README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - tweet_eval
metrics:
  - precision
  - recall
model-index:
  - name: bert-emotion
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: tweet_eval
          type: tweet_eval
          config: emotion
          split: train
          args: emotion
        metrics:
          - name: Precision
            type: precision
            value: 0.7233519737225043
          - name: Recall
            type: recall
            value: 0.7300875430242757

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: 1.1567
• Precision: 0.7234
• Recall: 0.7301
• Fscore: 0.7253

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
0.8665	1.0	815	0.7583	0.7486	0.6417	0.6654
0.5527	2.0	1630	0.9565	0.6848	0.6260	0.6438
0.289	3.0	2445	1.1567	0.7234	0.7301	0.7253

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2