metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - hyperpartisan_news_detection
metrics:
  - accuracy
base_model: distilbert-base-uncased
model-index:
  - name: hyperpartisan-classifier
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: hyperpartisan_news_detection
          type: hyperpartisan_news_detection
          config: bypublisher
          split: validation
          args: bypublisher
        metrics:
          - type: accuracy
            value: 0.9988466666666667
            name: Accuracy

hyperpartisan-classifier

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

Loss: 0.0036
Accuracy: 0.9988

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: 64
eval_batch_size: 64
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
0.1441	0.11	1000	0.1391	0.9453
0.1248	0.21	2000	0.1042	0.9595
0.1027	0.32	3000	0.0913	0.9647
0.0928	0.43	4000	0.0827	0.9688
0.0992	0.53	5000	0.0799	0.9698
0.0881	0.64	6000	0.0710	0.9741
0.078	0.75	7000	0.0640	0.9762
0.0708	0.85	8000	0.0626	0.9764
0.0696	0.96	9000	0.0564	0.9792
0.0586	1.07	10000	0.0516	0.9813
0.0558	1.17	11000	0.0507	0.9815
0.0531	1.28	12000	0.0463	0.9829
0.0585	1.39	13000	0.0468	0.9831
0.0488	1.49	14000	0.0403	0.9854
0.057	1.6	15000	0.0393	0.9865
0.0514	1.71	16000	0.0349	0.9879
0.052	1.81	17000	0.0366	0.9868
0.0572	1.92	18000	0.0300	0.9895
0.0311	2.03	19000	0.0309	0.9893
0.0332	2.13	20000	0.0262	0.9908
0.0396	2.24	21000	0.0250	0.9914
0.0314	2.35	22000	0.0223	0.9924
0.0361	2.45	23000	0.0236	0.9919
0.0289	2.56	24000	0.0197	0.9933
0.0322	2.67	25000	0.0182	0.9939
0.0416	2.77	26000	0.0183	0.9937
0.0273	2.88	27000	0.0159	0.9946
0.0317	2.99	28000	0.0152	0.9949
0.0203	3.09	29000	0.0132	0.9957
0.0182	3.2	30000	0.0146	0.9953
0.0165	3.31	31000	0.0123	0.9961
0.0184	3.41	32000	0.0105	0.9968
0.0208	3.52	33000	0.0103	0.9967
0.0187	3.63	34000	0.0083	0.9973
0.0183	3.73	35000	0.0076	0.9977
0.0258	3.84	36000	0.0073	0.9977
0.0114	3.95	37000	0.0066	0.9979
0.007	4.05	38000	0.0052	0.9983
0.0094	4.16	39000	0.0061	0.9981
0.0106	4.27	40000	0.0053	0.9983
0.0134	4.37	41000	0.0052	0.9984
0.0087	4.48	42000	0.0040	0.9987
0.018	4.59	43000	0.0047	0.9985
0.0118	4.69	44000	0.0041	0.9987
0.012	4.8	45000	0.0038	0.9988
0.0165	4.91	46000	0.0036	0.9988

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2