metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - tweet_eval
metrics:
  - precision
  - recall
model-index:
  - name: bert-emotion
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: tweet_eval
          type: tweet_eval
          config: emotion
          split: train
          args: emotion
        metrics:
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            name: Precision
          - type: recall
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            name: Recall
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            name: loss
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bert-emotion

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

Loss: 1.2053
Precision: 0.7072
Recall: 0.7233
Fscore: 0.7124

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: 4
eval_batch_size: 4
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	Precision	Recall	Fscore
0.8623	1.0	815	0.7198	0.7536	0.6312	0.6559
0.5637	2.0	1630	0.8756	0.7213	0.7166	0.7160
0.2845	3.0	2445	1.2053	0.7072	0.7233	0.7124

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2