Instructions to use iTroned/no_single_layer_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/no_single_layer_3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/no_single_layer_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
no_single_layer_3
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3749
accuracy
: 0.8419
f1
: 0.8426
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: 1e-05
- 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 | 0.3756 | 0.8535 | 0.8478 | | 0.4631 | 2.0 | 828 | 0.3749 | 0.8419 | 0.8426 | | 0.3717 | 3.0 | 1242 | 0.3789 | 0.8349 | 0.8338 | | 0.3056 | 4.0 | 1656 | 0.4133 | 0.8349 | 0.8338 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
Inference Providers NEW
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Model tree for iTroned/no_single_layer_3
Base model
distilbert/distilbert-base-uncased