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---
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