Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use denisdrobs/ModernBERT-base-clinc-oos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use denisdrobs/ModernBERT-base-clinc-oos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="denisdrobs/ModernBERT-base-clinc-oos")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("denisdrobs/ModernBERT-base-clinc-oos") model = AutoModelForSequenceClassification.from_pretrained("denisdrobs/ModernBERT-base-clinc-oos") - Notebooks
- Google Colab
- Kaggle
ModernBERT-base-clinc-oos
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: 0.2130
- Accuracy: 0.9471
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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 318 | 0.4451 | 0.9006 |
| 1.2647 | 2.0 | 636 | 0.2800 | 0.9310 |
| 1.2647 | 3.0 | 954 | 0.2279 | 0.9484 |
| 0.055 | 4.0 | 1272 | 0.2158 | 0.9471 |
| 0.0055 | 5.0 | 1590 | 0.2130 | 0.9471 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for denisdrobs/ModernBERT-base-clinc-oos
Base model
answerdotai/ModernBERT-base