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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: outputs
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.8386491557223265
name: Accuracy
---
# distilbert_rotten_tomatoes_sentiment_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7927
- Accuracy: 0.8386
## Model description
The goal was to fine-tune a model on the rotten_tomatoes dataset to showcase an end-to-end workflow using the Hugging face library. As such, only the bare minimum of pre-processing was used.
## Intended uses & limitations
The model will be used as part of a blog post to help others engineers better understand what natural language processing is and how to perform a text classification.
## Training and evaluation data
The model was evaluated using the accuracy metric that form part of the Hugging Face library.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 134 | 0.5940 | 0.8340 |
| No log | 2.0 | 268 | 0.7095 | 0.8227 |
| No log | 3.0 | 402 | 0.7276 | 0.8321 |
| 0.065 | 4.0 | 536 | 0.7693 | 0.8415 |
| 0.065 | 5.0 | 670 | 0.7927 | 0.8386 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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