Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kitsunea/m3")
model = AutoModelForSequenceClassification.from_pretrained("kitsunea/m3", device_map="auto")Quick Links
m3
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: 0.3164
- Accuracy: 0.9587
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.162 | 1.0 | 318 | 0.9002 | 0.9026 |
| 0.5751 | 2.0 | 636 | 0.5701 | 0.9429 |
| 0.3266 | 3.0 | 954 | 0.4162 | 0.9555 |
| 0.222 | 4.0 | 1272 | 0.3541 | 0.9558 |
| 0.1758 | 5.0 | 1590 | 0.3308 | 0.9597 |
| 0.154 | 6.0 | 1908 | 0.3206 | 0.9597 |
| 0.1421 | 7.0 | 2226 | 0.3187 | 0.9581 |
| 0.1358 | 8.0 | 2544 | 0.3164 | 0.9587 |
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 kitsunea/m3
Base model
answerdotai/ModernBERT-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kitsunea/m3")