metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - sst2
metrics:
  - accuracy
model-index:
  - name: distilbert_base_SST2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: sst2
          type: sst2
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8990825688073395

distilbert_base_SST2

This model is a fine-tuned version of distilbert-base-uncased on the sst2 dataset. It achieves the following results on the evaluation set:

Loss: 0.4181
Accuracy: 0.8991

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: 8
eval_batch_size: 8
seed: 42
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
0.4378	0.06	500	0.3452	0.8601
0.343	0.12	1000	0.3483	0.8578
0.3342	0.18	1500	0.3373	0.8704
0.308	0.24	2000	0.4102	0.8819
0.2932	0.3	2500	0.3546	0.8830
0.3116	0.36	3000	0.3609	0.8716
0.2805	0.42	3500	0.3800	0.8945
0.2655	0.48	4000	0.4131	0.8842
0.2504	0.53	4500	0.4299	0.8830
0.2543	0.59	5000	0.5196	0.8727
0.2409	0.65	5500	0.4387	0.8807
0.2414	0.71	6000	0.4121	0.8922
0.2319	0.77	6500	0.3772	0.8830
0.247	0.83	7000	0.4179	0.8876
0.2233	0.89	7500	0.3544	0.8945
0.2202	0.95	8000	0.4160	0.8865
0.2242	1.01	8500	0.5125	0.8784
0.1296	1.07	9000	0.4212	0.8842
0.1429	1.13	9500	0.4675	0.8968
0.1466	1.19	10000	0.5034	0.8922
0.1626	1.25	10500	0.4431	0.8945
0.1459	1.31	11000	0.5001	0.8922
0.1489	1.37	11500	0.4739	0.8968
0.1591	1.43	12000	0.3852	0.8945
0.1211	1.48	12500	0.4648	0.8945
0.1275	1.54	13000	0.5281	0.8956
0.1302	1.6	13500	0.4411	0.8933
0.1313	1.66	14000	0.4914	0.8979
0.134	1.72	14500	0.3923	0.8979
0.1355	1.78	15000	0.4164	0.8956
0.1263	1.84	15500	0.4293	0.8945
0.1326	1.9	16000	0.4185	0.8933
0.1315	1.96	16500	0.4181	0.8991

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0