Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use mrm8488/ModernBERT-base-ft-all-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/ModernBERT-base-ft-all-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrm8488/ModernBERT-base-ft-all-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/ModernBERT-base-ft-all-nli") model = AutoModelForSequenceClassification.from_pretrained("mrm8488/ModernBERT-base-ft-all-nli") - Notebooks
- Google Colab
- Kaggle
ModernBERT-base-ft-all-nli
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: 0.6255
- Accuracy Score: 0.8903
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: 8e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Score |
|---|---|---|---|---|
| 0.3421 | 1.0 | 7360 | 0.2863 | 0.8931 |
| 0.2019 | 2.0 | 14720 | 0.2978 | 0.8974 |
| 0.1003 | 3.0 | 22080 | 0.3934 | 0.8948 |
| 0.0441 | 4.0 | 29440 | 0.6255 | 0.8903 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for mrm8488/ModernBERT-base-ft-all-nli
Base model
answerdotai/ModernBERT-base