metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - go_emotions
metrics:
  - f1
model-index:
  - name: roberta-large-go-emotions-2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: go_emotions
          type: multilabel_classification
          config: simplified
          split: test
          args: simplified
        metrics:
          - name: F1
            type: f1
            value: 0.518
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: go_emotions
          type: multilabel_classification
          config: simplified
          split: validation
          args: simplified
        metrics:
          - name: F1
            type: f1
            value: 0.5203

roberta-large-go-emotions-2

This model is a fine-tuned version of roberta-large on the go_emotions dataset. It achieves the following results on the test set (with a threshold of 0.15):

Accuracy: 0.44020
Precision: 0.5041
Recall: 0.5461
F1: 0.5180

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: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 9

Training results

Training Loss	Epoch	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	0.0889	0.4043	0.4807	0.4568	0.4446
0.1062	2.0	0.0828	0.4113	0.4608	0.5363	0.4868
0.1062	3.0	0.0813	0.4201	0.5198	0.5612	0.5227
No log	4.0	0.0862	0.4292	0.5012	0.5558	0.5208
0.0597	5.0	0.0924	0.4329	0.5164	0.5362	0.5151
0.0597	6.0	0.0956	0.4445	0.5241	0.5328	0.5161
No log	7.0	0.0962	0.4648	0.5138	0.5277	0.5151
0.0458	8.0	0.0962	0.4462	0.5257	0.5270	0.5203
0.0458	9.0	0.1029	0.4432	0.5076	0.5249	0.5111

Framework versions

Transformers 4.20.1
Pytorch 1.12.0
Datasets 2.1.0
Tokenizers 0.12.1