README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - tweet_eval
metrics:
  - precision
  - recall
base_model: distilbert-base-cased
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:
          - type: precision
            value: 0.7052789678093683
            name: Precision
          - type: recall
            value: 0.7133003963197697
            name: Recall

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: 1.2194
• Precision: 0.7053
• Recall: 0.7133
• Fscore: 0.7084

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
0.8589	1.0	815	0.7744	0.7321	0.6122	0.6349
0.5321	2.0	1630	1.0469	0.7381	0.6703	0.6930
0.2615	3.0	2445	1.2194	0.7053	0.7133	0.7084

Framework versions

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