Synthetic Data Generator
Collection
A collection of tools and datasets related to no-code the Synthetic Data Generation.
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16 items
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Updated
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domain-classifier
This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3927
- F1: 0.8914
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-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 29 | 0.7298 | 0.7744 |
| No log | 2.0 | 58 | 0.4369 | 0.8311 |
| No log | 3.0 | 87 | 0.6091 | 0.8399 |
| 0.7663 | 4.0 | 116 | 0.4352 | 0.8798 |
| 0.7663 | 5.0 | 145 | 0.3927 | 0.8914 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.0
- Datasets 3.1.0
- Tokenizers 0.21.0
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Model tree for argilla/ModernBERT-domain-classifier
Base model
answerdotai/ModernBERT-base