Instructions to use iTroned/no_roberta_suba_fixed_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/no_roberta_suba_fixed_0 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/no_roberta_suba_fixed_0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
no_roberta_suba_fixed_0
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: 0.3627
accuracy
: 0.8558
f1
: 0.8525
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 | 0.3949 | 0.8407 | 0.8332 | | 0.5093 | 2.0 | 828 | 0.3862 | 0.8360 | 0.8368 | | 0.4039 | 3.0 | 1242 | 0.3641 | 0.8605 | 0.8543 | | 0.3751 | 4.0 | 1656 | 0.3627 | 0.8558 | 0.8525 | | 0.341 | 5.0 | 2070 | 0.3857 | 0.8453 | 0.8454 | | 0.341 | 6.0 | 2484 | 0.3924 | 0.8558 | 0.8491 |
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_0
Base model
distilbert/distilbert-base-uncased