metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - imdb
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: distilbert-imdb
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: imdb
          type: imdb
          config: plain_text
          split: test[6250:12500]+test[-12500:-6250]
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.93024
          - name: F1
            type: f1
            value: 0.9302304818748904
          - name: Precision
            type: precision
            value: 0.93047490567909
          - name: Recall
            type: recall
            value: 0.93024

distilbert-imdb

This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:

Loss: 0.2118
Accuracy: 0.9302
F1: 0.9302
Precision: 0.9305
Recall: 0.9302

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: 5e-05
train_batch_size: 32
eval_batch_size: 64
seed: 9072
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.2803	1.0	782	0.1874	0.9276	0.9276	0.9276	0.9276
0.1143	2.0	1564	0.2118	0.9302	0.9302	0.9305	0.9302

Framework versions

Transformers 4.38.2
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.2