metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - sibyl
datasets:
  - yelp_polarity
metrics:
  - accuracy
model-index:
  - name: bert-base-uncased-yelp_polarity
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: yelp_polarity
          type: yelp_polarity
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9516052631578947

bert-base-uncased-yelp_polarity

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

Loss: 0.3222
Accuracy: 0.9516

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: 1
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 277200
training_steps: 2772000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8067	0.0	2000	0.8241	0.4975
0.5482	0.01	4000	0.3507	0.8591
0.3427	0.01	6000	0.3750	0.9139
0.4133	0.01	8000	0.5520	0.9016
0.4301	0.02	10000	0.3803	0.9304
0.3716	0.02	12000	0.4168	0.9337
0.4076	0.03	14000	0.5042	0.9170
0.3674	0.03	16000	0.4806	0.9268
0.3813	0.03	18000	0.4227	0.9261
0.3723	0.04	20000	0.3360	0.9418
0.3876	0.04	22000	0.3255	0.9407
0.3351	0.04	24000	0.3283	0.9404
0.34	0.05	26000	0.3489	0.9430
0.3006	0.05	28000	0.3302	0.9464
0.349	0.05	30000	0.3853	0.9375
0.3696	0.06	32000	0.2992	0.9454
0.3301	0.06	34000	0.3484	0.9464
0.3151	0.06	36000	0.3529	0.9455
0.3682	0.07	38000	0.3052	0.9420
0.3184	0.07	40000	0.3323	0.9466
0.3207	0.08	42000	0.3133	0.9532
0.3346	0.08	44000	0.3826	0.9414
0.3008	0.08	46000	0.3059	0.9484
0.3306	0.09	48000	0.3089	0.9475
0.342	0.09	50000	0.3611	0.9486
0.3424	0.09	52000	0.3227	0.9445
0.3044	0.1	54000	0.3130	0.9489
0.3278	0.1	56000	0.3827	0.9368
0.288	0.1	58000	0.3080	0.9504
0.3342	0.11	60000	0.3252	0.9471
0.3737	0.11	62000	0.4250	0.9343

Framework versions

Transformers 4.10.2
Pytorch 1.7.1
Datasets 1.6.1
Tokenizers 0.10.3