metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
model-index:
  - name: BERT_Emotions_tuned
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          config: split
          split: validation
          args: split
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9295

BERT_Emotions_tuned

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

Loss: 0.2033
Accuracy: 0.9295

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: 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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.1	100	0.8098	0.7195
No log	0.2	200	0.4054	0.882
No log	0.3	300	0.4686	0.877
No log	0.4	400	0.2850	0.909
0.5652	0.5	500	0.2673	0.92
0.5652	0.6	600	0.2474	0.9255
0.5652	0.7	700	0.1943	0.933
0.5652	0.8	800	0.1779	0.9315
0.5652	0.9	900	0.1720	0.939
0.2212	1.0	1000	0.1747	0.9375
0.2212	1.1	1100	0.1902	0.933
0.2212	1.2	1200	0.1540	0.941
0.2212	1.3	1300	0.1599	0.937
0.2212	1.4	1400	0.1533	0.944
0.1315	1.5	1500	0.1421	0.937
0.1315	1.6	1600	0.1549	0.941
0.1315	1.7	1700	0.1284	0.9435
0.1315	1.8	1800	0.1376	0.934
0.1315	1.9	1900	0.1197	0.943
0.1204	2.0	2000	0.1319	0.9385
0.1204	2.1	2100	0.1535	0.935
0.1204	2.2	2200	0.1488	0.943
0.1204	2.3	2300	0.1583	0.94
0.1204	2.4	2400	0.1426	0.9425
0.0913	2.5	2500	0.1554	0.9395
0.0913	2.6	2600	0.1458	0.944
0.0913	2.7	2700	0.1504	0.943
0.0913	2.8	2800	0.1621	0.9465
0.0913	2.9	2900	0.1521	0.944
0.0842	3.0	3000	0.1533	0.944

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2