metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
base_model: distilbert-base-uncased
model-index:
  - name: distilbert-base-uncased-finetuned-sst2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: glue
          type: glue
          config: sst2
          split: train
          args: sst2
        metrics:
          - type: accuracy
            value: 0.9071100917431193
            name: Accuracy

distilbert-base-uncased-finetuned-sst2

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

Loss: 0.2842
Accuracy: 0.9071

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
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	0.02	100	0.3316	0.8624
No log	0.05	200	0.3357	0.8612
No log	0.07	300	0.3996	0.8383
No log	0.1	400	0.3012	0.8716
0.3421	0.12	500	0.3227	0.8693
0.3421	0.14	600	0.3643	0.8727
0.3421	0.17	700	0.2734	0.8853
0.3421	0.19	800	0.3077	0.8945
0.3421	0.21	900	0.2709	0.9002
0.2705	0.24	1000	0.2737	0.8899
0.2705	0.26	1100	0.3079	0.8979
0.2705	0.29	1200	0.2713	0.8968
0.2705	0.31	1300	0.2505	0.8933
0.2705	0.33	1400	0.2932	0.8922
0.239	0.36	1500	0.2842	0.9071
0.239	0.38	1600	0.2509	0.9014
0.239	0.4	1700	0.2819	0.8853
0.239	0.43	1800	0.2515	0.8956

Framework versions

Transformers 4.25.1
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2