---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-DistillBERT-amazon-polarity
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: amazon_polarity
      type: sentiment
      args: default
    metrics:
    - type: accuracy
      value: 0.9166666666666666
      name: Accuracy
    - type: loss
      value: 0.1919892132282257
      name: Loss
    - type: f1
      value: 0.9169435215946843
      name: F1
datasets:
- amazon_polarity
pipeline_tag: text-classification
---

<!-- 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. -->

# finetuning-DistillBERT-amazon-polarity

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [Amazon Polarity](https://huggingface.co/datasets/amazon_polarity) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1920
- Accuracy: 0.9167
- F1: 0.9169

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2