metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: bert-base-sst-2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: sst2
          split: validation
          args: sst2
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.930045871559633
          - name: F1
            type: f1
            value: 0.9299971705127952
          - name: Precision
            type: precision
            value: 0.9302394783826914
          - name: Recall
            type: recall
            value: 0.9298749684263703

bert-base-sst-2

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

Loss: 0.4216
Accuracy: 0.9300
F1: 0.9300
Precision: 0.9302
Recall: 0.9299

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: 0.0001
train_batch_size: 160
eval_batch_size: 160
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 640
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.2366	1.0	105	0.2193	0.9117	0.9115	0.9139	0.9111
0.1104	2.0	210	0.2174	0.9243	0.9243	0.9243	0.9243
0.0685	2.99	315	0.2441	0.9186	0.9185	0.9186	0.9185
0.0476	4.0	421	0.2524	0.9232	0.9232	0.9233	0.9234
0.0319	5.0	526	0.2832	0.9220	0.9219	0.9226	0.9217
0.0227	6.0	631	0.3093	0.9289	0.9289	0.9289	0.9289
0.0169	6.99	736	0.3755	0.9209	0.9209	0.9208	0.9210
0.0112	8.0	842	0.3793	0.9220	0.9219	0.9234	0.9215
0.0079	9.0	947	0.3980	0.9255	0.9254	0.9255	0.9254
0.007	9.98	1050	0.4216	0.9300	0.9300	0.9302	0.9299

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3