Hugging Face's logo

sangkm
/
augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta

Text Classification
Transformers
TensorBoard
Safetensors
English
roberta
Generated from Trainer
sentiment_analysis
Inference Endpoints
Model card Files Metrics Community
augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta / README.md
pm
Update README.md
15a607c verified
preview code
|
raw
history blame
5.27 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
  - generated_from_trainer
  - sentiment_analysis
model-index:
  - name: augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta
    results: []
datasets:
  - google-research-datasets/go_emotions
language:
  - en
metrics:
  - f1
  - precision
  - recall

augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta

This model is a fine-tuned version of distilbert/distilroberta-base on the these datasets:

It achieves the following results on the evaluation set:

  • Loss: 0.0731
  • Micro Precision: 0.7189
  • Micro Recall: 0.5774
  • Micro F1: 0.6404
  • Macro Precision: 0.6049
  • Macro Recall: 0.4433
  • Macro F1: 0.4898
  • Weighted Precision: 0.7004
  • Weighted Recall: 0.5774
  • Weighted F1: 0.6243
  • Hamming Loss: 0.0276

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
No log 1.0 11118 0.0765 0.7647 0.5046 0.6080 0.6047 0.3580 0.4127 0.7321 0.5046 0.5764 0.0277
No log 2.0 22236 0.0733 0.7309 0.5344 0.6174 0.5791 0.4162 0.4611 0.7105 0.5344 0.5923 0.0282
No log 3.0 33354 0.0731 0.7189 0.5774 0.6404 0.6049 0.4433 0.4898 0.7004 0.5774 0.6243 0.0276

Test results

Threshold = 0.5

Label Precision Recall F1-Score Support
admiration 0.65 0.70 0.67 504
amusement 0.72 0.88 0.79 264
anger 0.79 0.69 0.73 1585
annoyance 0.45 0.12 0.19 320
approval 0.63 0.27 0.38 351
caring 0.44 0.36 0.40 135
confusion 0.44 0.39 0.41 153
curiosity 0.52 0.36 0.43 284
desire 0.50 0.37 0.43 83
disappointment 0.35 0.19 0.25 151
disapproval 0.49 0.31 0.38 267
disgust 0.72 0.62 0.66 1222
embarrassment 0.68 0.35 0.46 37
excitement 0.46 0.43 0.44 103
fear 0.82 0.73 0.77 787
gratitude 0.93 0.89 0.91 352
grief 0.00 0.00 0.00 6
joy 0.85 0.78 0.81 2298
love 0.70 0.60 0.65 1305
nervousness 0.44 0.17 0.25 23
optimism 0.70 0.56 0.62 1329
pride 0.00 0.00 0.00 16
realization 0.36 0.17 0.23 145
relief 0.28 0.22 0.24 160
remorse 0.59 0.80 0.68 56
sadness 0.78 0.66 0.71 2212
surprise 0.63 0.29 0.40 572
neutral 0.70 0.52 0.60 2668
Micro Avg 0.73 0.59 0.65 17388
Macro Avg 0.56 0.44 0.48 17388
Weighted Avg 0.72 0.59 0.64 17388
Samples Avg 0.63 0.60 0.60 17388

Framework versions

  • Transformers 4.47.0
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.21.0