Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use kitsunea/m2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kitsunea/m2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kitsunea/m2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kitsunea/m2") model = AutoModelForSequenceClassification.from_pretrained("kitsunea/m2", 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: m2 | |
| 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. --> | |
| # m2 | |
| This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3031 | |
| - Accuracy: 0.9610 | |
| ## 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: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 4.8454 | 1.0 | 318 | 0.9877 | 0.8987 | | |
| | 0.4508 | 2.0 | 636 | 0.5239 | 0.9426 | | |
| | 0.1701 | 3.0 | 954 | 0.4176 | 0.9529 | | |
| | 0.1037 | 4.0 | 1272 | 0.3749 | 0.9545 | | |
| | 0.0779 | 5.0 | 1590 | 0.3482 | 0.9561 | | |
| | 0.0635 | 6.0 | 1908 | 0.3291 | 0.9594 | | |
| | 0.0546 | 7.0 | 2226 | 0.3163 | 0.9610 | | |
| | 0.048 | 8.0 | 2544 | 0.3095 | 0.9619 | | |
| | 0.0444 | 9.0 | 2862 | 0.3045 | 0.9616 | | |
| | 0.0423 | 10.0 | 3180 | 0.3031 | 0.9610 | | |
| ### Framework versions | |
| - Transformers 4.56.1 | |
| - Pytorch 2.8.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.0 | |