Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use underscore2/modernbert_base_slop_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use underscore2/modernbert_base_slop_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="underscore2/modernbert_base_slop_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("underscore2/modernbert_base_slop_classifier") model = AutoModelForSequenceClassification.from_pretrained("underscore2/modernbert_base_slop_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: answerdotai/ModernBERT-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: modernbert_base_slop_classifier | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # modernbert_base_slop_classifier | |
| This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3131 | |
| - Accuracy: 0.9412 | |
| ## 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: 6.6666666666666675e-06 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 10 | |
| - total_train_batch_size: 10 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: constant | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| | |
| | 0.7205 | 0.4667 | 150 | 0.1976 | 0.9235 | | |
| | 8.0092 | 0.9334 | 300 | 0.1773 | 0.9353 | | |
| | 0.0 | 1.3983 | 450 | 0.3947 | 0.9412 | | |
| | 0.0035 | 1.8650 | 600 | 0.3131 | 0.9412 | | |
| ### Framework versions | |
| - Transformers 4.52.2 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 | |