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transformers
pytorch
bert
text-classification
generated_from_trainer
en
apache-2.0
autotrain_compatible
endpoints_compatible
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BERT-Emotion detection model
⚙️ Model
This model is a fine-tuned version of
bert-base-uncased
on the
Emotion Dataset from Kaggle
. It achieves the following results on the test set after being trained and evaluated with the Trainer interface:
Loss: 0.2362
Accuracy: 0.923
F1: 0.9226
Precision: 0.9226
Recall: 0.923
💡How to: Inference API
Mapping of labels:
'anger': 0, 'fear': 1, 'joy': 2, 'love': 3, 'sadness': 4, 'surprise': 5
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 1e-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
lr_scheduler_warmup_steps: 500
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
1.5691
1.0
250
1.2681
0.564
0.4477
0.3868
0.564
0.9132
2.0
500
0.4917
0.8465
0.8349
0.8508
0.8465
0.3131
3.0
750
0.2362
0.923
0.9226
0.9226
0.923
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Training and evaluation data can be found on the same link as in the model description
Training procedure
Framework versions
Transformers 4.25.1
Pytorch 1.13.1+cu116
Tokenizers 0.13.2
