Text Classification
Transformers
Safetensors
English
modernbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Pirr/ModernBERT-large-moderator")
model = AutoModelForSequenceClassification.from_pretrained("Pirr/ModernBERT-large-moderator")Quick Links
ModernBERT-large
This model is a fine-tuned version of answerdotai/ModernBERT-large.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1346 | 1.0 | 1949 | 0.0866 | 0.8382 | 0.8965 | 0.8981 | 0.8949 |
| 0.0205 | 2.0 | 3898 | 0.0866 | 0.8460 | 0.8992 | 0.9219 | 0.8777 |
| 0.001 | 3.0 | 5847 | 0.1018 | 0.8487 | 0.9 | 0.9256 | 0.8758 |
| 0.0006 | 4.0 | 7796 | 0.1432 | 0.8446 | 0.8974 | 0.9191 | 0.8767 |
| 0.0031 | 5.0 | 9745 | 0.1761 | 0.8438 | 0.8984 | 0.9107 | 0.8864 |
| 0.0 | 6.0 | 11694 | 0.2007 | 0.8401 | 0.8973 | 0.9009 | 0.8937 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.21.0
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Model tree for Pirr/ModernBERT-large-moderator
Base model
answerdotai/ModernBERT-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pirr/ModernBERT-large-moderator")