Instructions to use iTroned/no_roberta_suba_fixed_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/no_roberta_suba_fixed_3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/no_roberta_suba_fixed_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
no_roberta_suba_fixed_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: 0.3670
accuracy
: 0.85
f1
: 0.8467
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 | 0.3913 | 0.8477 | 0.8405 | | 0.5047 | 2.0 | 828 | 0.3886 | 0.8314 | 0.8329 | | 0.402 | 3.0 | 1242 | 0.3704 | 0.8547 | 0.8478 | | 0.3722 | 4.0 | 1656 | 0.3670 | 0.85 | 0.8467 | | 0.339 | 5.0 | 2070 | 0.3816 | 0.8488 | 0.8486 | | 0.339 | 6.0 | 2484 | 0.4015 | 0.8477 | 0.8392 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
Model tree for iTroned/no_roberta_suba_fixed_3
Base model
distilbert/distilbert-base-uncased