metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - go_emotions
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: distilbert-go-emotions
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: go_emotions
          type: go_emotions
          args: simplified
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.42867674161444896
          - name: Precision
            type: precision
            value: 0.4503794622630937
          - name: Recall
            type: recall
            value: 0.5002719498073401
          - name: F1
            type: f1
            value: 0.47024637118703766

distilbert-go-emotions

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

Loss: 0.0907
Accuracy: 0.4287
Precision: 0.4504
Recall: 0.5003
F1: 0.4702

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: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	340	0.1102	0.4125	0.2946	0.2139	0.2014
0.1347	2.0	680	0.0890	0.3900	0.4252	0.4627	0.4262
0.1347	3.0	1020	0.0860	0.4097	0.4344	0.4992	0.4578
0.0771	4.0	1360	0.0864	0.4298	0.4561	0.4974	0.4701
0.0771	5.0	1700	0.0891	0.4266	0.4522	0.4992	0.4703
0.0617	6.0	2040	0.0907	0.4287	0.4504	0.5003	0.4702

Framework versions

Transformers 4.20.1
Pytorch 1.12.0
Datasets 2.1.0
Tokenizers 0.12.1