metadata

license: apache-2.0
base_model: albert-base-v2
tags:
  - generated_from_trainer
datasets:
  - imdb
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: albert-imdb
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: imdb
          type: imdb
          config: plain_text
          split: test
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.94816
          - name: F1
            type: f1
            value: 0.9481598672892603
          - name: Precision
            type: precision
            value: 0.9481645892053935
          - name: Recall
            type: recall
            value: 0.94816

albert-imdb

This model is a fine-tuned version of albert-base-v2 on the imdb dataset. It achieves the following results on the evaluation set:

Loss: 0.1571
Accuracy: 0.9482
F1: 0.9482
Precision: 0.9482
Recall: 0.9482

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: 16
eval_batch_size: 16
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
0.3213	1.0	1563	0.2683	0.8922	0.8921	0.8930	0.8922
0.2211	2.0	3126	0.2559	0.9082	0.9082	0.9082	0.9082

Framework versions

Transformers 4.38.2
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.2