Instructions to use iTroned/roberta_suba_iter_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/roberta_suba_iter_3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/roberta_suba_iter_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta_suba_iter_3
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: 0.3745
accuracy
: 0.8442
f1
: 0.8408
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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss |
accuracy
|
f1
| |:-------------:|:-----:|:----:|:---------------:|:------------:|:------:| | No log | 1.0 | 414 | 0.3964 | 0.8326 | 0.8201 | | 0.4929 | 2.0 | 828 | 0.3849 | 0.8407 | 0.8408 | | 0.3991 | 3.0 | 1242 | 0.3760 | 0.8442 | 0.8373 | | 0.3685 | 4.0 | 1656 | 0.3745 | 0.8442 | 0.8408 | | 0.3311 | 5.0 | 2070 | 0.4041 | 0.8360 | 0.8370 | | 0.3311 | 6.0 | 2484 | 0.4220 | 0.8395 | 0.8330 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
Model tree for iTroned/roberta_suba_iter_3
Base model
distilbert/distilbert-base-uncased