Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use C-L-V/PsyDefDetect_ModernBERT-base_unmerged_lr-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use C-L-V/PsyDefDetect_ModernBERT-base_unmerged_lr-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="C-L-V/PsyDefDetect_ModernBERT-base_unmerged_lr-5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("C-L-V/PsyDefDetect_ModernBERT-base_unmerged_lr-5") model = AutoModelForSequenceClassification.from_pretrained("C-L-V/PsyDefDetect_ModernBERT-base_unmerged_lr-5") - Notebooks
- Google Colab
- Kaggle
PsyDefDetect_ModernBERT-base_unmerged_lr-5
This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1439
- Accuracy: 0.4209
- Macro F1: 0.2336
- Weighted F1: 0.4522
- Macro Precision: 0.2379
- Macro Recall: 0.2525
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Weighted F1 | Macro Precision | Macro Recall |
|---|---|---|---|---|---|---|---|---|
| 2.1513 | 1.0 | 187 | 2.1061 | 0.3780 | 0.1479 | 0.3889 | 0.1835 | 0.1722 |
| 1.8599 | 2.0 | 374 | 1.9307 | 0.4370 | 0.2102 | 0.4338 | 0.3074 | 0.2372 |
| 1.5468 | 3.0 | 561 | 2.0476 | 0.3298 | 0.2274 | 0.3744 | 0.2678 | 0.2634 |
| 1.1950 | 4.0 | 748 | 2.1439 | 0.4209 | 0.2336 | 0.4522 | 0.2379 | 0.2525 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for C-L-V/PsyDefDetect_ModernBERT-base_unmerged_lr-5
Base model
answerdotai/ModernBERT-base