metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - sibyl
datasets:
  - yahoo_answers_topics
metrics:
  - accuracy
model-index:
  - name: bert-base-uncased-yahoo_answers_topics
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: yahoo_answers_topics
          type: yahoo_answers_topics
          args: yahoo_answers_topics
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7499166666666667

bert-base-uncased-yahoo_answers_topics

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

Loss: 0.8092
Accuracy: 0.7499

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 86625
training_steps: 866250

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.162	0.01	2000	1.7444	0.5681
1.3126	0.02	4000	1.0081	0.7054
0.9592	0.03	6000	0.9021	0.7234
0.8903	0.05	8000	0.8827	0.7276
0.8685	0.06	10000	0.8540	0.7341
0.8422	0.07	12000	0.8547	0.7365
0.8535	0.08	14000	0.8264	0.7372
0.8178	0.09	16000	0.8331	0.7389
0.8325	0.1	18000	0.8242	0.7411
0.8181	0.12	20000	0.8356	0.7437
0.8171	0.13	22000	0.8090	0.7451
0.8092	0.14	24000	0.8469	0.7392
0.8057	0.15	26000	0.8185	0.7478
0.8085	0.16	28000	0.8090	0.7467
0.8229	0.17	30000	0.8225	0.7417
0.8151	0.18	32000	0.8262	0.7419
0.81	0.2	34000	0.8149	0.7383
0.8073	0.21	36000	0.8225	0.7441
0.816	0.22	38000	0.8037	0.744
0.8217	0.23	40000	0.8409	0.743
0.82	0.24	42000	0.8286	0.7385
0.8101	0.25	44000	0.8282	0.7413
0.8254	0.27	46000	0.8170	0.7414

Framework versions

Transformers 4.10.2
Pytorch 1.7.1
Datasets 1.6.1
Tokenizers 0.10.3