Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use kartikeyapandey20/MiniModernBERT-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kartikeyapandey20/MiniModernBERT-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kartikeyapandey20/MiniModernBERT-qqp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kartikeyapandey20/MiniModernBERT-qqp") model = AutoModelForSequenceClassification.from_pretrained("kartikeyapandey20/MiniModernBERT-qqp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MiniModernBERT-qqp
This model is a fine-tuned version of kartikeya-pandey/MiniModernBERT-Pretrained on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5676
- Accuracy: 0.9024
- F1: 0.8687
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: 3e-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: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.2.0
- Tokenizers 0.22.1
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Model tree for kartikeyapandey20/MiniModernBERT-qqp
Base model
answerdotai/ModernBERT-large