metadata

library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
  - generated_from_trainer
  - sentiment_analysis
model-index:
  - name: go-emotions-fine-tuned-distilroberta
    results: []
datasets:
  - google-research-datasets/go_emotions
language:
  - en
metrics:
  - recall
  - precision
  - f1

go-emotions-fine-tuned-distilroberta

This model is a fine-tuned version of distilbert/distilroberta-base on GoEmotions dataset. It achieves the following results on the evaluation set (threshold = 0.5):

Loss: 0.0841
Micro Precision: 0.6789
Micro Recall: 0.5047
Micro F1: 0.5790
Macro Precision: 0.5559
Macro Recall: 0.4000
Macro F1: 0.4502
Weighted Precision: 0.6538
Weighted Recall: 0.5047
Weighted F1: 0.5577
Hamming Loss: 0.0308

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Micro Precision	Micro Recall	Micro F1	Macro Precision	Macro Recall	Macro F1	Weighted Precision	Weighted Recall	Weighted F1	Hamming Loss
0.1062	1.0	5427	0.0889	0.6956	0.4498	0.5464	0.5087	0.3111	0.3537	0.6246	0.4498	0.4936	0.0314
0.0828	2.0	10854	0.0834	0.7042	0.4798	0.5707	0.5874	0.3562	0.4108	0.6872	0.4798	0.5306	0.0303
0.0704	3.0	16281	0.0841	0.6789	0.5047	0.5790	0.5559	0.4000	0.4502	0.6538	0.5047	0.5577	0.0308

Test results

Class	Precision	Recall	F1-Score	Support
admiration	0.69	0.73	0.71	504
amusement	0.79	0.87	0.83	264
anger	0.58	0.41	0.48	198
annoyance	0.45	0.16	0.24	320
approval	0.58	0.34	0.43	351
caring	0.51	0.29	0.37	135
confusion	0.57	0.38	0.46	153
curiosity	0.50	0.46	0.48	284
desire	0.70	0.36	0.48	83
disappointment	0.60	0.19	0.28	151
disapproval	0.42	0.29	0.34	267
disgust	0.63	0.33	0.44	123
embarrassment	0.82	0.38	0.52	37
excitement	0.57	0.33	0.42	103
fear	0.71	0.64	0.68	78
gratitude	0.94	0.90	0.92	352
grief	0.00	0.00	0.00	6
joy	0.69	0.54	0.61	161
love	0.82	0.84	0.83	238
nervousness	0.67	0.17	0.28	23
optimism	0.63	0.48	0.55	186
pride	0.00	0.00	0.00	16
realization	0.54	0.13	0.21	145
relief	0.00	0.00	0.00	11
remorse	0.58	0.77	0.66	56
sadness	0.67	0.49	0.57	156
surprise	0.61	0.44	0.51	141
neutral	0.73	0.54	0.62	1787
micro avg	0.68	0.51	0.58	6329
macro avg	0.57	0.41	0.46	6329
weighted avg	0.66	0.51	0.56	6329
samples avg	0.56	0.53	0.54	6329

Framework versions

Transformers 4.47.0
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.21.0