metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - tweet_eval
metrics:
  - accuracy
  - f1
model-index:
  - name: base-vanilla-target-tweet
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: tweet_eval
          type: tweet_eval
          config: emotion
          split: train
          args: emotion
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7780748663101604
          - name: F1
            type: f1
            value: 0.7772664883136655

base-vanilla-target-tweet

This model is a fine-tuned version of google/bert_uncased_L-12_H-768_A-12 on the tweet_eval dataset. It achieves the following results on the evaluation set:

Loss: 1.8380
Accuracy: 0.7781
F1: 0.7773

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.3831	4.9	500	0.9800	0.7807	0.7785
0.0414	9.8	1000	1.4175	0.7754	0.7765
0.015	14.71	1500	1.6411	0.7754	0.7708
0.0166	19.61	2000	1.5930	0.7941	0.7938
0.0175	24.51	2500	1.3934	0.7888	0.7852
0.0191	29.41	3000	1.9407	0.7647	0.7658
0.0137	34.31	3500	1.8380	0.7781	0.7773

Framework versions

Transformers 4.25.1
Pytorch 1.12.1
Datasets 2.7.1
Tokenizers 0.13.2