Instructions to use iTroned/tb_single_layer_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/tb_single_layer_2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/tb_single_layer_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
tb_single_layer_2
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: 0.3720
accuracy
: 0.8453
f1
: 0.8458
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 | 0.3768 | 0.8558 | 0.8510 | | 0.467 | 2.0 | 828 | 0.3720 | 0.8453 | 0.8458 | | 0.3713 | 3.0 | 1242 | 0.3747 | 0.8488 | 0.8468 | | 0.3082 | 4.0 | 1656 | 0.4173 | 0.8314 | 0.8313 |
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/tb_single_layer_2
Base model
distilbert/distilbert-base-uncased