metadata

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 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