metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: multilabel_mood_distilbert
results: []
multilabel_mood_distilbert
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0852
- F1: 0.5646
- Precision: 0.7199
- Recall: 0.4644
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|---|---|---|---|---|---|---|
| 0.138 | 1.0 | 1357 | 0.0932 | 0.5104 | 0.7194 | 0.3955 |
| 0.0888 | 2.0 | 2714 | 0.0860 | 0.5498 | 0.7359 | 0.4389 |
| 0.0807 | 3.0 | 4071 | 0.0852 | 0.5646 | 0.7199 | 0.4644 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2