### Framework versions

- Transformers 4.43.2
- Pytorch 2.3.0
- Datasets 2.20.0
- Tokenizers 0.19.1