Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use Huyle2501/classifier-assignment01_ModernBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Huyle2501/classifier-assignment01_ModernBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Huyle2501/classifier-assignment01_ModernBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Huyle2501/classifier-assignment01_ModernBERT") model = AutoModelForSequenceClassification.from_pretrained("Huyle2501/classifier-assignment01_ModernBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
classifier-assignment01_ModernBERT
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: 0.3026
- Accuracy: 0.9264
- F1: 0.9264
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: 16
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2946 | 1.0 | 625 | 0.2445 | 0.9167 | 0.9165 |
| 0.1595 | 2.0 | 1250 | 0.3026 | 0.9264 | 0.9264 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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Model tree for Huyle2501/classifier-assignment01_ModernBERT
Base model
answerdotai/ModernBERT-base